A few years ago, I taught a brand new (to me) two-course sequence: Real Analysis 1 & 2. These are upper-level courses, required for some of our majors and taken by electives as others. Each class is capped at 24 students and is often much smaller.

These courses require students to write and understand proofs, which are written mathematical arguments. If you’re not familiar with them, you might be surprised at what an upper-level math proof looks like: It’s mostly words, sentences, and paragraphs with some carefully selected symbols. A proof can range from one paragraph through multiple pages. The essence of a proof is to communicate precise technical ideas in carefully selected language, and small variations in wording can lead to big differences in meaning. Writing proofs is a skill that students – and all mathematicians – build throughout their lives, beginning as undergraduates.

For these reasons and more, students view Real Analysis 1 & 2 as some of the hardest courses we offer, and a test of their mathematical skills at the end of their major.

While designing the classes, I wanted a way to assess students’ proof-writing abilities, as a way to let them show deep understanding of class topics. One way I did this was through take-home homework, which gives them lots of time to think, collaborate, and write. But I also wanted something proctored, to give me a bit more confidence about students’ individual knowledge.

That’s the pain point that I encountered: Writing mathematical proofs in a timed, high-stress exam environment is really hard to do. Proofs that students encounter in Real Analysis are often long, detailed, and abstract. Coming up with the central logic of such a proof, much less working out the details and ensuring its correctness, can take days. It’s a process that benefits from lots of time sitting in a comfy chair, staring at a wall, and just thinking. Timed proof-writing is also inauthentic: Mathematicians just plain don’t do math in that way. Math takes time and requires a lot of thought, false starts, and wrong turns, on the way to eventual success. How could I assess that in a shorter, proctored setting?

To solve this problem, I turned to an experience that I’d had many years ago as an undergraduate: “mastery exams”.1 In today’s post, I’ll walk you through how I used them, how well they worked, and things you might want to consider when using them yourself.

How mastery exams work

Mastery exams are not typical in-class exams. These are brief, focused, timed exams in which students are asked to recreate proofs that they’ve already seen. Generally these proofs are too complex to memorize, and are selected so that a student who understands their fundamental ideas can reconstruct the details on the fly. This gives students a chance to show in-depth knowledge in a proctored setting, with the benefits of being able to prepare, struggle, and learn outside of that proctored setting. It also removes the surprise and difficulty of having to invent a proof from scratch.2

I assign three mastery exams per semester, each covering a few main topics from the last month or so. Each exam contains a short list of proofs or definitions that students will be expected to be able to recreate in a clear, complete, and correct form. Importantly, these are all results that we’ve already seen and studied before, either during class or on a homework assignment – so students always have a chance to practice the exam content before taking it.

I post the full requirements for each mastery exam shortly after we’ve finished studying those topics in class. That’s right – I tell students exactly what will be on each exam (here’s an example of the first mastery exam from last year). That linked file is exactly what is posted on our LMS. In class, I announce when a new mastery exam is open (typically during weeks 6, 9, and 12 of our 15-week semester).

Mastery exams happen outside of class, during an office hour or a time of students’ choosing. After the exam has been posted, students can choose when they attempt each exam. This gives them time to study and practice the proofs.

To take the exam, a student comes to any office hour — or sets up another time individually — and asks for the exam. I give the student a paper copy of the exam — the exact thing they saw on our LMS — and they sit down at a table and write out complete proofs of those results, from scratch, based only on their memory and understanding. I allow up to 30 minutes for this, although typically students use less. Students can’t use any resources when taking the exam, but I encourage them to prepare for the exams by working through proofs with classmates and using any regular class materials they’d like.

I hold office hours in a common study space, and I often have multiple students taking mastery exams at various tables around the room. When they are ready, they bring their written work to me and I read it for completeness, correctness, and clarity. This is usually a quick process, since I’ve also reviewed the proofs ahead of time and know what to expect.

If all of the proofs are complete, correct, and clear, then the student earns Successful on the entire exam, otherwise Not Yet. I grade the exam “live” in front of the student, talking through my thought process as I do. If I run into a minor issue that is worth fixing, but not so serious that it requires a new attempt, I’ll ask the student to explain or correct their work on the fly. Occasionally this turns into the student reworking bits of the proof on a whiteboard.

In the case of Not Yet, a student can reattempt the same exam up to three times total. Attempts must be done on different days so that they have time to review and improve their understanding. The questions are the same on each reattempt.3 All that I record is a student’s ultimate grade: Successful or Not Yet, regardless of the number of attempts.

There are three different mastery exams during the semester, and they must be taken in order: students must pass Mastery Exam #1 before they can attempt #2, and must pass that before they can take #3. I require this because the topics in Real Analysis build heavily on each other, so each exam requires a solid foundation from the earlier exam topics. If you’re thinking about using a similar setup in your class, this strict sequencing might or might not be necessary.

Mastery exams are a key element of the final grade. Along with other requirements,4 in order to earn an A, a student must earn Successful on all three mastery exams. For a B, two exams, and for a C, just one exam. Not earning Successful on any exams means earning a D or lower.

Results

I’ve loved using mastery exams. They satisfy their main purpose well: to show me a student’s understanding of some key proof ideas in a proctored setting. This is supported by having a carefully curated collection of proofs that are laser-focused on the big ideas of each class. When a student completes a mastery exam, I’m quickly able to tell if they truly understood the key ideas in each proof. Better yet, if they have some confusion, the exams reveal exactly where that confusion is, and I can give targeted feedback to help them improve.

I worried that adding a timed test element to these classes might cause too much anxiety for students. I didn’t need to worry. In anonymous surveys (and also through informal comments during office hours), students said that they liked the challenge that the exams posed. Having multiple attempts without penalty was a “stress-reliever”, and students liked the flexibility in choosing when to take each exam.

Not all students were thrilled by the exams, but their main complaints were actually about logistics: For students who had a hard time making it to my office hours regularly, there was an additional step of scheduling a special time to take the exam. I was very flexible about this, but some students found that extra step annoying.5

As is usually true in alternative grading, this structure gives students the power to set a grade goal. If a student passed exams 1 and 2 and were happy to earn a B, they could decide not to worry about the last exam. Several students did that quite intentionally and told me so.

On the flip side, even though students are limited to three attempts on each exam, nobody has ever run out of attempts in the three semesters I’ve taught these courses. Some have certainly needed all three attempts, especially on the first exam (it takes some time for students to get used to the format and expectations). The median number of attempts required on the very first mastery exam is 2 (out of 3 possible). For the second and third mastery exam in each semester, students needed a median of 1.5 attempts each.

Q&A

You likely have some questions about this approach to exams, so I’ve put together some of the most common concerns I hear:

Won’t students just memorize the proofs? What are you really testing them on, then? I choose proofs that are too big and too complex to memorize in full. I deliberately choose proofs that require certain key methods or ideas at their heart, and are structured in a way that a student who understands those key methods can reconstruct the rest of the proof. The result is that students must work to understand the overall structure and logic of the proof, but then recreate the details on the fly. If they don’t come into the exam truly understanding the underlying ideas, it immediately shows in their work. Students notice this as well – many have told me that they had to truly understand the big ideas of each proof before they could pass the exam.

Why not just give everyone the exam all at once in class? I could, but my more flexible approach lets students take the exams when they feel confident and have had time to study. That’s an important part of reducing the stress and anxiety of a timed exam. Plus, this approach saves class time and gets students to come to office hours! I did notice that some students delayed taking the first exam until I started bugging them about it. When I encountered mastery exams as a college student, my professor had a way of dealing with this: If a student hadn’t passed Mastery Exam #1 by the Friday before spring break, they were required to come to class that day to attempt it again (everyone else could leave for break early). This was very effective at motivating students to prepare.

Aren’t these still high-stakes, high-stress exams? I was worried about this, and students certainly take the exams seriously, but they don’t report a lot of stress or anxiety. Built-in reassessments help, as does the ability to choose when they take the exam. Being able to collaborate and even have practice sessions with classmates also improves students’ confidence. The first attempt on the first exam is often a bit rough, as it is any time students encounter a new type of assessment – but it gets considerably better after that as students learn and adjust, with no grade penalty.

Do students need to produce polished proofs? Isn’t that a lot to ask in a timed setting? Polish isn’t required, but students do need to communicate well enough to unambiguously show correct understanding of mathematical and logical ideas. Part of the test is understanding how and when to show that understanding most clearly. You can see my advice about writing at the top of the first exam. I do find that students are sometimes surprised at how “silly writing mistakes” can actually lead to really serious logical errors.6 If a student earns Not Yet on their first attempt for this reason, I immediately pivot to helping them see the underlying logical issues.

What other classes might mastery exams work in? I’ve only used this approach in upper-level proof-based math classes. Proofs require multiple steps of logic and an understanding of how all of those steps fit together. Even a student who has seen a complete proof still needs to do some serious logical work to fully understand the proof’s logic, which is what the mastery exams best show. Other situations that involve longer-form logical arguments (perhaps philosophy), or the ability to reproduce a detailed chain of logic that’s based on important core principles (like diagnosing and designing treatments for patients) might benefit from this same approach. Likewise, any situation where a student would benefit from taking time to more deeply process an argument that they’ve already seen would be appropriate for a mastery exam.

As I mentioned above, the goal isn’t to test a student’s creativity, since that’s not something that fits well in a timed environment – so I would avoid mastery exams when creativity or originality is the standard that you care most about. In addition, I wouldn’t use mastery exams with content that is more procedural or rote, since that could lend itself to memorization.

In the end

Mastery exams fit a very specific niche, but if you’re in a similar situation, you might want to give them a try. If you do, please let me know how they work for you!

Katie Baker is an Associate Professor of Writing at Rosemont College and Discipline Coordinator for English, Writing and Theater. She also coordinates the Writing Studies program in the School of Graduate and Professional Studies, an online accelerated bachelor’s program. She is an avid writer, reader, and runner, though not all at the same time.

On March 31, 2025, my colleagues and I were called to an emergency meeting on campus, where we learned that our institution, Rosemont College, had entered a merger agreement with Villanova University. Rosemont College is located directly behind Villanova University, with both institutions just a few miles outside of Philadelphia. I’d like to say it was an “anticipated” surprise, but the truth is that Rosemont College, like many small liberal arts colleges, had been struggling for years. With each new entering class, we held our breath, thinking we would be OK, and we were— until we could no longer sustain the lower numbers of admitted and continuing students and the upkeep of a beautiful but costly college infrastructure. While there are many details of the merger agreement (you can read more here) ), a few affected students and faculty right away.

First, the merger would be finalized in June 2028, meaning that we would work to make sure all of our undergraduate, professional studies and graduate students could finish by that date as best as possible. Second, we admitted our final incoming freshman class this past fall. This was bittersweet for me, as this is my sixteenth year at the college. This was also the final year of the college’s involvement in Division III sports. So as one might expect, students transferred after the announcement, especially if they hoped to continue to participate in athletics. And obviously, there are always students transferring for other reasons: a new major or program, relocation, or personal reasons.

Our motto has always been “The Power of Small,” one that I wholeheartedly embrace. Even our “larger” core classes were usually no more than twenty students, twenty-five at most. The intimate class sizes have been one of my most favorite parts of teaching at a small liberal arts college. Except now, just about a year out from the announcement, our classes are becoming much smaller than usual as our overall student body decreases, leading me to think more carefully and critically about grading in the classroom with the students who are finishing out their degrees.

I oversee our English and Writing department. I grade a lot. Between teaching academic and professional writing courses, creative writing courses and American literature classes, I read a great deal of student work, whether it be papers, digital projects or other reflective work. Reading student work is honestly one of my favorite parts of the job but also naturally, the most time consuming. Long ago, I tried various styles and strategies of grading and assessment. Now, I have the chance to really observe these and put many into practice as I am teaching fewer and fewer students until the merger takes place.

Embracing Process Based Grading

As a writing professor, I have always favored process based grading but with a 4/4 course load, I could not fully put into practice all parts of the model. Process grading emphasizes shifting the focus of grading to each step of the paper or project, completing all requirements and focusing more on the actual process of learning and reflection of each step. Previously, I had to limit process based grading to an assignment or two, and typically, the final project. I was able to fully implement process based grading this past fall in my courses, as I could now really devote the time to grading more intently, making changes to support my students’ learning.

My smallest class right now, an Honors section of Advanced Writing and Oral Communication has four students and the largest class (Introduction to Short Fiction) has ten students. Both are in the core curriculum. This section of Advanced Writing and Oral Communication is the smallest class I have ever taught. The small size has allowed me to meet with each student in the class individually at least once a week. The students asked if this could be incorporated into their class engagement grade, realizing an opportunity to slow down their writing practice and shift the focus to refining their writing skills. I was thrilled at this request, and they helped me construct a rubric for participation. I wanted to give them more autonomy in their work and to increase their confidence in their writing. I was hoping this might reduce the stress around producing a final product of work. The conferences each week helped each student voice concerns they had with their writing and research, which then could be easily integrated into the class lessons.

I am not rushed to only be able to hold conferences with students once, maybe twice a semester, or to beg for students to come to office hours. Not only has the quality of their writing has increased tremendously from the individual meetings, but they are also asking wonderful questions on the course material and voluntarily adding readings and ideas for digital projects to the class, many of which are coming from our conference sessions. I am also grateful for the time where I can learn more about the students in and outside the classroom, a much needed positive of the whole situation. The students are to come prepared to conference for ten to twelve minutes, having read and responded to assignment feedback from myself. They are expected to have questions written down and a plan for revision for whatever next steps might follow. The rubric assesses these areas as either “incomplete” or “complete.” Even in a small group, some students do not always feel comfortable voicing concerns or questions about their writing. Some assignments are harder for others. The individual conferences weekly offer a space to engage in discussion and to make a plan for their writing.

Finding flexibility

This leads to my next area of change in regard to grading: flexibility in assignments. For the Honors Advanced Writing class, the flexibility that has come with smaller class sizes has led to more digital projects by request of the students. I love digital projects, especially in my writing and literature courses. But coordinating and working with twenty students in just one course (when you hold a 4-4 load) on a given project can be difficult. I value student feedback immensely, but I would not have had the time to alter assignments and projects mid-semester with a larger group of students. By request, my four students in the Advanced Writing class asked to create a digital anthology of readings on pop culture inspired by a podcast we listened to in class. It was easy to make sure all four students listened and engaged in discussion after, which is where we discovered this was a project they wanted to work on collaboratively. I had no problems with substituting it in place of another.

Now, my students are writing consistently on their own websites, designing and presenting on digital pop culture artifacts, and creating their own class anthology, all from their own feedback on what they wanted to produce for the course and their expected outcomes (aligning with the course goals and objectives). They had brainstormed and inspired the assignment so we collaborated on the rubric, to assess the organization, content, source credibility and text analysis. Their own digital portfolios of work are growing tremendously. I was impressed by the range of their research and the wide amount of topics included, which far surpassed the required amount. Now, students often embark on a “choose your own adventure” style of prompts, which I am able to accommodate widely in a small class. After the students created the anthology, they had the option to create a lesson plan based on the anthology to “teach” to their classmates or record a podcast episode live with peers. This is a small silver lining that I am holding onto deeply.

Collaboration is Cool

Peer to peer feedback is used in all of my writing courses. Students are required to comment on peer work and graded on the quality of feedback, the usefulness of their remarks, and on being an attentive listener. This takes place in peer workshops in both creative and academic writing courses. Like many other faculty, I find that having a larger group of students participate in a peer workshop with meaningful comments can be difficult. Even with direct questions to answer or points to cover, minds wander, conversation gets off track, and comments are not helpful to a revision process.

Now, graded peer to peer feedback is more efficient, directed and widely participated in. No one leaves “filler” comments or simply exclaims they “like” someone’s work. We often participate in live feedback on the spot when drafting work in class. Sometimes this is going through everyone’s paper on the Smartboard. Sometimes it is going through each paper or project on Google Docs, which has helped students comment and critique with more directed focus. Each student wants to collaborate and help better a draft, not just their own. No one is trying to read seven different papers. There is less pressure on trying to comment on so many others’ work in the class.

I have always tried to stress that each assignment is not about the grade, but rather, the focus on the process to make each student a better writer. The students genuinely look for the good in each other’s work and give responsive comments. Our classes are widely a larger conversation, but that’s exactly what is needed.

Making and re-making the classroom space

The merger has given me time to think about what some of the final assignments will look like as I teach many of these courses for the last time. I’m not sure I would have had the work capacity to revisit grading strategies or redesign assignments but knowing that this may be the last time I teach these courses was motivation to finally make changes that I had wanted to implement for the past few years. I would have made small tweaks or modifications but I also have used this time to experiment with adding more process grading and in turn, leaning into student interests more.

I sometimes can not wrap my mind around the fact that I will be teaching many of these courses for the last time. Reality will hit but I have tried to use the space these past semesters to incorporate assignments and assessments that highlight the legacy of our college. My Short Fiction Writing class is writing their own fiction and composing their own portfolio of work. That is easily assessed and welcomed. This semester, I was able to tie the production of our college’s undergraduate literary magazine with assignments in the course. My students in the Short Fiction Writing class formed their own editorial board and made a detailed timeline to ensure the publication of the literary magazine. They each assumed roles and responsibilities. Part of their final project is publishing the second to last issue of a literary magazine that has been around since 1952. The students each have equity in the production of the magazine.The have roles and responsibilities that are documented, noted on their resumes. Each literary magazine is documented and digitized in our archives. Their names will be imprinted in this issue. I am excited to grade and assess their process of learning, knowing they also have the ability to adjust and pivot in this space as needed. If the students need more time to work on a part of the project, we take it. If a design is not working in our layout, we brainstorm on the spot. We reassign roles if needed.

After almost two semesters now following the merger announcement, grading in the classroom with fewer students naturally is less time consuming but also feels more engaging, purposeful, responsive and impactful. I am focusing on the “I get to do these” moments, memories, projects and prospects. I am embracing the unusual change in size in my classrooms. I am seeing the merger as a positive way to enhance my teaching at Rosemont with my students as they seek degree completion.

My students and I are more reflective. We journal now more than ever before as a means of reflection. I am thoroughly grateful for the time spent with my students and I am conscious of my time spent reading their work and preserving the legacy of our college. No matter what the numbers, I am so happy to be in the classroom working collaboratively with my students.

Today we’re bringing you an update of one of our oldest posts, originally published on September 13, 2021. Back then, we brought these important thoughts on “rigor” to all 359 of our readers. This topic is just as important today as it was back then, and with more years of experience (and about 20 times as many readers), we thought it was time to give this post a refresh. We hope you enjoy it.

If you’re involved in academia in any way, you’ve heard the term rigor. It’s a constant companion in any discussion of grades, assessments, teaching methods, curriculum and course design, and of course, AI.

Let’s flip this intro, though: Before you read any farther, what does the word “rigor” mean to you?

Seriously: Take a minute. Actually write down what “rigor” means to you in a classroom setting, what it implies, maybe some examples of what you would consider rigorous or not. Then keep reading once you’re done.

What does rigor mean?

So, what did you write down?

After some extensive asking around, here are the words we most often heard used to describe rigorous courses: “difficult”, “challenging”, “strict”, “high standards”, “C average”, “bell curve”, “gatekeeping”.

If you’re not a fan of anecdata, here are some better-cited examples:

• In Specifications Grading, a book that inspired many to rethink assessments, Linda Nilson uses rigor to mean “high academic standards”.

• A collection of sentiments that many will recognize, wrapped up nicely by EdGlossary: “instruction, schoolwork, learning experiences, and educational expectations that are academically, intellectually, and personally challenging.”

• In “Academic Rigor: A Comprehensive Definition” (written for Quality Matters, a nonprofit focused on measuring and guaranteeing course quality), Andria Foote Schwegler defines academic rigor as: “intentionally crafted and sequenced learning activities and interactions that are supported by research and provide students the opportunity to create and demonstrate their own understanding or interpretation of information and support it with evidence”.

• Dictionaries cover a lot of ground with rigor. Ignoring non-academic meanings1, some relevant definitions include: “the quality of being extremely thorough, exhaustive, or accurate”; “strict precision”; “the quality of being unyielding or inflexible”; “scrupulous or inflexible accuracy or adherence”.

• Robert even wrote about this way back in 2008: rigor is “thoroughness, carefulness, and right understanding of the material being learned”, and a rigorous course “examines details, insists on diligent and scrupulous study and performance, and doesn’t settle for a mild or informal contact with the key ideas”.

But by far the most common definition for rigor is: none. That is, most articles, books, and random internet conversations about rigor leave the term completely undefined. Rigor seems to be placed beyond definition, left up to the audience to interpret and recognize. You don’t need a definition for rigor — you know it when you see it, and it’s either there or it isn’t.

Or that’s what we’re told, at least. Authors and speakers often refer to the vaguely sinister specter of “lack of rigor” in modern education, a dog-whistle that covers all of the ground from kids-these-days have it too easy and I suffered back in my day and so you must too, to your course doesn’t cover my favorite pet topic.

This post was originally inspired by an article from Inside Higher Ed, “Upholding Rigor at Pandemic U”, that made the rounds in 2021. If the word “rigor” weren’t in the title, you might not realize that this is what the article is supposedly about; the word only appears twice outside the title, both times in the context of “upholding standards of rigor” and without clearly indicating what those standards are — or what rigor itself is. We are apparently supposed to simply know what rigor is, intuitively.2

There are so many more examples. Last year, we saw Harvard’s “Update on Grading” and the follow-up proposal to cap the number of A’s in each course at 20%. The update itself refers to “rigor” a few times without definition; news reports leaned in to undefined “rigor” even more heavily (see Forbes and the Harvard Crimson’s editorial, which interestingly also mentions “mastery-based grading”). As in the Harvard kerfuffle, “rigor” often appears paired with the bogeyman of “grade inflation”, with a large number of high grades being seen as evidence of lack of rigor.3 Rigor inevitably appears in any discussion of generative AI and how it affects grades and learning.

Here is, ultimately, the problem: Rigor is a wildly overloaded word. It means something different to each person, and even instructors with many shared educational values are likely to have different definitions. When two or more people are gathered to talk about rigor, there too shall be ambiguity. These discussions are inevitably surrounded by unexamined assumptions, biases, and cultural baggage.

What can we say?

So, what can we do about this overloaded word? Perhaps the simplest solution is best: Don’t use the word “rigor” at all. Too much is tied up in the word; it’s a red herring and distraction and a vehicle for our biases.

Instead, let’s pull apart some of the knotted threads that form “rigor” and see what we can say about them. In particular, what concrete things can we say about how alternative grading systems approach the issues that seem to be indirectly addressed by “rigor”?

In the rest of this article, we’ll take a look at what we can say about the academic standards in grading systems based on the Four Pillars.

Clearly defined standards with marks that indicate progress

When student work is evaluated against clearly defined standards, there’s something that doesn’t happen: Comparison to other students. Grading based on clearly defined standards is also called criterion-referenced assessment, which gives a clearer and more consistent meaning to grades. This meaning is linked to clear criteria and doesn’t vary depending on how other students perform (aka norm-referenced assessment).

In other words, these two pillars lead to grades that are more meaningful and directly reflect student learning.

Despite the words “rigor” and “standards” often showing up near each other (like in that phrase, “standards of rigor”), actual standards — criteria for what constitute acceptable work on a task, clearly spelled out and accessible to the student — aren’t often a part of traditional grading. Instead, in that context “standards” is often a proxy for “grade frequencies”. Did your class have too many A grades? Not rigorous enough. Was the distribution bell shaped with the mean around C? That’s more like it. To make this happen, instructors judge students against each other by “curving” grades, limiting the number of A’s, or using other wildly inequitable procedures that muddle the meaning of those grades.

Expecting student grades to fit a bell curve is simply not based in reality,4 and enforcing this through curving is the opposite of holding students to a high standard — it’s holding them to an arbitrary standard over which they have no control.

A reasonable objection here is that we haven’t actually said what or how high those standards actually are. Clearly, if we hold students to fluffy and light standards, like a meringue but with less academic meaning, then grading based on those standards isn’t going to fit anybody’s idea of rigor. And indeed, we want to make sure our courses challenge students intellectually and that a high grade is based on authentic evidence of real learning. Choosing standards, and determining what should be involved in meeting a standard, is a place where instructors can have productive discussion.

Ironically, when grading with standards, instructors often overcorrect and expect perfection from students. This inevitably involves aspects of a student’s work that are not central to the idea being assessed, possibly including things like arithmetic, copy errors, or writing style.

If a standard is clear, it must specify what matters and, implicitly or explicitly, what doesn’t matter. And in most cases, what matters is not “everything”. When a student is writing a solution to a math problem, misspelled words or poor punctuation might matter (if communication quality is part of the standard) — but probably not. As long as the solution is understandable, we can judge whether it meets the standard separately from whether it is well communicated. And we probably should.

But wait! If we’re leaving out some parts of a student’s work — such as not “removing points” for spelling or arithmetic errors — doesn’t that mean we’re lowering standards, decreasing rigor? Only if those things are part of the standards being assessed. But then, they should also be part of what is taught in the class. Things that matter should be spelled out clearly in the standards or specifications.

In the end, the clearly defined standards must necessarily ignore some aspects of student work. Standards should be clear about which items matter, and which don’t, and these will depend on the course in question. This can be a difficult, but essential, part of creating standards for your own classes. What matters in a lower-level course — for example, attention to numerical detail — may not be as important in a later course with a different focus.

Helpful feedback and reattempts without penalty

Alternative grading systems also critically feature helpful feedback and the ability to revise, resubmit, or reattempt without penalty. Those two pillars look like they reflect lower standards. If we give students lots of feedback, and then give them chances to reattempt their work without penalty, what’s to prevent every student from earning full credit on everything?

Nothing! And, that’s a good thing!5 These two pillars are the core of the feedback loop that makes learning work. In the end, helpful feedback and unpenalized reattempts lead to greater learning, as opposed to one-and-done assessments that incentivize students to focus only on the grade and flush away content from their brains once they’ve been tested.

This is best illustrated through the classic example of Alice and Bob, which shows the dangers of one-and-done assessments. Students who have opportunities to continue learning through a feedback loop are pushed and challenged to grow in their understanding. Those in more traditional systems are encouraged to accept partial understanding (and partial credit) in place of real, deep learning. Surely, pushing for greater understanding is more rigorous, isn’t it?

The goal of these two pillars is for grades to represent a student’s ultimate level of understanding. This reduces confounding factors, like whether a student was feeling ill on the day of a test, whether they were experiencing a personal crisis, whether the room contained distractions, and so on. This approach acknowledges that different people learn at different paces and can grow in their understanding. If we really believe this — if we really care about treating students like human beings who can succeed in our classes — then “high academic standards” must have room for feedback loops.

There’s an interesting consequence here: Many people who use alternative grading notice that the number of A’s and B’s in their class increases. As we mentioned above, “grade inflation” is often cited as a consequence of slipping academic standards and decreasing rigor. But here, it’s actually the opposite: By holding students to high standards and allowing reattempts without penalty, we remove aspects of grades that aren’t related to learning. Having only one chance to demonstrate understanding penalizes students for not performing on the instructor’s schedule. Partial credit doesn’t make up for this: It still fundamentally represents a one-and-done approach.

When we insist on concrete evidence of learning, every ounce of those high grades can be traced back to an explicit piece of work that meets high standards.6 When grading with feedback and reattempts, students are no longer permanently penalized when they fail an early test. Instead, if they work to improve their understanding, their grade fully reflects that they’ve achieved a high bar.

Instead of rigor…

We’re not saying that college courses shouldn’t be rigorous. We are saying that the word rigor itself has no inherent meaning and is therefore powerless to describe the kind of learning environment we want. In fact, we — David and Robert, and likely most others in on “Team Alternative Grading” — probably want the same kind of learning environment that many on “Team Traditional Grading” want: an environment where students are pushed and challenged to grow, engage deeply with difficult ideas, and show us in clear terms that they’ve met the challenge. In order to reach this goal, we have to start by using real words.

Rigor is not one of those. It is essentially a buzzword that just happened to appear in academic discourse decades ahead of all the other ones we currently deal with. So let’s stop using it.

If you’d like to see what word we do recommend, here’s Robert’s followup post from 2021: Moving on from “rigor”.

Today’s guest post comes to you from Erin Coffman, an assistant clinical professor of business analytics at the Kelley School of Business at Indiana University Indianapolis. At Kelley, she coordinates a required undergraduate analytics class (taken by around 500 students per year) and teaches a foundational analytics class in the MBA program. Erin holds a PhD and MA in economics from Georgia State University and an BA in mathematics education and economics from Anderson University (Indiana). Before joining IU in 2023, Erin spent a decade working as a data scientist at Airbnb, where she also developed and led a company-wide data and analytics training program called Data University. She lives in Indianapolis with her miniature schnauzer, Pretzel, and enjoys traveling, biking around town, golfing, and playing pub trivia. You can reach her at erincoff@iu.edu.

About IU-Indianapolis

IU Indianapolis is an R1 public university and Indiana University’s second largest campus, with around 20,000 undergraduate students and nearly 10,000 graduate students. Most students are traditional (i.e., they enroll directly after graduating high school), and many commute to the campus from the city and surrounding suburbs. Additionally, many are first generation and work significant hours (25+ weekly) while attending school full time. The Kelley School of Business is a top-ranked business school and has locations in both Bloomington and Indianapolis. In Indianapolis, Kelley enrolls about 1200 undergraduate students, and many lower-level courses (including the one discussed in this post) are open to pre-business students.

My First Attempt (The Problem)

I was hired at IU in 2023 to coordinate and teach a newly-required undergraduate course, K303: Technology and Business Analysis, which is the second in a series of technology- and analytics-focused courses in the business school. As the coordinator working with all K303 faculty, it is my job to create the schedule, choose course topics, make assessments, populate each section’s Canvas site, collect feedback across sections from students and faculty, and iterate on all of these things each semester.

As for the course itself, K303 is based on the steps of the analytics cycle: data collection, transformation, exploration, modeling, and visualization. We use Excel and Tableau as our primary tools. The course is required for students before enrolling in their Integrated Core semester, which is a key curricular piece of a Kelley education. Most students take K303 in their sophomore year; about half are already enrolled in the business school, while the rest are hoping to be accepted. The class size averages between 50-65 students per section, and we hold between 9-10 sections per year.

In my first year, I set up K303 with a typical grading structure: 1000 points available, 3 exams, a relatively strict attendance policy, etc. The results weren’t terrible, but student feedback consistently mentioned feeling overwhelmed by the pace and high anxiety regarding exams, and I saw a clear mismatch between grades and student understanding. The visualization below shows data from the first year: nearly a quarter of the students passed the class, but averaged a below-passing grade on exams.

While I am not fully sure why this result occurred, my theory is that students invested in making sure their homework assignments were perfect or near-perfect (many auto-graded assignments allowed unlimited retries, so it was relatively easy to “game” the results) to act as an insurance policy against low test scores. Because homework and attendance made up 60% of the grade, it was possible for students to do relatively poorly on the exams and still pass the class. The result shown above seemed like a big problem: I care about what my students actually know a lot more than the course’s grade distribution.

Standards-Based Grading (A Solution?)

Also in my first year at IU, I was assigned a summer section of K303. I was worried about having two very different audiences: one being students who had failed and had to retake the course, and the other being high-achiever types looking to get ahead. So, I visited our very helpful Center for Teaching and Learning (CTL) to get some ideas. They suggested specifications and/or standards-based grading as a way to serve both populations, and I was immediately obsessed. I restructured the course with the help of many resources, but mostly by using posts from this very Grading for Growth blog.

A few of the major changes I implemented:

• No more points!

• The course was pared down to 13 skills assessed by “Checkpoints”, one for each skill. These Checkpoints replaced traditional exams, and multiple variations of each Checkpoint were created and offered across a set of “Checkpoint Days”. Seven skills were identified as “Core” - all students had to pass these in order to earn a passing grade for the course.

• Larger homework assignments (called Mini-Projects) and an optional group project were developed and these assignments offered retries without penalty.

• Attendance, discussion posts, pre- and in-class practices (lightweight assignments) were bundled into “Engagement Credits”.

• A grade table (example shown below) was used to communicate to students what had to be successfully completed to earn each grade. The numbers in the parentheses indicate the number offered for each category.

Testing the Results

The summer class was a great testing ground, as I had only 11 students. However, there were a lot of kinks to work out, specifically as it related to Canvas (IU’s learning management system). I knew I was going to continue with the new structure, but, given the numerous logistical issues to work out, I was hesitant to require the other faculty teaching K303 in the fall to implement the same.

The data scientist and economist in me saw this as an opportunity to employ an A/B test to identify the effects (if any) of the new grading system. At Airbnb, we tested everything, from website updates to how customer service agents were trained. A true A/B test is used in tech to isolate a change and test the effects, usually on a website (e.g., “If we change the checkout button to green from red, will more people click it?”). Web traffic is split and a portion of users see the treatment (green button) while the rest see the control (red button). Because everything else remained the same, the effect of changing the button color can be measured. (And of course, this is effectively just a tech rebranding of the scientific method!)

As we all know, it’s impossible to design a perfectly controlled experiment in education. However, some data is almost always better than no data. In order to best isolate the treatment (alternative grading) from the control (traditional grading), I did the following to set up the experiment:

• I assigned two class sections (taught by me) as the Alternative Grading sections. (I initially wanted to teach one of each type to further isolate the effects, but the school has a policy against the same professor teaching sections of the same course differently.1)

• I assigned two class sections (taught by two other faculty) as the Traditional Grading sections.

• I updated the exams for the Traditional sections to map to Checkpoints in the Alternative sections. For example, the “Excel Functions” part of the traditional exam directly mirrored the “Excel Functions” Core Skill Checkpoint offered in the alternatively graded sections. (This allowed me to directly compare student performance on assessments for each skill across groups.)

• I updated schedules for all sections to follow the same topics and cadence, including the same practices and homework assignments.

• I designed a student sentiment survey to collect feedback at different points in the semester.

Did It Work?

The short answer is yes: alternative grading yielded higher student achievement, lower student anxiety, a higher pass rate, and a higher GPA average.

The visualization below shows the passing rates by first attempt and after all attempts. Note that in the traditionally graded sections, students only got one attempt (on a traditional exam) to pass each skill, which is why the “first” and “all attempts” pass rates are the same.2

A few key takeaways:

• In every single Core Skill (those required to show proficiency in order to pass the class), alternatively graded students achieved higher (and near 100%) pass rates by the end of the semester.

• In almost all skills, alternatively-graded students also achieved higher passing rates upon first attempt. Why is this? One theory is that because each skill was offered multiple times in the alternatively graded sections, students took it for the first time when they felt prepared. Students in the traditionally graded class had one shot: the day the exam was offered.

Another facet I wanted to investigate was student sentiment. We surveyed the students three times throughout the semester: at the beginning, mid-semester point, and a few weeks before the end. I’m showing the mid-semester results here.3

The chart below shows how students viewed control of their grade in K303:

As you can see above, students in the alternatively-graded sections of K303 felt more in control of their grade destiny compared to a typical class, as well as compared to students in the traditional grading sections.

To gauge any effect the grading system may have had on student confidence, we asked them to report confidence levels of achieving their grade goal for the course. While most students felt somewhat or very confident about achieving their goal, the share of students feeling very confident was much higher in the alternatively-graded sections.

There was also a difference in final grades between the two groups. In the visualization below, the share of students earning an A or a B remained the same in each group, but many more students earned an A in the alternatively-graded sections. More importantly, the number of students failing the class (defined by the school as a grade of C- or lower) was significantly less than in the traditionally-graded sections.4

The Results Were Great! What’s the Catch?

If students were learning more, they felt more confident, and their grades were higher, we should definitely roll out alternative grading for all sections, right? It wasn’t quite that straightforward. The one outstanding issue was the time investment required for grading. With the alternative grading format I tested, there were more assignments to grade, both in number and frequency (although the time needed to grade each item was significantly shorter). I was hesitant to ask the faculty to take on that burden. After presenting the results above to our administration, they agreed to provide resources for a centralized grading program staffed by teaching assistants, and managed by me as the coordinator.

How does it work? Three undergraduates make up our shared grading team and a Google Sheets file is used for tracking that status of each assignment (see example below). A lead grader (who has been my TA since my first year) has trained and onboarded the others, and because she is graduating this year, she is training her replacement. Their resources include a repository of answer keys for all assignments, as well as links to video snippets (shareable with students) walking through almost every component of each major assignment and Checkpoint.

For lightweight practices where only effort (vs. accuracy) is required, the team assigns credit for completed work or marks the work incomplete if warranted (Canvas terminology only allows for “complete” and “incomplete” but these translate to “success” and “retry”). For Checkpoints and Mini-Projects, the graders do a first pass and mark all places where the work is incorrect (using the annotated comments feature in SpeedGrader), and add links to video snippets where necessary. If a student has a fully correct assignment, they get marked complete, and if they do not, the faculty then do a second pass and make the determination of whether the assignment met the bar for “success”. We as faculty also leave our own feedback and then update the due dates for those needing to retry. This means that no student is being asked to retry work without the faculty weighing in. Overall, the system has cut down on a lot of grading time, and allows us to focus on students who need a bit more direction.

Concluding Thoughts

Given the results above, all K303 faculty began teaching using the alternative grading method in the Spring of 2025. Students have generally rated the system favorably, and we continue to see a very high share of students achieving success across the learning objectives / skills. I have continued to make tweaks each semester, and am waiting for a day when Canvas allows adaptations for alternative grading schemes.

I’d like to thank Nolan Taylor and Michael Thomas (the faculty who taught the traditionally-graded sections for this study - and now teach K303 using alternative methods!) for their flexibility, willingness to collect data, and overall embracing of the adventure that is alternative grading.

Just before our spring break, I had a busy week of grading in my Calculus 2 class. I took notes each day on what I was doing and thinking around grades each day, and in today’s post I’ll share those notes with you. I hope you enjoy this on-the-ground insight into my grading decisions. These practical details are inextricably tied up with my own philosophy as well, so we’ll veer from the nitty-gritty details of midterm grades to my big-picture thoughts about building trust.

A little more about Calculus 2: This is a freshman- and sophomore-level class that serves a wide variety of majors, although because of when and where my section is offered, about 90% of my 30 students are Engineering majors.1 My class uses a mixture of standards-based grading and specifications. We have a quiz or exam every two weeks that assess students on a list of recent standards called “learning targets”. Students earn a mark on each learning target. During the alternate weeks, students complete advanced written assignments that I call “homework” and which are graded holistically using specifications, meaning that students earn one overall mark on the entire homework assignment. If you’d like to know a bit more, I wrote about a previous version of the class here: Updating an alternative grading system after a 6 year break.

Monday: Midterm grades

Today was the deadline to enter midterm grades. “How do I assign midterm grades?” is a big question when you’re using alternative grading, so I’m going to share my process.

I use a grade table to describe each letter grade. Here’s how final grades are assigned, where “S” refers to the Successful mark. Students must complete all of the items listed next to a letter grade in order to earn it:

Of course, if I used those requirements here in week 7 (of 14), everyone would have a D or F.2

Instead, I created a similar list of requirements based on how many learning targets and homeworks we’ve finished so far (which is almost exactly half: 6 targets, 3 homeworks). I deliberately added some wiggle room to this list, because students still have lots of time to retake and revise:

• A: targets at S = 5 or 6; homework at S = 2 or 3; engagement points = 90%+

• B: targets at S = 4 or 5; homework at S = 2; engagement points = 80%+

• C: targets at S = 3 or 4; homework at S = 1 or 2; engagement points = 70%+

• D: targets at S = 1 or 2; homework at S = 1; engagement points = 50%+

• F: targets at S = 1 or 0; homework at S = 0; engagement points: less than 50%

Then I determined which category each student’s current work best fit into, and used that to assign midterm grades. I also shared this list with students. Inevitably some students didn’t fit into one category, in which case I made a professional judgment. Criteria for that judgment included: Has the student been taking advantage of new attempts (for learning targets) and revisions (for homeworks)? If yes I erred on the higher side. Has the student missed a significant number of engagement-related items that correlate with success (like attendance, daily prep, or autograded homework)? If so, I erred in the lower direction.

I also added a personalized comment in our LMS gradebook to explain any special cases, always including concrete advice and words of encouragement.

What this approach to midterm grades does best is answer the question: “Am I on track?” or in more words, “Is what I’m doing good enough to earn the grade I want?” I emphasize to students that this midterm grade is just an estimate – it’s not a promise and it’s not inevitable. If they’re happy with this estimate, they can continue doing what they’ve been doing. If not, I ask them to come to an office hour to make a concrete plan. I also make it very clear that they do have time to change course and earn a higher grade, if that’s what they’d like to do.

If you want to read a lot more, check out my previous post on midterm grades.

Tuesday: Learning Target Explanations

We have an exam later this week, which means students will be assessed on some new learning targets. A big question, both for students and for me, is “what does it mean to meet a learning target (a.k.a. standard)?” In this class, there can be quite a lot of details to attend to, even in a single relatively small target.

Long ago, I started taking detailed notes about what matters most on each target, and also common mistakes. I quickly realized that I should share these notes with students as a way to clarify my expectations. I talk about these expectations as we learn about the new target in class, but that’s a lot to take in all at once, so having a written reference helps. Since then, that list has evolved into a “Learning Target Explanations” list. I make this available to students from day 1, and encourage them to use it for studying purposes — much of it is written in the form of reminders about things we’ve discussed in class.

Today, I updated the explanation for our next learning target. Doing this kind of update is a fairly short process now that the initial writing is done.

Here’s one example. My very first learning target is:

I.1: I can construct an accurate graph of the antiderivative of a function, including select points, increasing/decreasing, and concavity. (Section 5.1)

Short and sweet, right? But what does that target really mean, in a practical on-the-exam kind of way? Here’s the Learning Target Explanation entry for it:

You’ll be given a function and asked to draw its antiderivative. That means you’ve been given the derivative of the final result. So, use the given derivative to find intervals of increasing/decreasing/etc. in the antiderivative (this part is a Calculus 1 topic). You might be given an integral function as well.

Things to pay special attention to:

• Calculate an appropriate area under a given curve exactly, using geometric shapes to help (such as areas of triangles and rectangles). Sometimes I might give you the areas, especially for irregular shapes.

• Account for “net signed area” by attending to areas above vs. below the x-axis.

• Be careful if you’re given an integral function for which you might get “backwards” limits of integration. Write out an equation that represents the result – don’t try to track it in your head.

• Use the Total Change Theorem to find exact points.

• Figure out where the antiderivative equals zero. This might be given, or else calculate that point yourself if given an integral function.

• Review everything you can from Calc 1 regarding what the derivative tells you about the shape of a curve. Much of this is covered first in Section 1.5 and then in Chapter 3. In particular, be ready to identify and explain how the derivative tells you where the antiderivative is increasing, decreasing, concave up, concave down, linear, and has a local maximum or minimum.

• Draw each part of the graph carefully. Mark specific points that you’ve calculated exactly and make sure your graph goes through them. If a line should be straight, or curved in a certain way (e.g. concave up or down), draw that as clearly as possible. Give yourself a lot of space, and a large scale, to draw well. An ambiguous or hard to read graph could result in not meeting the target.

• Remove the word “it” from your explanations! Use the name of the antiderivative and derivative functions. For example: “Because f is positive on (0,1), F is increasing.”

I also provide a list of practice problems from our textbook, with links (we use Active Calculus, which is free, online, and excellent.) Together with the class slides and activities that I post on our LMS, this document is essentially a pre-made study guide for quizzes and exams.

This has been helpful for students, but also beneficial for me. For students, this provides some extra clarity on what matters in a standard, and reminders about what to study. For me, writing out these reminders has helped me stay consistent when grading, and clarified my own thinking about which things matter and which don’t.

Here’s some more information about what it means to meet a standard.

Wednesday: Being flexible about assessments

Today I was editing a future exam in Calculus 2.3 Each exam has about one page of questions per target. With six total targets to assess, the exam felt too full. Worse, I was certain that one of the learning targets was going to be a technical, annoying, detail-oriented pain to complete (and grade!).

After wrestling with this for a while, I decided to assess that target in a totally different way: with a worksheet in class. I already have a useful activity that directly addresses the target, and we’ll already be working on it in class in teams.

So, I’ll use that worksheet as an assessment. Specifically, students will work in teams on most of the worksheet (for about an hour), and then complete a few additional individual problems after a debrief (for another 30 minutes or so). They’ll have a chance to check in with each other, as a sort of built-in pre-assessment. I designed these additional problems to be easy to check quickly by eye. I’ll look over those individual problems on the fly during class time and immediately record a mark.

This isn’t a core target in my class, so I’ve already decided that I’m OK if it’s assessed in a less “rigorous” way. By removing this target from a timed exam, I give students more room to work on the other targets. The group work ensures that students have some practice, and the individual questions give me a little bit more confidence on individual student knowledge. Best of all, if a student shows some confusion, I can immediately ask a followup question to check their understanding. For students who are struggling, my plan is to offer a reattempt without penalty via a brief oral reassessment during office hours. But I also fully expect that most students will pass the activity (and therefore the target) on the first try.

My main takeaway is that it’s important not to get too committed to my overall class plan. I’m definitely the sort of person who wants to make a plan and then stick to it. That plan, made before this semester started, called for this target to be assessed on an exam—but on the ground, right now, it’s clear that I needed to make a change. Be ready to be flexible when the situation calls for it.

I’ll report back on how this works, but I am hopeful!

Thursday: The importance of relationships

Today is immediately before the Friday at the start of spring break, so I gave an exam. My class is 4 - 6 in the evening. Great professor move, right?

Rather than talking about the details of that exam, I want to mention something unexpected that I noticed. This exam was relatively short, and I gave students our whole 2-hour class block to work on it. Since I knew many students would finish early, I encouraged (but didn’t require) them to stay after completing the exam and work on their next homework. Despite being spring break eve, a large number of students actually did stay! Some worked on other class work, or revised a previous homework assignment. I got good questions and had time to talk individually with some students who needed advice.

What I really felt in that experience was that I’ve managed to establish some trusting relationships with this semester’s students.

Nothing beats having solid relationships with students. Not the best slides, the most innovative activities, the most alternative of grading systems. If students don’t trust you, none of the rest matters.

Building these relationships requires an intentional effort from me, and it’s felt like a slow process this semester. Having multiple snow days near the start didn’t help! There have been signs: I’ve started to see more traffic at office hours. Students are beginning to be more willing to chat with me before or after class. We’re near registration season, so they’ve asked me for class recommendations or advice on getting a math minor. I’ve heard a little bit more about their other classes, their spring break plans, their lives. Many of them stayed after the exam to work on an assignment not due for two weeks when they didn’t have to. These are all minor things, but they hint at a certain level of trust and a feeling of safety in my class.

How do I earn that trust? More than anything I try to be a trustworthy, honest, and straightforward instructor. I tell students, clearly and often, that I want them to succeed, and that I believe they can. I give feedback written in that spirit. I often let students in on my decision-making process and share my reasoning for why class policies work the way they do. I give occasional anonymous surveys, summarize the results, and make real changes if I think they’re warranted. I also do my best to be my honest self in class. I am, and always have been, a huge geek, and I don’t try to hide that. One recent class activity was based around my personal interest in historical copper mining in Michigan (yes, really). That activity actually drew several student comments thanking me for sharing such an interesting topic. I read those comments as meaning “we saw your enthusiasm shining through” or maybe “thanks for sharing a bit of yourself” rather than “We’re actually interested in the total weight of copper mined at the Quincy mine from 1880 through 1905.”

If students trust that you have their best interests at heart, they will be much more willing to take your advice, to listen to honest feedback, to believe you and act when you say “you need to change your approach to this class”. Trust is also a key element to making a successful alternative grading system: Students who trust you will be more willing to also trust that this weird grading system could actually be good for them.

If you’d like to read more, here an old post on the importance of trust and relationships.

Friday: Grading an exam

Today I graded the exam that I gave yesterday. Here are some thoughts about how I’ve arranged learning targets and exams in this class.

On exams, I assess learning targets via one page of questions each. Students earn one overall mark on that target, specifically: Successful, Revisable, or Needs New Attempt.

I’ve written about the Revisable mark many times, but here’s another chance for me to plug it. Essentially, a student who earns Revisable can come to an office hour, explain their mistake, show how to correct it, and then earn an automatic Successful. This is a great way to deal with minor errors or places where it doesn’t make sense to require a student to try a full set of new problems on the next exam.

Let’s not skip the main goal: earning Successful. In this class, I decided that students only need to earn Successful once per learning target in order to earn credit. (In other classes, I often require two Successfuls.) I did this in part because it simplifies the assessment schedule a lot. Students don’t need to spend nearly as much time on assessments and I don’t have to write as many. I already have a quiz or exam every two weeks; requiring two Successfuls would require weekly quizzes or much larger exams.4

However, I do think that it’s important to check on students’ understanding over time, and make sure they’ve retained essential knowledge. In Calculus 2, I do this by identifying five “core” targets. Students must re-attempt these on a required part of the final exam, to show their continued learning. Those attempts adjust the final grade up or down with a plus or minus. Those final attempts can also contribute to the student’s overall grade by counting as the one required Successful, if they still need it.

I’ve also settled on offering a total of three attempts on each target: Two during the semester, and one on the final exam. The two during the semester appear on the first quiz or exam after we’ve covered the material, and then the following quiz or exam two weeks later. After that, there are no more attempts until the final exam. As always, it doesn’t matter when a student earns Successful on each target, as long as they do.

I agree with Sharona Krinsky who often says that “three attempts is the perfect number”. Two attempts is too few to give students a chance to show learning and growth. But four or more attempts encourages students to not take those attempts seriously, and through years of observation, I’ve seen that students who are effectively studying rarely need four or more attempts (and those who aren’t effectively studying need an intervention!).

I also like that students have a first attempt to see how they’re doing, then a followup attempt that lets them correct any minor issues in the first attempt. If they aren’t ready by then, they have the entire rest of the semester to prepare, now knowing that this target is a tough one that they have to focus on. I say exactly this to students.

My class sessions are two hours long and I write exams to take at most one hour, but I offer the full two hours if needed. The goal is to make that time as low-stress as possible for a timed assessment.

In general, I’m pretty happy with all of these choices. They work well for my students and in my class, and I’ve intentionally set up my class to use these choices. Of course, there isn’t just one choice that works for everyone — if these don’t make sense for your situation, you can and should make a different choice!

Final thoughts

I enjoyed the exercise of intentionally reflecting on my day-to-day grading work for Calculus 2. You might enjoy doing something similar. If you do, please consider submitting a guest post proposal to share your reflections with our readers!

It’s Spring Break for David and me this week, even though in Michigan it’s still basically winter. So we’re going to take a week off from regular content. But we have three important things to share with you.

Share Grading for Growth

Listen up!

The first item to share is that the Grading For Growth book is now available as an audiobook!

Our publisher, Routledge Press, has been working with Audiobooks.com for a few months to get the book “translated” into audio form. David and I both thought the finished product was still months away, so we’re very excited to announce its availability. And, as an audiobook guy from way back (I used to listen to “books on tape” via my car’s cassette player) I just think it’s very cool to see the book out in the world in this format, and hopefully even more people can read and use it.

The audiobook has a 9 hour, 26 minute runtime and is available for $25 at Audiobooks.com. A very special shout-out to Michael Hacker, who narrated the book — I am not familiar with his voice-over work but I have to believe this was not the easiest book to narrate!

Calling all alt-graders

The second item is that we are looking for more guest authors.

We’ve been featuring guest authors twice a month on average, for around three years now — which is a mind-blowing fact on its own and a testament to the vibrancy of the alt-grading community. David and I have put out a big call for guest authors twice in the past, and we were able to schedule enough guest authors to stretch through August 2026. That date felt like a long way away at first. But now it’s on the horizon, and we want to start lining up guest authors for late 2026 and into 2027.

If you are using alternative grading, or connected to it in some way — as an instructor, a student, an administrator, etc. — and have something you want to share here on the Substack, we want to hear from you! Just fill out this form with a brief description of your idea:

Submit an idea for a guest post

We are looking not only for first-time guest authors, but also for those who’ve given us articles before but have something new, or something further to share.

David and I look at the submissions once every couple of weeks as they come in. Unlike a peer-reviewed journal, we hold a “the more the merrier” philosophy and our goal is to say “yes” to as many ideas as we can. We’ll get back to you shortly and try to get you into the queue.

So if you have something you always wanted to share with the general public, let Grading for Growth be your platform – and let us know what’s on your mind.

Coming Soon

The third item is a look ahead to what’s coming here, once break is over.

• Next week (March 16) David will give us a look at a week in the grading life of his Calculus 2 class.

• On March 23, Erin Coffman (IU Indianapolis) will share an implementation of alt-grading in an introductory data science course — with lots of cool data visualizations as you would expect.

• On March 30, since it’s a “fifth Monday”, we’ll be running a retrospective featuring some of the best from the blog both past and present.

• On April 6, Katie Baker (Rosemont College) will explore the dynamics of alternative grading when your college is about to merge with another, larger university.

We hope you are all heading into your own Spring Break seasons with good vibes and good experiences from helping your students grow.

Teaching and learning are two of the most distinctly human activities that a person can engage in. With alternative grading practices, what we really seek is to place the humanity of both instructors and learners at the center of education. Persistent engagement with feedback loops is not only the primary, but I believe the only way that humans learn anything of significance. By engaging in those loops we become more human ourselves, and to build a class around that engagement is both an opportunity and a privilege for us instructors.

But it can also be very hard work, and we need all the help we can get – not only from other humans (which is why you’re here at this blog, hopefully) but also from technology. Many of us already lean quite heavily on technology to help in our grading practice, and whenever there is a chance that some new technology might help us do the human work of teaching better, it’s worth a look.

I believe that generative artificial intelligence tools fall into that category of powerful technologies that can help and are therefore worth a look. And in this post, I’m going to share some ways where generative AI can be a productive partner in our work with alternative grading.

Where I stand on generative AI

I know the readership of this blog pretty well and I know that some of you have very strong negative opinions about generative AI in education. I would ask that you hear me out.

I am a “techno-pragmatist” with respect to AI. I neither mindlessly embrace it nor mindlessly resist it, but am convinced that it has the potential to be a powerful tool for helping humans do uniquely human things, when used with appropriate care. It is an epoch-defining technology that demands my attention as both a teacher and a scholar. I am committed to engaging with AI in that role: as a teacher-scholar, with an open and active mind, grounded in larger contexts and truths, and coming to my own conclusions based on data and, importantly, my experiences.

I cannot in good faith accept or reject something I don’t understand, and I can’t understand something I haven’t experienced. So for the last several months, I’ve been setting aside 30-60 minutes each working day to engage with AI tools in some way: learning how different ones work, using them to do simple tasks, using them to do real work, pushing their boundaries, thinking about the results. A lot of what’s in this post is an after-action report of those experiences.

I am aware that there are concerns about AI: its impact on human cognition, its impact on the environment, and more. I’ve looked into these as part of my AI engagement strategy and honestly, for me the jury is out on all those concerns. I’ve found no concern or issue that categorically leads me to stop exploring AI1. I’ll keep my mind open to changing my approach in the future as I learn more.

I’m happy to go into more depth and hear your responses to all that, in the comments.

But for now, here are three ways that AI can be useful for faculty doing alternative grading, and three ways it can be useful for students in alt-graded courses.

Faculty use #1: Making Clearly Defined Standards

In our usual visual about the Four Pillars of Alternative Grading, “clearly defined standards” comes first if you read it from left to right. It’s also first in the sense that writing standards is typically the first thing you do when building a course, and also first in the sense of being, in many ways, the most important of the pillars. It’s hard to implement any of the other three pillars if you don’t have clearly defined learning outcomes.

Writing a good set of standards seems to require threading three different needles: Your standards must be simple and easy for students to understand, they must capture all the important ideas of the course, and there can’t be too many of them. Getting two out of those three to work doesn’t seem so bad, but getting all three can be devilishly hard. This is where AI can help.

If you have a standard that seems important and you want to assess it and it belongs in your list, but you’re unsure about its clarity, an AI can help you clarify it, like below2:

Note how the prompt was phrased: “Please give me ideas”. We’re not asking the AI to write the standards for us, in order to use the results without thinking about them. That’s the very thing we should be avoiding. Instead, we’re just asking for help. As the instructor, you can take or leave the results, or make a remix that uses the best parts of each.

If you have a bunch of standards that all seem good, but there are too many of them, you can ask AI to cut or consolidate them for you.

The original list I gave Claude is here; the list of standards that Claude generated continues past the bottom of the screenshot. I don’t think I would use verbatim what Claude generated, mainly because this seems to consolidate quite a lot of concepts under a single standard, to the point of being double- or triple- or many-barrelled. (Maybe asking for 16 instead of 10 would help spread them out a bit.) But it would certainly get me thinking concretely about thematically grouping standards into assessable units.

Fun fact about this example: Long-time readers will know that my first attempt at specifications grading in 2015 involved having 68 learning standards for my course. The 33 standards that are in the file that I gave Claude were part of that list. So had an AI tool like this been available for me in 2015, I might not have been in grading jail for the entire semester attempting to manage 68 sets of assessments every week3.

Or if you just have no idea where to start with your standards, you can feed an AI tool your course materials and any other relevant information and ask it to come up with a list itself. As an exercise, I took the syllabus for one of the courses I am currently teaching and removed all references to learning targets from it, gave the syllabus (which contained the course description) to Claude, and asked it to create Learning Targets for the course. It produced a list of 14 of these, which mostly coincided with the learning targets I had made myself, which appear in the appendix to the syllabus4.

There was so much overlap in my human-generated Learning Targets and these, that I would feel comfortable in the future using Claude to double-check my work in a new course where I needed to write standards from scratch.

Faculty use #2: Generating reattempts

While having clearly defined standards is the first and perhaps the most important pillar, the real heart of alternative grading is the fourth pillar, reattempts without penalty. This is also one of the hardest to implement because of the time factor involved, not only for grading reattempts, but for making up the reattempts. Here too, AI can be extremely useful.

I avoid using AI for creative work, including making teaching materials like flipped-classroom video content or small group activities for class. But I have no problem doing something like the following, which is anything but creative:

And just like that, I’ve drafted the entire semester’s worth of assessments on this particular learning target in about the same amount of time it took me to write the prompt. Again, however, you as the instructor need to remain in the loop and not just accept the LLM’s output. Here for example, I might want to vary the phrasing of the conditional statements so it’s not always “If A, then B” (but perhaps “B if A”, or “A is sufficient for B”, etc.).

Faculty use #3: Making exemplars

When asking students to do higher level work, it’s good to have examples of work that meets the standard to certain degrees and also fails to meet the standard for different reasons. This is really is creative work on the part of professors, so much so that it can be hard to create something that really hits the mark with students. This is a good use case for an AI tool, at least for giving an assist:

The example from Claude goes on to explain why the first one is well written, but the second one is poorly written. Having seen one decent exemplar5, it’s easier for me as a human instructor to make more.

You could follow this up in class with an activity where students grade these two responses using your standards. Or alternatively, you can instruct students on how to do this for themselves, which gets us into our next group of three use cases.

Student use #1: Deliberate practice

I’ve written here a lot lately about the role of deliberate practice in alternative grading. In those posts I noted some defining characteristics of this kind of practice which include an intentional design to improve performance, the ability to be repeated indefinitely, and continuous availability. When I was a student, I never heard the word “practice” applied to learning math – that was something I did for music class, not math. Perhaps that’s because, unlike music classes where time-tested exercises and practice regimens were and still are common6, the means of practice in a math class were limited to the small number and limited nature of exercises in my textbooks. But students today can do things like this:

This is a fully-realized web app that can be run in a browser, that not only has the correct math in it but also does a really nice job pedagogically of scaffolding the complexity of the tasks.7

AI makes the availability of useful practice tools less of an issue. What is still an issue is inculcating the mindset of practicing in students and creating a culture of deliberate practice in our classes. For example, a student who has never had a discussion with their prof about deliberate practice might not think to add the bits about “increasing level of challenge” and “immediate feedback” to the prompt. And that is where you and I, as the human instructors, come in — to initiate and sustain discussions about what kinds of practice are truly useful.

Student use #2: Decoding feedback

In our description of feedback loops here at the blog, they start with a learner attempting a task, and then next they receive feedback from a trusted third party. But then there’s an important third step: making sense of the feedback. We try to give helpful feedback, but despite our best efforts, students sometimes may not speak the language (perhaps literally) and will need help in decoding what we tell them.

In this example, I’m pretending to be the student who wrote the poorly written contradiction proof from earlier, and I’ve made up some instructor feedback that is maybe trying to be helpful, but it’s not especially clear.

Here I am, acting as the student asking Claude for help in interpreting the feedback:

Now, there is a big caveat with this use case, and you can see it in the final paragraph of the prompt. In the original version of this example, I didn’t have that paragraph, and Claude produced a completely corrected proof, which would then be very easy for me to copy and paste. If the student doesn’t explicitly restrict the LLM to just the part of the feedback loop about interpreting what the feedback says, the default behavior of the tool will likely be to do the rest of the work for the student.

Obviously, this is not what we want, and its heightened likelihood, thanks not only to AI but the internet in general8, is a serious problem. But I believe we’re not going to solve the problem by banning AI technology from classes — as if any such ban would be more than a performative statement in our syllabi. A better solution is to communicate with students about the right and wrong ways to complete their work and the role of reattempts without penalty in doing so honestly. That communication should include, but should definitely not be limited to, sample boilerplate about academic integrity, like mine above, that a students can add to AI queries This may not eliminate cheating, but it will at least lessen its value proposition.

Student use #3: Scaffolding metacognition

In an ungrading/collaborative grading situation, students are tasked with assembling a portfolio of their work throughout the semester and then using it to make a case for themselves at the end of the course for the grade they should receive. It’s been well documented by instructors using collaborative grading, including many of our guest authors, that the part about making a case for oneself is easier for some students than others. Many students can struggle to pull together a narrative that accurately explains the arc of their learning journey and puts themselves in a positive light, even if the portfolio of work is consistently excellent.

Here is where AI can be very helpful to these students. I don’t have an example of this to share, but hypothetically this is how it could work: A student can take the items for their portfolio as individual files and put them into a folder and then upload them to an AI, along with the syllabus and standards for the course, and ask the AI to help them tell their own story. For example, “I believe I have met the standards for a grade of B in this course, but I’m having trouble creating a brief narrative explaining why. Can you give me ideas for how to talk about this with my professor?” Students should not simply use what the LLM tells them as if it were their own reasoning — that is a standard caveat to everything here. But in the spirit of this article, where we are enlisting AI as a helper just as we might enlist a trusted human, this can be quite helpful indeed.

Conclusion: One thing we should never do

I want to end this post by mentioning one thing, other than cheating, that we should absolutely not be doing with AI: We should not use AI to actually grade our students’ work.

It’s very tempting to say that generative AI offers us the opportunity to implement alternative grading at scale, by pulling in folders full of student work as PDFs and distributing feedback out to students that they can then work on. AI seems to offer a solution to one of the most vexing problems of alternative grading: how to do it for large classes.

But there are all kinds of reasons why this is a bad idea. One of the main reasons is privacy; I can’t think of a single LLM tool that’s FERPA compliant. (I could be wrong, so correct me in the comments.) But more generally, grading is supposed to happen in the context of a relationship between professors and students. Taking ourselves out of the feedback loop, is categorically against the spirit of alternative grading and just not a good move generally speaking, and we need to avoid using tech of any sort to do it.

If you’re an AI user and have other helpful use cases to share, drop them in the comments!

Kerry Mandulak is Professor in the School of Communication Sciences and Disorders (CSD) at Pacific University in Forest Grove, Oregon, where she has taught in the undergraduate and graduate speech-language pathology program for 14 years. Her research agenda has focused on holistic review for graduate admissions in CSD, and through that work, has developed deep interest in inclusive and accessible teaching practices. She is currently completing a sabbatical in the Spring semester of 2026 focused on investigating alternative grading practices in CSD. In her free time, she serves as a lacrosse training partner for her older son, a supporter of her younger son’s pursuit of many hyper-focused interests, a novice birder, and an expert college gymnastics fan! You can reach her at mandulak@pacificu.edu.

In the field of speech-language pathology, admissions processes act as the ultimate gatekeeper into the profession, but likely don’t tell the full story of the skills and abilities that our students and future clinicians possess. Below, I share my journey of how researching admissions practices in our field led me to advocating for alternative grading. I’ll describe how co-creating a national faculty learning community centered on grading for growth has helped facilitate change in our profession.

I teach at Pacific University, just outside of the Portland metro area in Oregon. The university has a unique identity in the Pacific Northwest as a small, private, undergraduate liberal arts university with a robust offering of graduate programs, notably in the health professions. I teach in the School of Communication Sciences and Disorders (CSD) that will soon reside within the graduate health professions college. CSD as a field collectively refers to the two professions of speech-language pathology (SLP) and audiology. I am a speech-language pathologist by training, and teach in our undergraduate minor program, which provides the pre-requisite coursework to prepare students for graduate work in SLP.

A master’s degree is required to become an SLP practitioner, and historically, entrance into SLP programs has been highly competitive. What we know from the analysis of national admissions data is that GPA is the #1 predictor of admissions offers, and therefore, the advice for “how to get into graduate school” was always simply … focus on your grades. I was the recipient of that advice 30 years ago when I was applying to programs – a 4.0 in my SLP coursework was my only goal (not necessarily what I was learning to get that GPA and prepare for graduate work). As I moved into a faculty position, the advice I received became the advice I gave: grades were most important, focus on your grades.

As I continued in my faculty role, my interest in the intersection between the emphasis on traditional grading, our admissions processes, and what GPA actually predicts about student success in our programs and as future clinicians continued to grow. I developed this interest through serving on admission committees, both within my program and at the national level.

About 10 years ago, I encountered a significant “disorienting dilemma” as I chaired the Graduate Admissions committee within our program. As the faculty began the work of making decisions about admissions offers, one applicant had a history of a “low” undergraduate GPA in an unrelated major, explained by a period of trauma that was followed by a period of growth and resilience. This student’s more recent performance in CSD coursework was stellar. We also viewed this applicant’s lived experiences as being deeply connected to becoming a competent and compassionate future clinician, above and beyond what a specific GPA could determine. We advocated for this applicant fiercely, but from the top-down levels of administration, ultimately, a denial was issued, strictly based on those undergraduate grades. As a committee, we could recognize the importance of academic performance, but did it have to be the ultimate and absolute deciding factor above all else?

The process of finding a solution to this issue served as a lightning bolt moment that ultimately prompted me to shift and change my entire research agenda to studying holistic review for graduate admissions (more recently named Mission-Aligned Selection). The field of medicine initially championed these methods as a way to simultaneously consider a student’s experiences, personal attributes, and context in which their experiences occurred, while still taking academic metrics (GPA) into account. Similar to medicine, in SLP, admissions serves as the gatekeeper for graduate work and therefore the future workforce. If SLP remained tethered to the belief that GPA was the only proxy for success, we were likely excluding many capable and qualified students, with important and relevant lived experiences, skills, and abilities from becoming speech-language pathologists. As a helping profession that requires both intelligence and compassion to work with patients experiencing communication challenges, it’s important that our clinical workforce has both the knowledge and skills to serve our patient populations. Adopting holistic review processes as a discipline, for me, seemed like a logical solution.

Since that original disorienting dilemma, I’ve worked to challenge longstanding beliefs about what merits admission in our field, and promote the implementation of holistic review. Seeing it become more accepted and widely adopted across graduate programs in SLP has been gratifying; however, that led me to another crucial question - why are we not examining how we are grading students if GPA is the most influential factor that merits graduate school admission? I hypothesized that traditional grading practices might not be representing what our students were learning, and was concerned that grades alone might not capture the important skills and abilities we were also looking for in graduate program applications.

Changing ingrained practices can be difficult and slow. As a field, could we build on the momentum of changing admissions and channel it into conversations and discussions about shifting grading practices? In order to start to do this work, I was hoping other faculty members would be willing to connect and collaborate to explore alternative grading - together.

As I began to imagine what would be needed to do this work, I began to see and hear from other faculty members in our field that they were similarly interested. When opportunities arose to collaborate with others, I paid attention and took advantage of those possibilities, and found a community of like minded folks! I am hoping that sharing the journey of finding that community, and what worked for us to build it together, might inspire others to create their own.

Collaboration instead of mentorship

During the pandemic, our national organization hosted a virtual Teaching Symposium on Foundational CSD Science Courses, followed by follow-up webinars on various teaching methods. In those webinars, I shared my thoughts and early experiments with alternative grading, and it caught the attention of a fellow attendee. She reached out afterward seeking mentorship on how to conduct research on alternative grading, and I countered with a different proposal – “How about we collaborate?” That shift from hierarchy to partnership led to a research study conducted across our two programs, and a presentation at our annual national conference to disseminate the results. We were delighted to have a surprisingly full room, and felt we had tapped into an area of interest for many people. How could we keep this conversation going?

Find your people: They’re at your presentations!

At that presentation, we used a QR code with a link to Padlet in order to provide our slides, our contact information, and other resources. In addition, we included a place for attendees to include their contact information if people were interested in this work. Post-conference, we followed up with an email about forming a faculty learning community, focused on reading and discussing Grading for Growth together. The final community that resulted was an eclectic group of eight faculty members that spanned the country with varied roles, responsibilities, and years of experience in higher education. While some of us had already begun experimenting with alternative grading in our course design, others had never implemented it and were looking for ways to get started.

What’s the common thread? Lean into it.

While a learning community focused on alternative grading can certainly be successful with varied disciplines present, having the discipline-specific familiarity of SLP and all of the areas we were teaching, across undergraduate and graduate coursework, also had its benefits. All of us as speech-language pathologists had a general understanding of the content and context for all of the courses we were redesigning. Having that shared knowledge and awareness of what might work for a Phonetics class versus Language Development at the undergraduate level, for example, and Stuttering versus Alternative and Augmentative Communication at the graduate level, allowed us to work from shared experiences and also shared curiosity.

It doesn’t have to be formal, but it is important to be organized.

There are formal methods and guidelines for implementing faculty learning communities, often with the support of a teaching and learning center on campus. We chose to adopt the “book club” format, and began meeting biweekly in March 2024. Every other week, one of the eight of us led the discussion focused on two chapters of Grading for Growth. We used Padlet as a collaborative working space to populate discussion questions for each meeting, share examples of our work, post links and resources, and have a “parking lot” for ideas that we didn’t want to lose. We finished that Spring semester with a “virtual retreat”: in order to bridge three time zones and do the deep work of redesigning grading in our courses, we scheduled two-hour work blocks with several check-ins within the day. This dedicated retreat time during the summer gave us some space for the work that is hard to carve out during a busy semester.

Get your work out there - start talking about it!

Midway through spring semester, the deadline for our annual conference proposals came due. We submitted a proposal to our national conference not just on alternative grading, but on the power of the community itself. It was selected as a “Visionary Session”—one of only 150 chosen from 3,300 submissions, so we were encouraged that these ideas were of interest. We followed up six months later with a proposal for our annual academic programs conference, with a “workshop” format, where we did a series of short presentations on the four pillars of Grading for Growth, and then allowed attendees to work in small groups on a concept or idea after it was presented. What we were gathering from the feedback and this feeling of momentum was that alternative grading was needed in our profession, faculty members were interested and ready to implement these methods, and a community built from the ground up was a way to drive this shift to implementing alternative grading.

Keep curiosity at the core

As a community, we have felt fortunate that the overall “vibe” of our learning community has been one of sharing, collaboration, and most importantly curiosity, without judgment. We have felt the freedom of sharing ideas, allowing a space for critique and comment, knowing that we have the best intentions for each other and for our students’ success, and having the support of other members who are willing to be your thought-partner about your ideas. We also acknowledge the privilege that comes from having our own “home” programs consisting of colleagues that are not resistant to the work of modifying grading practices. While we all believe we are student-centered and also center kindness in our teaching, we also needed a space to air grievances when we encountered student resistance to the work. We kept each other in check but also helped focus on solutions for moments of maybe taking on too much, or making it too complex. We took the work seriously, but didn’t always take ourselves seriously, which allowed for some levity when it felt like the negative feedback from students became tough to bear, even when it’s expected with new or innovative teaching methods. At the core of this work was a spirit of curiosity - about each other’s courses, our programs, and our students, and how we could facilitate their success in our classrooms.

Support others in doing the good work

There are always opportunities, beyond your own informal or formal learning community, to involve others in the work. I’ve used the social capital that comes with seniority in my field to reach out to conference organizers and ask for other faculty members to be invited to present with me. Within our community, I’ve been able to serve as a mentor and collaborator on projects and opportunities that have arisen during our work together, and get to support and facilitate the success of pre-tenure faculty members. The opportunities for connection, collaboration, and the strong working relationships that result are inherent rewards for working in community.

Conclusion

At its core, SLP is a profession that uses evidence-based practice to make informed decisions about the assessment and treatment of patients with communication challenges. Within our teaching, we also have to embrace evidence-based education for best teaching practices. Our community started as a way to learn from each other and to strive toward collaboration in order to build that evidence base around alternative grading in our field. We are looking forward to continuing to work together to design research studies to explore and examine the feasibility and outcomes of alternative grading for more widespread dissemination.

Acknowledgements to my original collaborator, Jen Essig (Program Director, Indiana University - South Bend), and our GFG faculty learning community → Laura Chapman (University of Scranton), Trish Geels (Indiana University - South Bend), Heather Isaacson (Northern Michigan University), AnnMarie Knight (Oklahoma Baptist University), Alli Sauerwein (Southern Illinois University Edwardsville), Sam Scripture (Worcester State University).

So you’ve set up an alternative grading system. It’s going well and you like the results… mostly. But maybe something is nagging at the back of your mind: Alternative grading is all about feedback loops, opportunities for students to try, struggle, learn, and improve. Does the rest of your class align with that too?

The longer I’ve used alternative grading, the more I’ve become convinced that it’s just one part of a balanced diet of educational ideas that center on feedback loops. Helping students see the value of struggle and growth in one part of your class is good – sending that same message across all parts of the class is even better!

Here are some more elements you can bring into your classes that work well with alternative grading. Each of these ideas is based on feedback loops, so they reinforce the goals of alternative grading and send a consistent message about what you value.

Active learning

“Active learning” is a very broad term, encompassing everything from groupwork and worksheets to student presentations to wholly student-driven inquiry-based classes. All active learning aims to directly center students in the learning process, in contrast to traditional lecture.

A necessary part of active learning is struggle, although this isn’t always made explicit. It’s easy for a student to think that they understand an idea when they’re listening to somebody else talk about it. On the other hand, active engagement with new ideas forces students to deal with the inevitable difficulties that surface, sort them out, and work to improve their understanding. Pair this with helpful in-class feedback, and you have a perfect feedback loop.

For example, an instructor might build in low-stakes initial encounters with new ideas (as in flipped learning, which Robert has written much more about) that are then re-encountered, in more advanced forms, during class time. This is a feedback loop that’s built right in to the structure of the class. Inquiry-based classes often involve students inquiring into new ideas, working to solve problems, and then presenting to their peers. All parts of this process help students hone their ability to process and communicate complex ideas, face questions, and fix errors on the fly (I’ve written a lot more about grading presentations too). Large-scale active learning methods like Peer Instruction fundamentally give students a structured and safe opportunity to commit to an answer – possibly wrong! – and then revise their thinking and grow from the experience.

The feedback loops in active learning and alternative grading are the same idea. If you use both of these in the same class, you can make the feedback loop connection explicit. I do this by telling students about the value of struggle in learning, and pointing out how each part of the class promotes this. When both class activities and grading send a consistent message about the value of struggle, students are more likely to get that message, believe it, and use it.

Active learning and alternative grading fit together very well. When you walk the walk as well as talk the talk, students are more likely to buy in to the benefits of feedback loops.

Student teams

Team-based learning can also help students engage with feedback loops. There are dozens of ways, formal and informal, to use teams in a classroom setting. In this context I’m thinking of semi-permanent “base groups” of 3-4 students that are set up for pedagogical purposes within a specific class. These teams could be set up to complete a project, to work together on class activities, to study outside of class, or many other goals. The key is that students work with the same peers regularly for multiple weeks, giving them time to build trust and confidence in each other.

The main benefit of such teams is the relationships that they help build, and the opportunities for support that they promote. As teammates grow more comfortable with each other, they begin to form a small, safe world that can gently encourage struggle and productive failure. Teammates are usually more willing to admit confusion or show mistakes to each other, rather than doing so in front of the instructor or the entire class. This encourages constant informal feedback loops within the teams. Of course, it’s critical to help teams build this trusting relationship, which I believe is best done through group-worthy tasks that show teammates the value of each others’ strengths.

If you make a clear connection to how teams can support each other, then these teams can reinforce the importance of feedback loops in your class. Again, this sends a consistent message about what you value.

There are also grading-specific advantages to using teams. If a student doesn’t understand how some part of my alternative grading system works, I’ve noticed that they are much more willing to ask their teammates for an explanation rather than staying quiet (and confused). I’ve also found that if some students have been in alternatively graded classes before, they will “sell” the benefits of the system to their teammates who are new to the system. This helps build buy-in and trust far beyond what I could do individually.

Goal-setting and reflection

This is maybe the most unusual item in this post. Several years ago, I started asking students in my Euclidean Geometry class to set individual goals for the semester. We use the SMART framework to set practical, achievable, yet nontrivial goals.

This started out as a way to encourage students to stretch and grow beyond the formal content of the course. For students who are already familiar with the content, a personal goal presents a much-needed challenge. For others who are overwhelmed by new and abstract ideas, individual goals give them something more personal and concrete to focus on. For some, a goal provides a challenge to dig into a difficult topic that they might otherwise try to avoid. In every case, I ask students to propose a goal that feels like a “stretch” to them, and to explain why they have room for growth in that direction. This helps ensure that students aren’t choosing things that they are already good at.

There’s the connection to alternative grading: As with everything else in this post, individual goals are a chance for students to engage in feedback loops. Goals push students to try things they might not otherwise do, which necessarily involves taking risks and possibly failing (temporarily!) along the way. And since these goals are happening in an alternatively graded class (in my case, collaboratively graded), students know that early struggles or initial failures with their goals won’t permanently hurt them.

I make it very clear that the actual standard I care about is for students to take consistent steps towards meeting their goal. Periodically, I ask students to reflect on their progress, citing specific examples, and eventually self-assess their growth towards meeting their goal at the end of the semester. This self-assessment contributes to an overall narrative description of how they’ve earned a specific letter grade, including selected evidence from their work during the semester. They aim to meet their goal, but it’s the process of making that stretch, not the final result, that matters. This also helps smooth out the impact of bumps, minor failures, or other difficulties along the way.

I also help: When I see a student working towards their goal, I praise them for that, in person or by email. We have occasional check-in meetings in which I ask students to reflect on the path towards meeting their goals, which includes the chance to adjust or extend the goal if needed. If a student needs a bit of a kick-start, I’ll sometimes create a situation where they can take a meaningful step forward. For example: I use daily student presentations in my Euclidean Geometry class. More than once I’ve had a student whose goal was to face a fear of presenting by volunteering to present more often… but after a week or two, they hadn’t volunteered at all. To help those students, I offer a guaranteed chance to present a problem of their choice (avoiding the usual process of selecting presenters from a group of volunteers), as long as they do it in the next class. Inevitably, they take that offer and do just fine – and as a result, they see how a small goal can be achievable, giving them more willingness to take those small steps in the future.

Goals tend to focus on student presentations (which are a significant part of the class) or mathematical writing (the class comes right after our “intro to proof writing” class, and students are still learning what “proof” really means). Over time, students have found ways to make connections across the curriculum as well: I’ve had pre-service teachers set goals that involved thinking about geometry from their future students’ points of view, practicing skills from education classes such as actively anticipating student questions, and preparing materials they might want to use in their future classrooms.1

Some students even push in unexpected directions. I’ve had students set goals to not use my flexible due-date policy, challenging themselves to turn in work on time (yes, this was a genuine stretch). Others have asked me to “cold call” them to present their work, facing their fear of being unprepared.

In other words, the consistent message of the importance of feedback loops actually encourages students to set themselves up for struggle and even failure – but in a safe context where they know that I have their back.

Sending a consistent message

It’s important to send a consistent message. If you’re promoting the value of feedback loops in your grading, do students get that same message from other parts of the course? Or might they sense a contradiction? You can reinforce the value of feedback loops through how you structure class time, how students are organized, and through the types of assignments you have.2

The Grading Conference!

One more thing: Registration for the 2026 Grading Conference is open! The Grading Conference is an annual online conference all about alternative grading. This year it will be June 16-18, 2026 on Zoom. The conference has pay-what-you-can and institutional registration options too.

Learn more about the Grading Conference

Ainsley Vergara is a clinical assistant professor in the Communication Sciences and Special Education department at the University of Georgia. She teaches didactic courses at the undergraduate and graduate levels and supervises graduate clinical instruction in UGA’s on-site clinic and in the community. Her background as a speech-language pathologist inspired her to rethink traditional grading and redesign her courses to center student learning, reflection, and application. These days, she spends much of her time thinking and talking about alternative grading with anyone who will listen. Ainsley lives in Athens, Georgia with her husband (Ryan), fur child (Gio), and two human children (Nora & Alden). You can connect with her via email at avergara@uga.edu

Becoming an A+ Speech-Language Pathologist

As an instructor in a high-demand, limited-enrollment pre-professional program, I teach driven students who have competed for years to gain admission to the University of Georgia, then the undergraduate cohort, and ultimately graduate school. Grades become markers of excellence and measures of success, often at the expense of creativity, risk-taking, and mental health. I once wrestled with that same anxiety in the very seats where my students now sit.

From high school through college, I protected my 4.0 GPA at all costs. As an undergraduate in Communication Sciences and Disorders (CMSD), I often memorized information for the sake of a grade—after all, graduate school admissions were even more competitive. This fixation on earning a 93 in every course gradually pulled my attention away from genuine learning.

When I began treating clients as a graduate clinician, my definition of “accomplishment” shifted. I learned that speech therapy is dynamic, shaped by patient factors, evidence, and weekly progress. While some interventions follow clear procedures, others depend entirely on the individual, requiring flexibility over perfection. Within those unpredictable, real situations, I fell in love with the process.

The only “grade” I received that semester reflected my independence in professional and clinical competencies. Motivated by client outcomes and my own growth, independence became my passion, not an obligation. Although I was pleased to earn an “A,” I was far prouder of the skills I developed as a speech-language pathologist.

Transitioning into Academia

After several years of clinical work, I returned to UGA as clinical faculty. Within my first semester, I lost count of how often students searched for the “right” answer—the project they thought I wanted. Recognizing that familiar anxiety to get everything “just right,” I sought an approach that prioritized skill development and professional competence over validation through an A. Knowing that clinical practice relies on feedback, mistakes, and teamwork, I questioned why I expected pre-professional undergraduates to execute assignments perfectly on the first try. I realized I needed a pedagogical shift toward learning and competence over numerical grades.

Enter alternative grading. Surrounded by a small group of faculty in a UGA Faculty Learning Community (FLC) focused on alternative grading, I decided to take the leap.

First Attempt: Specifications Grading

For my initial experiment, I chose an undergraduate course, Professional and Clinical Principles in CMSD (CMSD 5000). The course enrolls approximately 75 students and focuses on the legal, professional, and foundational principles of clinical practice in audiology and speech-language pathology. My classroom and clinical work reveals common gaps in students’ professional and clinical skills entering graduate training, and I hoped earlier support would build more confident, competent clinicians.

I was more inclined to take this risk because this course is typically one of students’ final undergraduate courses; by then, graduate applications are submitted, the pressure eases, and students arrive more open to collaboration.

When I first began teaching CMSD 5000, I maintained the previous instructor’s traditional, points-based grading system. As an academia newcomer, I was unaware of alternative options. While I felt relatively confident in my lecturing, I was surprised by limited student participation and minimal creativity in written work. I also recognized that artificial intelligence would permanently shape student engagement, and several of my assignments could easily be completed by AI. It was clear my assessments needed an overhaul to better engage students and more authentically represent their learning.

I attended Center for Teaching and Learning events whenever possible, but my “aha moment” occurred during my commute while listening to the “Teaching in Higher Ed” podcast. In the episode “A Pedagogy of Kindness,” Cate Denial described kindness as a “revolutionary and evidence-based teaching practice in higher education, challenging academia’s traditional focus on rigor over compassion.” She also introduced the concept of “ungrading.” Intrigued, I spent hours researching alternative assessment but quickly realized I needed guidance. I joined our university’s Ungrading Faculty Learning Community, a small group of faculty who had already embraced alternative assessment.

After a semester with the Ungrading FLC, I felt brave enough to begin exploring alternative assessment in CMSD 5000. As one of the newer faculty members in my college, though, I was nervous to fully embrace alternative assessment as described by Dr. Denial. Instead, I began with specifications grading, which offered clear structure and expectations. Many posts here do a great job of explaining specs grading, so I won’t repeat them here; if you’re unfamiliar, check out Linda Nilson’s book, “Specifications Grading 2.0: Restoring Rigor, Motivating Students, Saving Faculty Time, and Developing Career Competencies.” It felt the easiest to explain to students and seemed like a stepping stone to Dr. Denial’s style of alternative assessment.

To establish a clear course structure, I pre-determined “grade bundles” outlining the work required to earn each grade, which were explicitly defined in the syllabus. For example, earning an A required completing all assignments to a satisfactory level and meeting specific attendance standards, while B and C grades required fewer assignments and lower attendance expectations.

As I prepared for my first semester using specifications grading, generating “specs questions” for my assignments was more difficult than I anticipated. I initially viewed specs questions as a means for structure and quality control. Explicitly integrating the Transparency in Learning and Teaching (TILT) framework helped me revise assignments for clarity, as TILT guides instructors to clearly convey the purpose, task(s), and criteria for a given assignment. That emphasis on clearly articulated criteria aligned naturally with the creation of specs questions which students completed prior to submission as a structured self-check.

CMSD 5000 is divided into three themes/units, and following each theme, students worked in groups to “show what they know” in an assignment called the “Thematic Integration Product (TIP).” The TILT framework clarified TIP expectations, and students knew revision was built into the summative assessment process. The TIP allowed students to use any format to integrate each unit topic while applying course content to hypothetical clients. Beyond the TILT instructions, students were free to choose how they showcased their knowledge.

The specifications questions included in the assignment prompted students to reflect on assignment quality, content, and accuracy prior to submission; however, the final determination of whether the work met specifications, or were “Satisfactory,” remained my responsibility as the instructor. Compared with previous semesters, when students typically chose the safe route (hello, PowerPoints!), the first semester of specifications grading saw students truly showing up and showing out. Groups wrote scripts, filmed instructional videos, created comics, crafted infographics, and more to integrate and share course content. Students shared that they enjoyed the process, even citing new friendships with group members, and I genuinely loved reviewing their products and reflections.

As mentioned above, feedback and revision were central to the course. Following all assignment submissions, I provided detailed feedback, and students were given opportunities to revise their work until they reached a “yes” for each of those questions (found within the assignment instructions), translating to a “Satisfactory” for the overall assignment. The focus remained on the process of learning rather than perfection. This feedback-and-revision cycle led to rich discourse and noticeable growth over the semester, with several students sharing that this was their first experience being allowed to revise large projects.

By removing the fear of failure associated with traditional grading, I hoped to encourage deeper engagement. What I observed was increased participation in class discussions, greater willingness to take academic risks, and more thoughtful reflections on learning. After two semesters using this model, though, I realized that specifications grading still seemed to feed into students’ grade-centered thinking. Because I teach students who have typically excelled throughout their academic careers, nearly all students chose the A bundle by default, and they still frequently asked questions about whether individual assignments “counted” toward the A bundle or met specifications. These questions often reflected a desire for grade validation rather than engagement with feedback for learning.

Removing the Training Wheels: Self-Assessment

The following year, while maintaining specifications grading in CMSD 5000, I continued working with my FLC colleagues to explore ways to move even further towards a student-driven assessment model. After many hours of conversation and reviews of alternative assessment literature, we determined a self-assessment model may be a better fit for the course. Within our FLC, we defined self-assessment as an assessment practice that decenters traditional grading mechanisms and asks students to assess their own grades through structured reflections.

Thinking back to my first exposure to alternative assessment through the “Teaching in Higher Ed” podcast, I realized self-assessment was always my ultimate goal. My semesters using specifications grading provided me the confidence and scaffolding to continue moving in that direction. In my mind, specifications grading acted as my “training wheels,” with structures including instructor-determined grading bundles and questions about “satisfactory” work. Though students no longer received numerical scores for individual assignments, I still maintained a high level of control within specifications grading.

Before implementing self-assessment in CMSD 5000, I piloted this framework in classes with fewer than 30 students: my graduate-level course and a one-hour freshman seminar. I noticed increased self-examination, creativity, and deeper application of course content across assignments and reflection. Students also initiated more frequent conversations about course material and related clinical questions. After successful runs in both smaller classes, I decided to remove the training wheels for this larger group of students.

When I shifted to self-assessment in CMSD 5000, I felt strongly that I needed to explain the rationale behind this assessment practice. I personally chose this alternative assessment journey to improve student learning and better mirror the clinical world, so I provide students with a related analogy. I explain to students that my course more closely resembles a workplace than a traditional undergraduate class. I share that my own annual evaluations require me to demonstrate to my department head whether I am meeting expectations or exceeding them—and that “exceeding” expectations is analogous to earning an A. Within the first week of class, students respond to structured prompts in a Microsoft Form that guide them to define what each grade (A, B, and C) “looks like” in practice, with the shared understanding that an F reflects not meeting course objectives or expectations at all. I then use AI to synthesize the 70+ responses into an “average” definition for each grade, which I compile into a shared set of grade definitions that are added to the syllabus. Although students’ descriptions of A-level work are often quite lofty, I intentionally do not revise their definitions. Once the grade definitions are shared, I work with students during the same week to establish deadlines for major assignments. I find that having them assess their responsibilities across various courses, and considering their group members’ schedules and needs, helps them take ownership and establishes a collaborative environment.

Throughout the semester, students complete “weekly learning logs” that prompt reflection on their course contributions, topics covered, evidence of learning, challenges, and potential solutions. They are invited to upload files that represent their learning process, and these logs help them detail their progress in the course. We frequently discuss how these logs, which I invite them to think of as journals, support ongoing reflection and accountability. At midterm, students write a narrative assessing their coursework and the letter grade they have earned to date, using the co-created grade definitions and evidence from their learning logs. This reflection builds confidence in both the self-assessment process and their own growth. The course concludes with a final reflection synthesizing the learning logs and midterm narrative, in which students assert their final transcript grade in relation to the shared grade definitions..

The revision process remains similar: I ask students to submit their “best draft” by the assignment deadlines, and we engage in written feedback cycles until students have confidently and competently exhibited mastery of course content. I will not pretend that my students’ desire for perfection has magically melted away as the result of self-assessment. This model of assessment comes with its own trials. For example, especially before the first TIP, students line up after class to ask what I’m looking for, or whether a particular idea “works” for the TIP. Even after receiving feedback on the first TIP, students often continue asking similar questions.

I personally view these questions as an opportunity to help reinforce their metacognitive growth; I share with them, both in class and in LMS announcements, that I love questions because it means they are open to learning! In addition to office hours and in-class discussions about self-assessment, I hold weekly “Coffee Chats” before our 8:00 am class. I invite students to attend and ask questions about the grading structure or anything related to our class and field. During that Coffee Chat time, I consistently reiterate that the goal of the assignments and the course is to build their understanding of foundational principles of clinical practice in speech-language pathology and audiology. I consistently acknowledge the stretching they may feel as they learn how to navigate the class. A recent student shared in her weekly learning log, “If you haven’t been made aware, the format of your class is very different than what we are used to, so it has been a little bit of an adjustment. But thank you for your kindness and patience!” The first few weeks of class require significant dedication on my end to help students feel comfortable with this new experience.

Practical Advice

The biggest challenge I have encountered is students’ initial discomfort. Their weekly learning logs reveal that I am asking them to do something extraordinarily different from their previous 16 years of schooling, so I address this uneasiness through consistent check-ins.

• Each of my slideshows for class include a slide that reminds me to check in with the students as a group.

• I send an announcement on our LMS after each class recapping the most important points and addressing any themes I see in the weekly learning logs, including those related to alternative assessment.

• Within the weekly learning logs, students answer the question, “Do you need Professor Vergara to check in regarding self-assessment?” They select “Not at this time,” “Yes, immediately,” or “Yes, no rush.”

• Also on the learning logs, students have an optional field in which to ask any question or comment on anything related to the class and/or major.

• I offer “Coffee Chats”: a consistent 20-minute casual open time before class one day each week per week. Students can come chat about the therapy profession, self-assessment, or life.

Besides addressing student questions that come with this shift, I’ve found that it is my job to build confidence in this new way of doing things. In each class, and often in my LMS announcements, I provide parallels between self-assessment and professional practice to build student buy-in. I also assure them, from past experience, that they will increase in confidence after experiencing the feedback loop on their first assignment.

I have moved forward with self-assessment across all of my courses in the name of building competent, critical thinkers who will become thoughtful clinicians. Although I initially explored alternative grading because I recognized my past self in my stressed-out students, my eyes were opened to how misaligned traditional grading can be for students preparing for allied healthcare careers. The real goals for these future clinicians include clinical judgment, reflection, and the ability to work with imperfect information. While students initially express some discomfort with alternative grading, with one student saying she felt like she had to unlearn everything she knew about being a student, the shift has been tremendously positive overall. I have seen students grow into more confident learners who are willing to make mistakes in a safe environment, all in the name of learning. This approach has opened new possibilities for both my students and my teaching, and I am excited to continue shaping my courses with these principles.

Higher education is very good at addition, but struggles with subtraction. The default of higher education and many of those who work within it is to add stuff on top of other stuff, whether or not the cost of addition outweighs the benefits, and to continue this semester after semester. We know how to reduce and how to say “no”, but it’s not what we were trained for and it’s not in the nature of the system.

But the older I get and the longer I spend as a professor, the more focus I have on radically simplifying everything that is around me. Over the last few years as I continue to learn how to grade alternatively, I have been specifically focusing on what I can take out of my practice rather than what new, shiny things I might put in.

Today I wanted to share six things that I have removed from my teaching practices that are related to alternative grading and explain the consequences – good, bad, and ambiguous – of doing so.

Having too many learning standards

I’ve mentioned in some earlier posts that my first attempt at specifications grading involved having over 60 different learning standards in my course. Those 60 standards covered every possible topic that I felt students could or should learn in the course. And also, obviously (to me, today) created an enormous and unending amount of grading for me and work for my students. I knew by around the third week of the semester that 60 standards was way, way too many.

So in the following semester, I was much pickier about my standards — not about the rigor of the standards or the thresholds for acceptable work on them, but about the standards themselves. I started saying no to a lot of topics in my class that I formerly felt were inviolable and must be assessed in order for the course to be “a real course” in the subject. Either those topics would get cut out of the class entirely, or they’d be included as interesting side quests but not to be assessed. For some topics that were so small they honestly didn’t merit their own assessment, I would either not assess them at all or glue them together with other similar standards to form one mega-standard that would be assessed1.

Over time, the number of learning standards in my courses has dropped from a completely insane 60 down into the 20s and eventually to 15 or 16. This is a nice round number because it’s the number of weeks in a typical US college semester. And if you have 15 learning standards, that works out to one per week on average. You can use this structure and this number of learning standards as a time-saving device when you are planning out the lessons on your course calendar: Simply target one week per standard.

This semester I’ve managed to cut my standards down to 12. I think 12 is even better than 15 because then you can line up your standards with a 12 week semester which I will write about below.

Cutting and consolidating this way does mean you are covering or assessing fewer things. This can cause problems for some faculty, so proceed with caution. The Chesterton’s Fence principle applies here: Don’t remove things until you understand why they were there in the first place.

Using multiple layers of assessment to certify mastery

As if it weren’t bad enough that I had 60 learning standards in that first specs-graded course, each standard required three successful quiz attempts — three “layers” of assessment — in order for me to certify if the student had actually learned the standard. This seemed like a genius idea… at the time. But several years ago I ended this practice, and now have a higher bar for “acceptable” work and a rule that a single “acceptable” attempt on a standard was enough.

There is value in requiring more than one successful assessment on a standard in order to certify the student has met the standard. It’s possible for a student simply to have a good day, or to get lucky, and demonstrate mastery once, but having them do it twice or possibly three times seemingly allows you to triangulate the position of the student with greater fidelity. I know that in the past when I’ve used multiple layers of assessment in this way, I’ve seen students master a topic in week 3 and then fail to demonstrate mastery in week 12, so the second layer prevented (or at least delayed) a false positive.

However, it’s undeniable that if you require n levels of assessment, it is going to multiply your workload roughly by a factor of n, and not just your workload but also students’. I have to believe that having an inflated workload cuts into the helpfulness of feedback. It’s just harder to give helpful feedback when you’re exhausted. And anyway, are we really sure that multiple levels of assessment improve the validity of the data that we get? It feels like this should be the case, but I have never seen an actual statistical analysis to prove that it does. Maybe, in fact, one layer of assessment is just as good as three. Even if one layer were only 80% as good as three, I would be willing to risk it and just do one, because I am going to be a better grader and professor if I am not being hounded by my own grading workload.

Flexible due dates on assignments

The conventional wisdom is that having flexible due dates is a sign that one truly cares about students. But having been publicly supportive of flexible deadlines in the past, even advocating the outright elimination of deadlines, I’ve changed my mind about this. (And I think I still care about students.)

The last time I had flexible deadlines was three years ago. If a student found it difficult to meet a deadline, they could just fill out a form that notified me that they were picking a different deadline, what that deadline was, and give their reasoning and anything else they needed for support. Sounds good, right?

Unfortunately this problem caused more problems than it solved. Some students used it merely to deal with procrastination.2 On the other hand, some had legitimate needs for flexibility but wouldn’t use the policy, for a variety of reasons.3

My students were asking for deadlines by the end of the semester. I think they were right. I found that, while students value having flexible deadlines, they worked better and learned better when there were fixed deadlines that were set reasonably and enforced fairly (along with reattempts for work that was submitted by the deadline but didn’t meet the standards). They are a stake in the ground on their calendars that gives them structure, and for my students at least, the structure was better than so much flexibility.4

Graded asynchronous work

As I’ve written extensively here, I’ve recently shifted to a model in my teaching where anything that is done outside of class is graded on completeness and effort only, getting only feedback and not a mark. This is basically ungrading on a small scale and is intended to get students to build their mastery of a concept through iteration with a feedback loop. But the “final” demonstration mastery is done in class through timed tests. These often replicate or are significantly based on the asynchronous work that students do, but it’s the tests that are graded, not the asynchronous work.5 (The “final” is in quotes because there are limited reattempts built in; see my syllabus for details.)

I have been and remain fairly critical of the concept of ungrading as a whole, but I readily admit that this shift has really been beneficial for me and my students. I originally did it to mitigate issues with generative AI, and I think it’s been successful in that. But it also motivates a nice shift towards a culture of self-regulated learning in my classes that I also like very much.

This approach has several upsides so far. First of all, to be perfectly honest, I have much less grading to do because fewer students are going to turn things in if there’s not a “real grade” on it. I don’t like this, but I won’t lie: I do like the lighter workload. Less facetiously, this kind of grading is more the sort of work that I want to do with my students. I am getting good faith, honest attempts from students who are motivated to learn from feedback loops, and I am giving those students helpful feedback and seeing the fruits of iteration with it.

The main downside here, as you might guess, is that a lot of students skip doing the ungraded work and just try their luck in the timed test. This often works out roughly how you would expect. There are re-attempts built into these tests, but they’re very limited because they take up time and space. So students almost by default tend to put themselves in bad situations and the students who are in the worst situations tend to be the ones who need reattempts the most.

Covering any content at all in the first week and last two weeks

One of my most popular posts from my old blog is called The 12 Week Plan. You can read the whole thing at the link, but here’s the gist: You design a 15-week course to fit entirely into 12 weeks, then use the extra time for initial onboarding in week 1, and two solid weeks of reassessment at the end. I have been a devotee of the 12-week plan for years now and saying “no” to Covering All The Things, especially in weeks 1 and 14-15, has been one of the best moves of my career for student success.

The upside of the 12-week plan is that you get these three weeks of time to do useful stuff. Students can learn the grading system in the first week of class and enter into the main body of the course confidently, and more importantly they are able to put your grading system into a background process and not obsess about it. And there is far less of an insane crush at the end of the semester to try to assess and reassess on topics, especially those that first appear late in the term. And let’s face it, by the end of the semester, you’re working with students who are in no shape to absorb new material. So you might as well just use that time profitably by giving students time and space to breathe and reattempt what they need.

The downside is that you lose three weeks of time to do course material. I don’t necessarily see this as a downside. But it can be hard, in some cases impossible to implement fully. One engineering course from a couple of years ago was so packed with topics that were deemed “essential” for subsequent courses that all I could manage was a “13.5-week rule”. But we work with what we’ve got, and even keeping just the last week blank on the calendar for reassessments might be a huge help for students.

Worrying about downstream academic performance

I get asked the following question a lot: Do students who have alternatively graded courses perform better or worse or the same as their counterparts in a traditionally graded course, in later courses? For example, how do students who have a specs graded Calculus 1 course do, grade-wise, in Calculus 2 compared to students who had a traditionally graded Calculus 1 course?

This isn’t a bad question, but I have never felt motivated to research the answer seriously because if I’m being perfectly honest, I just don’t care that much about it.

Let me explain: I care a lot about student success. Specifically, I care about the success of the students that I currently have in front of me nine hours a week, in the classes I am teaching this semester. As for the students I don’t yet have, or the grades of my current students in future courses, I somewhat care. But I also must admit I have no control over this. And I have a strict policy of not getting invested in things over which I have no control.

So instead, I’ve learned to trust myself and my ability to design courses, and trust my students to be able to learn well in the future once they have been given the tools. It is possible that because of the reductions and subtraction that I’ve mentioned above, that my students are missing some topics or haven’t been assessed as thoroughly on certain topics, as they could have, and that some of those topics might be the most important things in the world to the next professor up the chain. But the opposite is equally possible. Therefore, it’s just not worth the energy to think about it. That energy is better spent invested in the students that I have now, and the courses that I am teaching this semester, and remember that “sufficient for each day are the rigors thereof”.

Addition by subtraction

I’m a firm believer in the concept of addition by subtraction. We need to be careful not just to add things into our systems simply because they might provide some small value. The value that’s provided by the things we add, has to be balanced against the values of the things that we can no longer do because we’re adding.

Can every person reading this article do every single thing that I have mentioned here? Probably not, nor should they necessarily. But I do think it’s universally true that a simple system with a small number of moving parts is likely to be a better fit for you and for students than a complex machine that seems to cover it all and produce seemingly perfectly optimized results.

Acacia Ackles is an assistant professor of Computer Science at Lawrence University, a small liberal arts college and conservatory in Appleton, Wisconsin. She has been using alternative grading practices in her classroom since starting at Lawrence in 2022. She’s passionate about computer science pedagogy, theoretical computer science, ethics in computing, and a bit of evolutionary computing, as a treat. You can reach her at acacia.ackles@lawrence.edu.

It is likely that every student in your classroom has experienced trauma in some form.

Not every student has experienced the same trauma, but they all arrive in your classroom with baggage that they cannot simply set down at the door. Some may have experienced what psychologists sometimes call “big-T Trauma”, which are events we might typically think of as traumatic: Car accidents, war, natural disasters, or other direct threats to life. But many more have experienced “small-t trauma”, or the everyday stressors that pile up to create similar effects to those larger, singular events: long-term financial stress, marginalization and discrimination, or perhaps academic troubles or struggles.

For this reason, I have always strongly believed in trauma-informed pedagogy (TIP) practices. The previous link provides a good overview, but in brief, trauma-informed pedagogy asks instructors to recognize that the students in their classroom may have experienced trauma in the past, and respond by creating teaching practices and classroom relationships which actively foster a sense of safety and healing for students.

Here’s a concrete example. Cold-calling students in class is a common pedagogical practice, the purpose of which is often to solicit ideas that students may not immediately volunteer, or to check whether students are actively engaged. However, it can also provoke anxiety in students. For some students, this anxiety may be brief and quickly moved past; they get nervous when called upon, but they stumble their way through a response. For a student who has experienced trauma, the anxiety they feel getting cold-called may overload their nervous system, because it reminds their body of the anxiety and fear that they felt during that trauma. Now rather than experiencing a little anxiety, they’re in full-blown panic. They can’t answer your question, nor can they really even remember your question; they may not be able to regulate and refocus for the rest of class.

In this example, Trauma-informed pedagogy recognizes that some students may have an outsized reaction to the small anxieties that cold-calling can produce, and as a result, tries to lower the overall anxiety for all students by rethinking cold-calling as a teaching tool. Maybe instead of direct cold-calling, you ask a question, then tell students to think about it for a few moments. Then, you say that in a minute, you’ll call on someone randomly. This allows students to think and prepare themselves for the question, hopefully lowering the overall surprise and anxiety.

We can see with this example that TIP doesn’t just help students with trauma; it can help all students feel better and produce higher quality responses.

TIP practices dovetail nicely with the practice of grading for growth. In fact, the growth in grading for growth often means growing out of old neural pathways like those forged by traumatic experiences (both “big-T” and small-t”) that tell our students that failure or even the threat of failure is dangerous to their health and safety, for one reason or another.

As a new professor in 2022, I wanted to be conscious of TIP principles as I was developing a new course: Introduction to Programming with Python, a 100-level class for non-majors which fulfills a general education requirement in quantitative reasoning. I knew a lot of my students were coming to me scared of math and hoping they’d see less of it in a programming class. I also knew that many of them were only a year or two removed from the height of the COVID-19 pandemic, coming from homes that were unsafe for them, or coming into a world that felt unsafe for them.

I made a few deliberate choices when designing the course, influenced by both TIP and Grading for Growth practices. The three most salient were:

1. Flexible Deadlines, to allow for growth over the course of the term and an opportunity to make mistakes and try again. My approach to flexible deadlines has changed over the years for a number of reasons, but it was a guiding principle at the time and one that seemed pretty simple to implement.

2. Explicit Identification of “Failure” as a Learning Objective, to encourage students’ understanding that I might push them beyond their comfort zone when it came to academic work, and that I expected things to be difficult, so it was not their fault if they encountered difficulty. I used the word failure in my syllabus, but in hindsight I really meant faltering, or not immediately succeeding. (Maybe it’s a good time to reflect on my own assumption that those things were equivalent to failure.)

3. Language in the syllabus which acknowledged their humanity–recognizing that, at times, my class may not be the most important thing going on in their lives, and that I knew that, expected that, and would not hold that against them. An example, pulled from my sick policy: Your health and wellbeing as a human are more important than any university course, including mine.

Then, when I was slated to teach Intro to Python for the fourth time, I experienced a sudden and intense mental health crisis. And it all suddenly became a lot less abstract.

There’s not a good cohesive narrative to share here about point A to point B–I really did sort of wake up one day a lot less stable than I’d gone to sleep the day before. After muddling through a few strange weeks, I was pretty quickly diagnosed with a significant dissociative disorder which had reared its head only upon getting into the stability of a tenure-track job. Ironically, the reason I’d put so many TIP practices into my teaching was that I knew many of our students might experience a similar shift upon entering the new environment of undergraduate life–which brings us around to why these practices ended up helping me, the instructor.

I learned a lot about myself and my teaching practice that term (which included an extended hospitalization, a good deal of crying at work, and a lot of support from colleagues), but there are two things I want to highlight as it relates to alternative grading:

1. I had really underestimated how difficult it was to teach, and to learn, through trauma and even trauma recovery. I thought I knew, but I didn’t know. It is nearly impossible to retain any kind of information while your brain is in active fight-or-flight mode. I could not name for you a single student I taught that term. I’m not even sure where my notes are for that iteration of the course. However hard you imagine it may be for your students in this situation, it’s probably harder.

2. But crucially, because I had created a classroom culture of revision, mistakes, and trying again, I did not have to be perfect. I did not always have to return work on a strict timeline; late penalties were not as severe on the student, so I felt less pressure in the classroom to maintain rigidity. I was able to tell my students that sometimes (for example) I had forgotten to set up an assignment correctly, and that we needed to try it again, and that was okay. I had set up a culture where my students were allowed to make mistakes, so I was too.

I point this out because I really believe giving myself grace as an instructor like this was only possible because I was already implementing such grace for my students. Academia is so rarely forgiving–not just for our students, but for us as instructors.

It is a terribly difficult time to be a higher ed instructor right now. I imagine, as time goes on, we will all need some flexibility, grace, and patience. Doing the work of building a classroom culture of growth is deeply worth it not just for our students, but for ourselves.

I’ll leave you with only one piece of advice, one that I have difficulty implementing in my classes out of fear that it will seem out-of-place in a computer science classroom. However, as I gear up to teach Intro to Python one more time, I am challenging myself to implement this advice this term. It is not an original thought, but one I want to try here.

Take a deep breath together. Yes, even if you’re teaching math. Yes, even if you have a giant lecture hall (maybe especially if you have a giant lecture hall). There is something very powerful about all arriving mentally to the room at the same time.

Over the last few months, I’ve been exploring the connections between practice and grading systems. In my first post about this, I went into some depth about deliberate practice and how it connects to the feedback loops that we find at the heart of the Four Pillars framework. Then in a follow-up I unpacked a research paper that addressed the question of how to motivate students to practice. My takeaway in both articles is that alternative grading systems set students up to be motivated and to actually engage in deliberate practice on the things they are learning. So there is a mutually supportive relationship between deliberate practice and alternative grading.

But it turns out there’s more to deliberate practice than I was letting on in these two articles. And today I wanted to look at different kinds of practice, including but not limited to deliberate practice, and think about when those different kinds of practice are appropriate and how alternative grading connects to each.

Types of practice

“Practice”, generally speaking, just refers to repeatedly rehearsing a behavior. There are at least three ways that this can be done, depending on the details of the rehearsal and the goals of the person practicing.

Naive practice is practice where someone simply does an activity repeatedly, with the assumed belief that repetition leads to mastery but otherwise with no real trajectory, goals, or outside help in mind. Naive practice is characterized as being on autopilot versus being engaged and concentrating on improvement. Naive practice typically also lacks structure and leads to the “arrested development plateau”, where although repetition will lead to improvement for a time, at a certain point, no further repetition leads to any further improvement. In fact, one’s skills may deteriorate over time, even with further repetition.

Naive practice isn’t necessarily bad or wrong, it is just one way to practice a skill, and for some skills naive practice is appropriate. For example, if you are a driver learning how to commute to a new job, naive practice would involve simply driving that commute every single workday for as long as you’re in the job. At first you may struggle to find your way to your workplace, but with repetition – and repetition is all that’s needed – you will get good enough at it. There’s no real value in “mastering” your commute, so naive practice is just fine.

Purposeful practice, by contrast, involves more intention. Purposeful practice is distinguished from naive practice in that it has well-defined specific goals that break a complex skill down into manageable chunks. The person doing purposeful practice is fully engaged and concentrates hard to push toward those goals. Purposeful practice also involves immediate informative feedback on the performance toward those mini skills that lead to the higher goal, and a strong element of getting outside one’s comfort zone.

If simply repeating your commute over and over every day is naive practice for learning a commute, then purposeful practice would be like working through a specialized self-paced course to master a driving skill like parallel parking. There is a particular goal in mind (to be able to parallel park a car) broken down into manageable bite-sized pieces (for example being able to back into a space). Practicing those skills involves concentration, effort, discomfort, making mistakes, and learning from mistakes.

And that gets us to deliberate practice. Deliberate practice includes all of the hallmarks of purposeful practice, but adds two extra ingredients. First is the presence of expert guidance. Whereas purposeful practice is typically self-directed, deliberate practice in almost every instance requires the presence of an expert coach, teacher, or guide whose job is to design activities customized for each student and targeting the specific needs of those students. Secondly, and related to the first, deliberate practice is only really possible in mature fields of study where the pathways to mastery are well established, and can therefore be taught.

To continue the driving analogy, suppose that instead of a self-paced course in parallel parking, you are in a driver’s ed course that teaches parallel parking (among many other things). Here you have a driving instructor who can give you classroom instruction as well as individualized coaching as they sit in the passenger seat while you actually attempt a parallel park. And parallel parking is an activity that is well established enough to have what amounts to a single correct way to practice – even if the instructor does give you personalized lessons. In this case, you are performing deliberate practice, not just purposeful practice.

When does deliberate practice fail?

The differences between purposeful practice and deliberate practice are important, because they tell us that there are some fields in which deliberate practice may not be possible in a strict sense. And in some cases, not even all of the defining characteristics of purposeful practice can be met.

For example, consider the domain of entrepreneurship. Many universities have academic programs in entrepreneurship, but deliberate practice in being an entrepreneur is highly limited. Practicing being an entrepreneur involves starting a business, or simulating it. Many of the aspects of purposeful practice are available: One can set well-defined specific goals (“Write a business plan by the end of the month”; “Become profitable by the end of the year”) and break those down into small pieces. It requires engagement and concentration and certainly involves discomfort.

But getting immediate informative feedback on entrepreneurship is hard. The feedback that one gets as an entrepreneur is often informative but not immediate. You might not be able to get actionable feedback on your plans for making a business profitable until the end-of-year deadline has passed. Other times, feedback is immediate but not informative, for example when someone unfamiliar with the full scope of your business tries to give advice on large-scale matters. Additionally, the field of entrepreneurship is not mature in the sense that there is currently no established pathway towards being a good entrepreneur (despite the profusion of books that claim otherwise), nor is it a straightforward process to hire a good entrepreneurship teacher to give you customized exercises.

These systemic constraints on domains such as entrepreneurship place an upper limit on just how intense or intentional practice can be. In fields where immediate informative feedback is problematic, or in which expert guidance is scarce, or in which the domain itself is not mature enough to have standard approaches to mastery, purposeful practice is just about all we can hope for and deliberate practice may not be possible. This is partly why many people consider domains like entrepreneurship, but also including domains like leadership or even teaching itself, to be hard to master because they are hard to practice.

Practice and the Four Pillars

Deliberate practice is considered the gold standard, but is not necessarily the right goal in mind for all forms of instruction in higher education. It’s probably best to say that our aim in higher education should be purposeful practice at minimum and any portion of deliberate practice when possible. And this is where our grading systems in classes can come into play.

You probably heard echoes of the four pillars as I was describing purposeful and deliberate practice above. But here is how I see the connections between these forms of practice and the four pillars more explicitly.

• Having well-defined specific goals that break a complex skill down into manageable chunks is the very essence of the first pillar, Clearly Defined Standards.

• I’ve mentioned “immediate informative feedback” a couple of times already, and this is obviously related to pillar number two, Helpful Feedback. I’ve often framed the word “helpful” here as meaning feedback that invites students to continue participating in the feedback loop. But viewed from the lens of practice, “helpful” has more detailed connotations. First, it should be immediate, which I don’t take to mean instantaneous, but simply given to the learner while the experience is still fresh, and the fresher the better. And it should be informative, which means being honest and clear about what the learner/practicer is doing well and what is missing from mastery. The clarity of that feedback also appears, if indirectly, in the third pillar, Marks that Indicate Progress.

• And it goes without saying that the fourth pillar, reattempts without penalty, must be in place, otherwise we’re not practicing at all, because there was no repeated rehearsal of the behavior.

Other elements of purposeful and deliberate practice do not directly connect into the four pillars, but are addressed by them or are a combination of them. For example, purposeful practice involves getting outside one’s comfort zone. I phrased this earlier by saying that deliberate practice is often deeply uncomfortable – sometimes boring, exhausting, and not very fun.

In my experience, alternative grading definitely pushes students out of their comfort zones because the expectation is that you will not just earn enough points to pass the class, but actually master the content of the class through confronting your mistakes, learning from feedback on those, and repeatedly reattempting until the standard is met. This is a profoundly uncomfortable process. It’s much more comfortable to live in a world where if you do poorly on one quiz you just have to do extra well on the next, and the averages will take care of the rest. It’s the kind of discomfort that we sometimes call desirable difficulty: Discomfort that means real learning is taking place.

Also not directly addressed by the pillars, but baked into most of our contexts where we use alternative grading, are ourselves as the instructors. Students can engage in self-directed practice, which would by definition not be “deliberate”. Perhaps this is what many of our students do when they’re not in class. The way that instructors can elevate students’ practice from “just” purposeful, to deliberate is by inserting themselves into the picture.

This is the domain of class activities. I have mentioned before that when we talk about “clearly defined standards”, the word clear doesn’t refer simply to the language that’s being used, but the alignment of the standards to what happens in the classroom and then to what happens on assessments. Standards are truly clearly defined when they are stated simply and given as specific goals that are broken down into manageable chunks, but then also instantiated through active learning in the classroom1.

What kind of practice do we want?

Every grading system supports some kind of practice methodology. The only question is which kind.

Traditional grading – with an emphasis on points, percentages, and statistics instead of feedback loops – is optimized for naive practice. The hallmarks of purposeful and deliberate practice we described above may sometimes be present in traditional grading, but they are not the default, and traditional grading neither encourages nor supports any form of practice beyond the naive.

Alternative grading practices, on the other hand, set students up for success through high quality purposeful or deliberate practice. This is true whatever the particular flavor or combination of flavors one may be using, as long as they are based on the four pillars. These skills can be instantiated and learned so as to be useful across one’s whole lifetime.

Halley McCormick is a professor in the mathematics department at Walla Walla Community College (WWCC), a rural two-year college in eastern Washington State. She holds a master’s degree in mathematics from Western Washington University. Her professional interests include alternative grading (obviously!), statistics pedagogy, and active learning. When she’s not thinking about teaching and learning, she’s knitting or trying to keep her garden alive.

The challenges of communicating with students about alternative grading

Implementing alternative grading poses challenges, partly because such grading systems can be a significant departure from what students are used to. Getting student buy-in and understanding of an alternative grading system requires intention in setting the tone for a course’s learning community and in providing opportunities for students to practice using the grading system. This post will share some of the strategies I use to ensure that students are informed about and empowered by their course’s grading system.

Walla Walla Community College (WWCC) is a small rural two-year college located in eastern Washington State. Our college has four campuses, and our student body is split approximately equally between workforce education and transfer education.1 The math department, however, mainly serves students seeking to transfer to a four-year college after graduating from WWCC. We teach courses from the first two years of college mathematics. The courses in which I’ve implemented alternative grading include Math in Society (aimed at students who need a general math course for graduation), Introduction to Statistics, and the precalculus and calculus sequences.

Math classes are typically capped at 28 students. Within any given class, we encounter a wide range of ages and educational backgrounds. Two students sitting next to each other in class may have had wildly different experiences with math and with academia. While I do teach some asynchronous online classes, I have only implemented all four pillars of alternative grading in synchronous settings – mostly in face-to-face classes, but also in synchronous online classes.

In considering the challenges of getting students to buy into alternative grading, I’ve come to split those challenges into two categories: getting students to understand the motivation for alt-grading (the “why”) and getting them to understand the mechanics of alt-grading (the “how”). The more familiar students can be with those two aspects, the more comfortable they seem to be with the grading system.

Communicating the motivation for alt-grading

To help students understand the “why” of my courses’ grading systems, I’ve tried the following strategies:

Talking about my own experiences as a student. On the first day of class, to set the stage for motivating the alternative grading system, I talk about my time as a college student: in particular, I share frustrations I had with being graded traditionally and positive experiences I had when provided with meaningful revision opportunities. For example, I talk about a recurring assignment I had in a course during my first year of graduate school. Each week, my classmates and I were tasked with writing a short mathematical proof; our instructor’s feedback consisted of suggestions for improvement and a mark indicating either success or revision. Now, with my own students, I talk about how this reduced my stress, encouraged me to return to difficult tasks, and made clear the connection between my work and my grade – all outcomes I hope to support by grading alternatively. Of course, other instructors will have their own anecdotes to share. My anecdotes correspond to choices I’ve made in how I grade, and I try to convey that I am not trying to make their lives harder by grading in this way; instead, I’m trying to fix problems I’ve perceived in my own education.

Discussing students’ personal experiences of getting good at something. Within the first week of the term, I set aside class time for students to talk about what they’re good at and how they got good at it (this is an implementation of Robert’s least-hated icebreaker). I am then able to connect their response to the four pillars of alternative grading, and the discussion provides a touchstone for the rest of the term.

Relating the grading system to the science of learning. Within the first two weeks of the term, once students have gotten used to the flow of the course, I set aside class time to formalize some key ideas in cognitive neuroscience about effective learning: retrieval practice, spacing, interleaving, and metacognition. I have two goals: to give students a framework for how to learn, and to link how they learn to how they are graded. To address the first goal, we brainstorm independent study strategies that support each of the cognitive practices listed and how to implement them in the context of the course. To address the second goal, I am able to point toward the fact that the four pillars of alternative grading turn out to be great ways to reinforce those valuable cognitive practices. I’ve found that reassessment without penalty supports retrieval practice, spacing, and interleaving, and that all four pillars support metacognition. When discussing these ideas with students, I don’t talk about the four pillars by name, but I do talk about specific aspects of our course structure.

Students seem to have the least trouble identifying the cognitive value of reassessment without penalty – students readily note that reattempting assignments both allows and forces them to revisit difficult ideas at different points in time. They might not always like having to “redo” assignments, but they can see how the exercise is connected to learning. Marks that indicate progress are a tougher sell – students are used to numerical grades, and switching frameworks is demanding. I lean on the notion that getting information about their progress not only helps students think about their thinking but also helps them plan exactly how to improve.

Communicating the mechanics of alt-grading

To help students understand the “how” of my courses’ grading systems, I’ve tried the following strategies:

Distributing a “punch card”-style grade tracker on the first day of class. This is probably the most important tool I have for communicating clearly with students. I credit Grading for Growth, both the book and the blog, for providing many useful models of grade trackers that informed my choices. My grade tracker has two pages (printable on one sheet). The first page describes what students accomplish over the course of the term; the second page has them transfer what they’ve done to how much they’ve done to get them to each grade band. In particular, a past student coined the useful analogy that my grading system operates like a “punch card” – you have to meet all the criteria for a grade in order to earn it. I print the grade tracker on brightly colored paper and pass it out on the first day of class. Students keep it handy for the rest of the term. (In synchronous online classes, students print their own copies.)

Asking students what grade they want and how they can get that grade. This is another exercise I’ve lifted from Robert – a beginning-of-term assignment where students report their goal grade and the way to earn that grade. This requires them to think through the grading system and gives me an opportunity to catch some misconceptions.

Frequently updating grade trackers using the “student view” of our LMS. Before the first major assessment and after subsequent major assessments, students update their grade trackers during class. They have a chance to ask me and each other questions about how to track their progress. An important aspect of this involves me sharing the “student view” of our LMS gradebook so that students know (from their perspective) how to interpret the gradebook results in terms of the grade tracker.

Having students submit their grade trackers. Two or three times throughout the quarter, students submit a scan or photo of their grade tracker via an assignment in the LMS. This is also an opportunity for them to ask questions if they have them. In my feedback to the assignment, I answer questions but make it clear that I haven’t checked their grade tracker for accuracy.

Relating students’ current standing to action steps. Whenever the topic of grades comes up, I emphasize that knowing one’s current standing in the course is most useful for planning future action. After addressing the question of “What’s my grade?”, I follow up with a question of my own: “What do you have left to do?” The transparency of alternative grading lends itself to a clear itemization of next steps.

Reflection and ideas for the future

Over the last few terms, I’ve polled students about the challenges and benefits they see in alternative grading systems. Those with positive reactions tend to point to satisfaction with both the “why” and the “how” of the grading system, saying things like they appreciate the ability to focus on effective learning because of retakes (the “why”) and that once they got the hang of it, the grading system was understandable and satisfying (the “how”). A remarkable number of students comment on how fun it is to check off different requirements on the grade tracker!

Students also discuss persistent challenges with the grading system, including lingering confusion about what their current grade is, discomfort with its unfamiliarity, difficulty translating the information from the LMS gradebook into a course grade, and frustration that one “bad” category can tank their grade for the course since assignment scores are not averaged.

I will continue adjusting my grading practices to improve students’ understanding of what their grade is and what it means. Based on my experiences with communicating the motivation and mechanics of my alternative grading system, here are a few possibilities I’m considering implementing in the future.

• Meet with students to discuss their progress. I often meet with students at the beginning of the term to get to know them, but I’d like to add an opportunity to meet during the middle of the term, both as a general check-in and as a chance to explicitly discuss their standing in the course.

• Make videos on how to use the LMS to track progress. In class, I regularly show students how to use the LMS gradebook to track their progress, but making videos would afford students more autonomy and independence. It would also provide reference material that I could easily point to when students have questions about their grades.

• Brainstorm expansion to asynchronous online classes. Dependable two-way communication with students is harder in asynchronous settings; how can I adapt my strategies for such an environment? (Some inspiration: a Grading for Growth guest post on using the LMS to convey marks in an asynchronous alt-graded course.)

• “Do less, better.”2 This is another way of thinking about the Grading for Growth mantra to “Simplify, simplify, simplify.” I will be reminding myself to look for places where I can remove complexity3 to improve how I communicate with students.

Alternative grading can focus student attention on important ideas rather than on the accumulation of points for a grade. I hope that clarifying alternative grading for students will free up time and energy to do the fun stuff (math!).

Welcome to the first Grading for Growth post of 2026 (our 5th year!). I’ve had a lot of ideas bouncing around in my head over our semester break, but none of them were long enough to be a whole post on their own. So today I bring you a bunch of bite-sized thoughts about alternative grading in the new year.

Try Revisable

Revisable is quickly becoming my favorite mark that indicates progress. This mark is used when students have multiple opportunities to meet standards through new attempts, such as quizzes, exams, or problem sets. Revisable gives students a chance at a “mini-reassessment” rather than insisting that they make a wholly new attempt.

When one of my students earns Revisable, they can come to an office hour, explain the mistake in their work, and then give a full and correct solution. If they do this, the Revisable becomes Successful for free, no new attempt required.

The biggest advantage of Revisable is that it recognizes that sometimes students make small, important, yet easily fixable mistakes. The standard example I use to tell students about Revisable is a simple error that a student can quickly fix once they notice it, for example messing up a negative sign, or slightly misreading a problem. It can be disheartening to need to make a fully new attempt in such a situation, so Revisable gives students a quick way out of that. It can scratch the itch for “partial credit” while maintaining the high bar of meeting a clearly defined standard.

I was originally going to write an entire article about this, but then I realized that I’d almost done that already.

Take notes!

It’s the start of a new semester for many of us, so now is the time to start taking reflective notes about your new classes.

This really applies to all teaching, not just alternative grading. If you’re like me, you likely think that you’ll remember how well a class worked and what changes large or small you’d want to make in it. Also if you’re like me, you absolutely will not be able to remember all of those details a few weeks later, much less a semester or a year in the future.

This is why I always take notes about any class I’m teaching, while I’m teaching it, usually in a document called “Notes for next time”. Some notes are purely mechanical: on-the-ground edits to make to worksheets, changes to topic orders, and so on. But I also leave more reflective thoughts at the end of the semester, often addressed to “future Dave”.1 I leave notes during the semester, and also take a few minutes to summarize my overall observations at the end of the semester. Then I use those notes to make changes next time I’m teaching a class (and I try very hard to trust past Dave, who definitely knows what he’s talking about).

This reflection and iteration is immensely powerful. It’s helped me significantly improve my own teaching. Most immediately, the notes are a record of which changes are really needed, which is easy to forget a semester or a year later. The reflective practice also helps me see trends in my teaching and in my students, which helps me react to shifts rather than keeping my classes static and unchanging.

All human learning happens through a feedback loop, and that includes our learning and growth as teachers. It’s worth building that loop into your own practice.

Do what you can

Recently, I’ve had several conversations with people in extremely restrictive teaching circumstances. These include heavily coordinated classes, administrators requiring specific final grade averages or distributions, and more. These faculty asked “how can I use alternative grading when I’m so limited?”

Of course, it’s worth fighting against unnecessary constraints. But in the meantime, even little things can make a big difference. Both Robert and I have written about “small starts” for alternative grading – small things that work within existing contexts. Those are good places to start. But even some of those small starts are too big when there are extreme limitations.

In those cases, I think that the best things to do are those that make a connection with your students, to show them a bit of humanity in a difficult situation. Perhaps use tokens in whatever way is possible – allowing deadline extensions, permitting a revision (if you can do that), and so on. Find new ways to give helpful feedback, like using a video or audio tool, that might be more meaningful to students. Create clearly defined standards for yourself, but also share them with students. Use them to organize class time and exams (even if you aren’t able to grade based on them). The structure, clarity, and flexibility provided by these items can help students focus on learning even if your grading system is fixed in place.

We’re hoping to have some more guest posts about these kinds of situations soon. If you’re in such a situation and have found ways to use alternative grading, let us know!

Keep simplifying

I know, we talk about simplifying a lot on this blog, so much so that we call it the “Prime Directive” of alternative grading. What amazes me is that every time I think I’ve got a grading system as simplified and streamlined as I want, I always end up discovering more ways to improve it.

Next semester, I’m teaching Calculus 2 again – this is a class that I taught a year ago, for the first time in 6 years. A few weeks ago, when I pulled out last year’s syllabus and read it with fresh eyes, I found even more ways to remove and simplify standards, streamline the homework system, and generally make things simpler and easier for students while keeping the core elements of assessment in place.2

The same happened when I was taking notes about the Communicating in Mathematics class that I taught last semester (and which I wrote about over the summer). After teaching the class, which I’ve constantly revised and improved over many years, I was yet again able to identify a few places to simplify. In this case, I identified a few standards that were left over from a previous approach to assessment. They don’t lend themselves to good quiz questions in my current approach, which is why I decided to remove them. They had hung around for several semesters beyond their “best by” date. Next time, they’ll be gone.

So, there’s always more you can do! Even if your grading system works well for you, it’s also likely that your students are changing in ways big and small. Trying new approaches is an important way to meet your students where they’re at. Speaking of which…

Share what you’ve tried

If you’ve tried alternative grading in any form, big or small, please share! Have you found something that works particularly well in a certain class, with certain students, in a certain context? Colleagues will benefit from your ideas, your syllabus, your successes and even your failures.

You might think that what you tried was very small, or maybe it didn’t work. Even so, I guarantee that those ideas will be helpful to others. Some ways to share what you’ve done include:

• Talk directly with colleagues.

• Create a reading or discussion group through your teaching and learning center.

• Contribute your syllabus to a repository (find a list on the Center for Grading Reform’s Resources page).

• Present in a session at the Grading Conference.

• Propose a guest post for this blog! (We love all kinds of posts about alternative grading, including “what went wrong and what I’ll try next time” reflections.)

• Write an article for a disciplinary journal that’s focused on the Scholarship of Teaching and Learning.

There are lots more ideas in my recent post on ways to find and build community.

Be kind to each other

Finally: Alternative grading is an extremely broad umbrella. It covers everything from small changes in one pillar, up to wholesale reimagining of what grading can look like. Alternative graders have a huge variety of motivations and beliefs, from an itch to improve their own classes in small ways, through big-picture philosophical desire to burn it all down. Alternative graders teach in a wide range of contexts, classes, disciplines, students, and more, all of which impose limits and create opportunities.

On this blog, we intentionally feature the widest range of voices we can, so that we can showcase this big umbrella. This breadth in our community is a strength: It guarantees that we are constantly generating new ideas, critically examining old ones, and combining them in new and innovative ways.

It can also create friction as we meet people in different contexts and with different motivations, especially those that don’t seem to match our own.

So as we go into a new year, I encourage all of us to be kind to each other. Welcome new people into the community. Write, speak, and act from a place of genuine curiosity and care. Understand that those in different contexts have different priorities, and what works for you might not work for them. Remember that we are a big and ever-growing community. Let’s meet each other where we are and have fun talking about it.

Thanks for reading. Next week we’ll be back with a new guest post about how to communicate effectively with students about alternative grading.

We hope that your holiday season has been full of joy and rest. This week we are taking a break from regular content to partake in some of that joy and rest ourselves, and since Monday falls almost exactly halfway between Christmas and New Years Day this year, this week we present a year-end retrospective and a look ahead to 2026.

It was an eventful year for higher education and for alternative grading. Let’s look at three trends we saw here at Grading For Growth.

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AI enters the chat

Generative AI went from being a technology on the edge of the radar screen for many instructors, to an unavoidable fact of academic life in 2025. That was reflected in three of the most-read posts at the blog this year:

Robert wrote about grappling with how to redesign assessment and grading in his Discrete Structures class, based on AI issues in a late 2024 class, at this post:

After the course was done, he wrote this postmortem:

David wrote about his response to AI and how he’s changed his assessment and grading, in this post:

It’s safe to say that the conversation about artificial intelligence in higher education is far from over. We believe alternative grading has much to contribute here, and you can expect more analysis from us and our guest authors in 2026.

Our guest authors continued to be awesome

Speaking of guest authors: We started featuring posts from guest authors twice a month in 2024. The fact that we’ve managed to continue that pace of guest authorship is a testament both to how widespread the use of alternative grading has become and how generous its users have been in sharing their experiences. Here are three of the most-read guest posts from 2025:

Sarah Hanusch wrote this article about four issues with giving feedback to students and what to do about them:

Jordan Freitas shared a useful framework for thinking about the psychology of giving and receiving grades, the “Drama Triangle”:

And in a first for this blog, we featured a student guest author: Lance Markowitz, an undergraduate at Oakland University, shared what it’s like as a student to experience an alternatively graded course:

We have guest authors on the schedule through July 2026, but we are looking for new articles for the second half of the year. If you’re using alternative grading in your classes (or if you’re a student experiencing it) and you’d like to share your successes, failures, questions, and anything else — don’t be shy! Use the Guest Author Interest form to give us your “pitch” and we’ll get back to you soon.

Share your idea for a guest post!

Back to basics

Interest in alternative grading seems to be picking up momentum. To aid in understanding what alt-grading is all about, we wrote a few articles in 2025 that addressed some of its foundational ideas. Some of these were among the most widely read articles of the year.

David did a thorough study of one of our most fundamental examples of the flaws in traditional grading: The story of Alice and Bob.

We have written lots about The Four Pillars framework for alternative grading, but never an article devoted to it. Robert fixed that here:

Finally, David addressed some of the most common “knee jerk reactions” to alternative grading here:

The year that was, and the year ahead

In 2025, Grading For Growth enjoyed a 29.8% increase in subscribers and a 21.6% increase in overall views compared to this time in 2024. That’s over 1600 new subscribers!

We appreciate every one of you reading the blog. We know attention is a scarce resource, and it means the world to us that you are spending some of yours here.

This blog is and will remain free — we make no money from it. If you’d like to support us, you could purchase a copy of the Grading for Growth book and start a book club (or convince your college’s teaching/learning center) to read and discuss it!

Robert was recently asked in an interview if higher education was on the cusp of a revolution in how grading was done. His response was No — we’re not “on the cusp”, we are well over the edge and the revolution is currently underway, through the everyday actions of instructors in the classroom making alternative grading happen. We fully expect this revolution to pick up considerable steam in 2026, as alt-grading seems to be at the intersection of many disruptive forces currently shaping education. And we’ll be here for it, with posts every Monday without fail. We look forward to the ride and having you along for it.

Having clearly defined standards is the first of the Four Pillars of Alternative Grading, and in my view it’s the “floor” on which the other pillars stand. We’ve written a lot about standards (and standards based grading, and how standards are different from specifications, etc.) here and in the book. But I’m preparing for a workshop I’m giving next month on writing standards, and it’s highlighted some new perspectives on this process that I’ve gained since I wrote this article on “How to write standards” almost four years ago.

Today’s article is both a beta version of that workshop and something that I hope is useful to everyone, as we think about setting up our courses for the upcoming semester.

First, a review

Let’s recap a few things that have been said here before, starting with the definition of a “standard”.

A standard is a clear and observable description of an action that a student can take to demonstrate their learning of some specific topic. Students take our classes (presumably) because they want to learn things. The way we instructors tell whether or not this has happened is to have students do something that produces evidence of learning, which we then evaluate (and sometimes grade). A standard is just a clear description of what that action looks like1.

I like to think of each assessment we give, as a mini scientific experiment in which the null hypothesis is, “The student didn’t learn the topic(s) that the assessment addresses”. The purpose of the assessment is to gather enough data to determine whether to reject that null hypothesis. A standard is the specific operational definition we use in the experiment, for some topic on that assessment.

In my original post on writing standards, I go into some depth on what each of the words (like “clear” and “action”) in the formulation above means. But there is one change that I’ve made: Replacing the word “measurable” with “observable”. Not every item of evidence of learning can be easily measured, or even measured at all in any statistically meaningful way. But in any case a standard refers to an evidence-producing action whose outcome can be observed. We insist on this, because feedback loops — the very heart of alternative grading — are predicated on observations of the outcomes of actions2.

That’s what I mean by a “standard”, but I should also mention what I don’t mean. A high-level learning outcome meant to apply to an entire course, is not (necessarily) what we’re calling a standard. Nor is it a micro-level topic or idea that you might encounter one day in class, but which is never assessed on its own. In past discussions of standards we have called the former course-level objectives and the latter lesson-level objectives. What we mean by “standards” lives in the middle of these — learning outcomes that are high-enough level to capture student learning on important concepts or groups of concepts, yet low-enough level to be specific and observable. These are the outcomes we actually assess, so we called them assessment-level objectives.

So when we talk about “clearly defined standards”, we are not talking about course-wide objectives in a syllabus, such as “Students will practice effective communication” in the syllabus for an English class. This objective is fine as an aspiration, but it’s too vague to be a standard. Nor do we mean something at the atomic scale like “Students will use semicolons properly”, because while this is a good thing to know (and which aligns with the above course-level objective), it’s way too small to merit its own assessment. An assessment-level objective that addresses the former while including the latter might be: Students will write sentences that use correct grammar and punctuation.

A workflow for writing standards

So how do we actually write clear standards? Again, I wrote an entire article about this almost four years ago. But as I was preparing for my workshop, I came up with this flowchart:

This process outlines the exercise I’m having faculty do at this workshop, and it’s my own practice as well. I invite you to test-drive it as you are building your courses for next semester.

The goal here is to craft a list of standards (assessment-level objectives) for a single instructional unit — a lesson, module, week, whatever makes sense for you. So the first row is clear enough: Pick which course and which unit you want to work on. Then, go through that unit and write down an exhaustive list of every idea and topic students will encounter in it, phrased as a learning outcome in a complete sentence (so, not just “Semicolons” but something like “I can use semicolons in a sentence” or “I can explain when to use and when not to use a semicolon”) and don’t worry about how micro-scale it might seem. This might result in a long list. Don’t worry: The process we’re about to walk through is intended to cut that list down to size. If you’re doing this for a whole course, you’d loop through this process for each unit.

Is it irreducible?

The first question to ask of your list of lesson-level objectives is: Is the list irreducible? What I mean by that is: Is the list as short as it can possibly be? Or, are there items on the list that can either be cut, because they don’t need to be included at all; or which can be consolidated with other items, because the item is too small to merit assessing it on its own? If not, i.e. you can cut or consolidate, then reduce. Otherwise, move on.

For example, your initial list might contain the three items (among others):

• I can use semicolons correctly

• I can use apostrophes correctly

• I can explain the history of the semicolon3

If this were my class, I definitely wouldn’t want to have one battery of assessments (an initial one plus reassessments) on semicolons and another on apostrophes. Taken individually, they just don’t rise to the level of importance to merit their own assessments, and it will create a lot more grading than I want. It makes more sense to take all such outcomes about punctuation and glue them together into a single outcome, something like I can use punctuation correctly, and then have a single assessment on that4. That’s consolidation, and it’s likely you will do a lot of this during this process.

As for the history of the semicolon, well, I might find that to be a fascinating subject but I would have to ask myself: Is it really essential for this course, especially based on the course-level objectives? If the course-level objectives spell out (haha) that the history of punctuation marks is a key learning outcome, then leave it in. Otherwise (for example, most introductory writing courses) it’s probably not relevant, despite how cool the topic may or may not be. So cut it if that’s the case. You will likely find yourself doing a lot of that, too.

Coming out of that diamond, you have a list that has been tailored. Now you have to approach it as a human being and ask: If I were to give assessments, each of which targets one item on the list, and each of which might need repeated attempts: Can I handle the workload of creating and grading all those assessents along with everything else? If you honestly do, then proceed. Otherwise, go back and try to cut/consolidate some more5.

These two diamonds are the hardest part of writing standards because they require honesty, and honesty requires courage. Experience feeds both. The first time I tried alternative grading, I had neither. I came up with a list of standards that was 63 items long, each of which required three successful assessments until Mastery was achieved. And I had 60 students. I did not cut or consolidate, because I was certain that every item, no matter how tiny, was of the utmost importance and needed its own assessments. And because it was this time of the year (mid-December) I was rested and removed from the pressures of the semester and so I thought “No problem, I’ll be able to handle all that grading.” The results were pretty much what you would expect. Had I simply been honest with myself about all this, I might have seen the sun once or twice in that semester, rather than be stuck grading day and night.

So I implore you: Be honest with yourself about what truly matters in your course and ruthlessly simplify your lists. And be honest with yourself about what you can and cannot do as a whole person in terms of carrying out the work of grading these standards. Simplify, then simplify some more.

Is it clear?

Next: Is the wording easy for a student to understand? What this means is: If the student knows all the terminology in your standard at an acceptable level, will they know what to do to demonstrate their knowledge, just by reading the standard?

Obviously, students have to learn the terminology in the standard. If a student doesn’t know what a semicolon is, they can hardly be expected to demonstrate they can use one. But assuming they do know, the rest of the action should be easy to parse: I can use a semicolon correctly in a sentence, as opposed to something like I can employ proper usage of a semicolon which says the same thing but the language could be simpler, or I am totally good with using a semicolon which (in addition to being cringey) doesn’t really specify an action to perform. What will a student do to show you that they are “totally good” with it? The answer to that question is the real standard.

Sometimes knowledge of terminology is itself the standard. For example a standard in one of my classes is: I can determine if a relation is reflexive, symmetric, transitive, or antisymmetric. The term “relation” is something students will have learned previously in class, but the other four technical terms are the subject of this standard. Here, we have to look inward to decide what we want from students. In this case, I want students to show me understanding of these four terms. But what does that “understanding” look like? I could have said, I can state the definitions of… those terms and that wouldn’t be out of line. But in my classes, I want more than just memorization, so I ask my students not to recite the definition but apply it to a concrete situation.

And how will a student know how to do this? I think the key is practicing the standard in class. On the day that we discuss these four properties, the class meeting revolves around reviewing the definitions and then spending the middle ½ to ⅔ of the class with students working together to “determine” whether various specific relations have those properties. In other words, we’re using the class meeting to engage in deliberate practice, and the practice session shows the students what the standard means6.

The next diamond (Is it phrased using an action verb?) is just a byproduct of the above. A standard, as we’ve seen, is an evidence-producing action. What action is it? You determine this, and then make sure that action appears in the standard.

Does it pass a final reality check?

The last filter or diamond I have in the flow chart is: Is the outcome relevant? Now that my objective is optimized and clear — does it matter?

If you feel like we’ve asked this question already, you’re right. In the first two diamonds, we were given an opportunity to engage in addition by subtraction — removing the inessentials, combining tiny objectives into one Voltron-like objective that could be assessed without micromanaging, and further simplification if the list was still too long for us to commit to. But sometimes the need for simplification isn’t fully evident until we actually clarify the standards. By going through the process of clarifying a standard, we may discover it’s not really that important after all.

So this final diamond in the chart, is us asking ourselves: Are we sure we’ve simplified as much as possible? You have until the first day of classes to make changes to this list of standards. After that point, every item on that list is a commitment you are making to assess that item, possibly multiple times, multiplied by the number of students you have, multiplied by the number of minutes it will take to grade each attempt. This is your last chance to be fully honest with yourself about that list.

I would encourage you to subtract as much as possible from your course until what you have left standing is a minimalist monument to the essential ideas of whatever it is you’re teaching. Like a stack of Jenga blocks near the end of the game, it’s still standing, but taking one more block out will cause a collapse. We can be really bad at estimating what’s actually essential. So I would encourage you now, before the semester starts, to just remove as much stuff as possible and think about how the course would play out. You might be surprised at what you can get away with.

Conclusion

There’s no science to any of this. It’s a craft, and it’s the result of we instructors engaging in the very feedback loops we want students to encounter — an iterative trial and error process to arrive at some version of this flowchart that really works for us individuals on a semester to semester basis.

I’d love to hear some of your variations or corrections to this process in the comments!

You probably know what issues I’m thinking about just from this post’s title: AI is unavoidable right now. Generative AI tools are freely available to anyone with an internet connection. In that context, what kinds of assessments can I use to understand student learning?

I have not found any obvious misuse of AI in my classes, but I’ve certainly had suspicions and I’m aware that it could easily be happening. As a result — and like nearly every instructor I talk to — I’ve been thinking about how to improve my assessments to better capture what my students actually know.

This semester, I made a few small but impactful changes in one of my classes that were meant to address AI use. I don’t claim to have all of the answers to how to manage AI and assessments, but I do think that these small changes were helpful. Let’s look at what I did, and how it went.

About my class, and what I didn’t change

In this post I’m going to focus on just one class that I taught last semester: MTH 210, Communication in Mathematics, which is an introduction to proof writing course. I wrote about this course at length this past summer (part 1 and part 2). I’ve taught it a dozen or more times over the past decade. This class comes early in the math major and is typically taken by first- and second–year students. Class topics include both learning individual mathematical skills (tested on weekly quizzes) and synthesizing those skills in written form (assessed through a proof portfolio). Along the way, the class has a significant emphasis on learning about disciplinary culture, writing standards, and tools.

We constantly put collaboration into action during class time, with teams of students working together at tables or whiteboards to investigate problems and draft mathematical proofs. All of this is unchanged from previous semesters, so for more details, see those posts from the summer.

Since about Fall 2023, including last semester, I’ve included an AI statement in my MTH 210 syllabus. Here it is, unchanged this semester:1

Generative AI (e.g. ChatGPT): In this course, we’ll be developing skills that are important to practice on your own. Using generative AI can inhibit the development of those skills – even if you just use it for hints or suggestions. So, please refrain from employing AI tools in this course. Using such tools for any purposes, or attempting to represent AI-generated work as your own, will violate our academic integrity policy. (Side note: ChatGPT and similar LLMs are good at writing nice-sounding but logically incorrect proofs that don’t follow our communication specifications at all. Don’t waste your time.)

In short: Don’t use it, particularly because it can hurt your learning. I also spent multiple short blocks throughout the semester talking with students about my own take on AI and why I included that syllabus statement. I’ll return to what students said about AI use a bit later.

What I changed

In MTH 210, I’ve always used short weekly quizzes that test skill-based standards. Students need to earn a Successful mark twice on each standard. Long ago, I gave these quizzes during 15-20 minutes of class, along with two or three longer in-class exams. During the Covid years, I changed to entirely out-of-class quizzes with long deadlines (e.g. 24 hours). This included the final exam, which was just one more chance to meet each standard. This asynchronous approach was very convenient: it opened up extra class time for learning, and also reduced time pressure on students.

Nonetheless, fully out-of-class quizzes were tempting places for students to misuse resources that would muddy the waters of the assessment, including (but not limited to) AI. But I didn’t want to move all of those quizzes in-class, both because of the test anxiety it would provoke, and because that would be a lot of class time!

Instead, this semester, I moved just a few carefully selected quizzes in-class. Specifically, I moved three “big quizzes”, covering more than the usual number of standards, into class time. These were basically short exams, spaced throughout the semester. They were a chance to earn Successful on a bunch of standards, no different from other quizzes except for their length. I also moved the final exam back to an in-person format. I had previously resisted this since the final would have been the only timed in-class assessment, and that felt unfair. Now with other assessments already taking an in-class format, it was no problem to move the final in-class as well.

All other weekly quizzes remained asynchronous. They could be completed any time between the end of class and the following morning. These quizzes were very short, each about one page long and covering at most two standards.

More importantly, I rearranged the order of the quizzes. Now, each standard appeared only once on the smaller out-of-class assessments. All other attempts on each standard appeared on the larger in-class “big quizzes”. This (generally) ensured that students had to earn at least one of the two required Successfuls on an in-class assessment. If you want to see what this looks like in practice, here’s the Quiz Learning Target Plan that I initially made for myself, and then shared with students to help them plan.2

While not new, the final exam also included a required “recertification” of some “core” standards. All students had to attempt these six most critical standards one more time on the final, whether or not they’ve already earned two Successfuls. The number of Successfuls on this recertification adjusted their final grade with a + or -, with a generous range leading to no grade change. This was one last chance, in an in-person environment, to check in on student understanding of the most important topics.

By only moving three regular quizzes in-class, I didn’t have to find as much extra class time as I might have otherwise done. In the end, I pinched and squeezed just a few parts of two multi-day topics, and removed one block of unstructured in-class work time. That’s all I needed to do – it would have been much harder to move all of the quizzes in-class.

Other assessments remained unchanged, including a proof portfolio. Proofs are long-form written mathematical arguments, and we spent a lot of time in class practicing with these. The actual work of drafting and revising these “portfolio problems” happened entirely outside of class.

While I know that writing is a magnet for LLMs, I wasn’t worried in the case of these portfolio problems. In part, this is because it is simply infeasible to require students to write detailed logical arguments in a high-stakes in-class timed testing environment. That would assess all of the wrong skills.

But there’s more beyond that as well. I heavily emphasized a rough-draft thinking approach to proof-writing that includes, among other things, a handwritten rough draft of each proof that is graded only for completion. I gave detailed feedback and might ask for a resubmission if the logic needs work. As a result, a student moving into formally writing a proof already had – and knew they had – a workable outline of a proof ready to be written. When it comes to formal writing, we used discipline-specific writing tools and discipline-specific guidelines about what “professional writing” should involve, and we practiced with both of those in class. These discipline-specific requirements added another layer of difficulty in the AI front that, in my experiments, made AI use much less practical. It also seems (see below) that students found significant value in this writing and revision process. For these reasons, I didn’t change the proof portfolio.

What I noticed, and what students said

Overall, I was pretty happy with these changes.

You will not be surprised to hear that students found the in-class exams more stressful.3 I hate proctoring in-person exams, not least because of the feeling that I’m inflicting unnecessary stress on students. This experiment with in-class assessment reminded me forcibly of why I’d gotten rid of them in the first place.4

However, students did about as well on the in-person “big quizzes” as they did on asynchronous quizzes. From reading student work and talking with them, both types of quizzes seemed to give a solid representation of what they actually understood. Some of that undoubtedly came from the fact that the “big quizzes” were usually second attempts: By the time they came to a big quiz, students were farther along their learning trajectory than they were on the initial asynchronous quizzes. This was part of my intent in arranging the quizzes as I did. In an alternatively graded class, all assessments are essentially formative, and I encourage students to keep learning as part of the assessment process. It’s also possible that students saw that a quick Successful with AI help (out of class) wouldn’t help them when they had to attempt the standard a second time in class.

Overall, this seems like a clear win, especially given that I only moved three quizzes (and the final) to be in-person out of 15 total assessments.

The out-of-class written portfolios were similar to past years as well, with all of the same difficulties and successes that I’ve come to expect in the context of this class.

Now let’s hear a bit more from students. At the end of the last two semesters, I’ve given students a totally anonymous AI survey. It asks a few simple questions, the key ones being: How often did you use AI in this class? What for? I had a nicely high response rate (91% across two semesters), and that plus the anonymity suggests that the surveys provided honest student feedback that is reasonably representative.

You might not be surprised to learn that students were using AI in my classes. But not nearly as many as you might expect, and perhaps not in the ways you expect. Across all of the semesters that I surveyed, less than half of my students reported using AI in any way, and of those who did, a large plurality chose “once or twice”.

What I found more interesting is what students actually said about how they used AI. Far and away the most common use case was as a fancy search engine. Many students said that they asked ChatGPT how to type a specific symbol in LaTeX.5 While I think that this is, at best, a mediocre use of an LLM (it’s truly no better than a standard web search), it’s not something that worries me when it comes to student learning.

A sizable number of students also reported asking for general overviews of a topic. Note that this wasn’t “how do I solve this problem?” or “how do I write this proof?”, rather, this came from a desire to understand the topic more clearly. Again, this might not be a great use of AIs, and I worry about subtle errors they might have internalized, but that’s no worse than many of the Youtube videos out there.

The third interesting use case was creating practice questions for quizzes. Students reported asking ChatGPT to quiz them on a topic. I had emphasized the value of self-quizzing and even worked with students to create flash cards, so this wasn’t too surprising. However, I do wonder if this may have led to one issue that I noticed: I regularly asked students to state specific definitions on quizzes. Definitions are at the heart of learning new mathematical ideas, and they are something we practice with in class. More than in previous years, I noticed students giving imprecise or slightly “off” answers when asked to state definitions. While definitions are fairly standardized, they aren’t identical from source to source, and publicly accessible LLMs would certainly not know our specific version of each definition. Perhaps this aspect of studying got missed when students relied on an AI that didn’t know our definitions — or maybe I’m imagining trends where there are none.

Finally, many students drew a clear line between using AI for (as one phrased it) “learning” rather than “doing the work”. In other words, they distinguished between trying to understand a topic, and showing their understanding of that topic. I don’t think that distinction is nearly so clear as students might think, but I did find those students’ intentions to be interesting, and it has given me some ideas about how to talk about AI use in the future.

After all of that, here’s the most important thing that I took away from these surveys: Students are not all of one mind about AI. If you read many of the articles written about college students and AI, you’d be excused for thinking that we live in a free-for-all of AI-based cheating. Another question on my survey asked students what they thought a reasonable AI policy would be for their class. Their answers were wide-ranging, but a substantial number gave ethical, moral, and environmental arguments against using AI at all. Others emphasized that the extra work involved in fact-checking an LLM’s response wasn’t worth the ease of asking it questions. Then again, others sang the praises of their ability to create high-level overviews, unlimited practice questions, or use an AI like a natural-language search engine.

What I’m doing next

I’m definitely keeping this approach to quizzes, and applying it to my other classes. AI or no AI, I think this approach to quizzing struck a good balance of flexibility and practicality. These small changes still required careful planning and rearranging some topics, but they didn’t require blowing up my whole course plan.

To help make this approach last for the long term, I’m simplifying my list of standards even further. I took careful notes throughout this semester, and I identified 5 (out of 18) standards that could get cut or merged into others. This will let me keep quizzes shorter, especially the in-class ones. It will also give me the wiggle room I need to ensure every standard has only one out-of-class attempt.

Looking farther ahead, I need to revamp my syllabus statement about AI use. As you might have noticed, among other things it’s essentially unenforceable. But I also strongly believe in the central idea that inspired it: That learning is a deeply human activity, it’s something that takes time and effort, and that AI can dangerously short-circuit that kind of learning. I will likely create something that includes more specific “dos” and “do nots”, including examples of appropriate and inappropriate prompts or uses. I’m also going to focus more on providing good study materials and explicitly addressing productive study strategies, so that students encounter good ways to study that have a closer connection to our actual course content.

This definitely isn’t the end of the story. But this is the end of the post. So before I sign off, I’d like to leave you with two key thoughts:

First: It’s possible to make small changes that address AI use on assessments. You don’t have to blow up everything and start fresh.

Second: Our students are not monolithic in their views on AI. Their views are as complex as the rest of the world, and we need to keep that in mind when considering our own choices.

This is our final exam week, so we are taking a short break to tie things up. I’ll be back next week with a reflection on how I’ve handled AI use in my classes this semester. (Brief spoiler: It’s complicated, but then again, so are students. Stay tuned!)

Until then, here are some recent guest posts you might have missed in the rush of the semester. Enjoy!

Finally, if you’re thinking about how to use alternative grading in your own classes, here’s one of my own recent posts about how to find and build a supportive community of practice:

See you next week!

Rebecca (Becky) Swanson is a University Distinguished Teaching Professor at the Colorado School of Mines, where she teaches a variety of courses and engages in scholarship of teaching and learning projects. She is a co-founder and co-advisor of the Mines Society for Women in Mathematics, a local chapter of the Association for Women in Mathematics. Becky enjoys reading, hiking, doing crossword puzzles, baking, crafting, and participating in bar trivia - when she isn’t busy with her daughters, Ella and Anna, whom she shares with her fellow mathematician and partner, Steve Pankavich.

Lucas E Quintero F is a teaching postdoctoral fellow in the Applied Maths & Stats department at the Colorado School of Mines. New to teaching pedagogies, Lucas has found a deep interest in alternative grading and novel methods of engaging with students. Lucas spends his time reading and enjoying the Mountains with his wife, two kids, and dog Milo.

After implementing standards-based grading in Linear Algebra at the Colorado School of Mines (which we refer to as “Mines”), we wanted to know how well it would scale to a multi-section, coordinated Calculus course . Mines is a public engineering school with an undergraduate student body of about 6000. Every student must take the Calculus sequence, which means we are offering a lot of it! In the fall of 2024, Becky became the Calculus I coordinator and thought the time was ripe to implement a similar grading system on a larger scale. That fall, around 600 students were enrolled in the course in sections of at most 40, taught by five different instructors. It was a challenge to find examples of large-scale alternative grading that didn’t rely on computerized testing, so we forged our own path, and wanted to share about lessons we’ve learned.

From (uncoordinated) Linear Algebra to (coordinated) Calculus I

Becky worked with a previous teaching postdoc, Aram Bingham, to develop a mastery-based testing model for Linear Algebra. They developed a list of learning outcomes, and students had multiple opportunities to demonstrate mastery or proficiency on these by completing problems on biweekly exams. Three possible marks were given, P for Proficient, MR for Minor Revision (completed online outside of class), and a N for Not Yet to indicate that the student needed to attempt a problem on an upcoming assessment. If the student was given a Minor Revision mark, a successful revision would move that outcome’s mark to Proficient. The exam portion of the course grade, which counted for 70% of the grade, was determined by the proportion of outcomes for which a student attained a mastery mark at some point in the semester1. We liked the simplicity of th2e system and the ease at which it could replace the exam portion of a grade, leaving other components of the grading structure intact. The model was successful and we created a webpage resource and published a pedagogical paper.

Unlike Linear Algebra, however, Calculus I is centrally coordinated and has been so for a long time. Coordinated courses assign common homework and exams, and, often, share a Canvas page. The coordinator is responsible for creating materials, the schedule, and the Canvas page, among other duties. Coordinated courses traditionally have 2-3 midterms and a final exam consisting of short-answer problems graded using partial credit. While undergraduate teaching assistants grade homework, exams are graded collaboratively by the instructors, with each instructor assigned to grade a portion of each exam for all students. Generally, individual class activities are determined by the faculty member, although the coordinator may provide resources such as lecture notes or group activities. Our coordinators are members of the teaching faculty, while most of the instructors are adjuncts, graduate teaching fellows, or postdocs. The coordinator is expected to hold regular meetings with the instructional team.

As Fall 2024 approached, Becky wanted to implement the Linear Algebra model in Calculus I, a 4-credit course required of all students. We referred to the system as “Proficiency-Based Grading” (PBG) to avoid using the term mastery, but it was similar to the Linear Algebra model. Becky reached out to the instructional team, including Lucas, to see if they were interested in piloting PBG in Calculus I. Luckily, everyone was! Becky identified 21 learning outcomes, which were assessed on eight exams held biweekly throughout the semester. Students were able to attempt outcomes between 3 and 5 times. An outcome would appear on three consecutive assessments before “falling off” until the end of the semester. Just as in Linear Algebra, the “exam score” was computed as the proportion of outcomes for which a student demonstrated proficiency at some point in the semester. One difference between the courses was that in Calculus I, we added a final exam over 5 designated core outcomes that was graded traditionally. The course grade was based upon the PBG exam score, the final exam score, homework, and engagement. 3

We made a few changes from the Linear Algebra model, due to the coordinated setting:

• Schedule Adjustment: To make space for 8 assessments, we used the 3 midterm and 1 final day and their corresponding review days from the previous year’s schedule. This meant we didn’t have as much review time in class, so we reduced time spent on a couple of topics to make time to review.

• Grading Assignments: Instead of having each instructor grade a portion of each exam, each instructor was assigned a subset of outcomes. Anytime one of those outcomes appeared on an assessment, that instructor was responsible for grading the corresponding problem for all students, as well as grading the revisions.

• Organization: We had to have a tight schedule to have time for exams, make-up exams, and revisions. This meant that if an exam occurred on a Wednesday in Week 5, make-ups4 would be held Friday of Week 5 and Monday of Week 6. We agreed to complete the grading by Monday of Week 6, so that students would have time to complete revisions5 by Thursday of Week 6. That gave the instructors time to complete the grading of revisions by Monday of Week 7, allowing us to release solutions in time for students to study for the next assessment on Wednesday of Week 7. Additionally, as coordinator, Becky needed to make sure that all assessments were created and printed, grading was done on time, and the Canvas gradebook was updated. This also meant careful scheduling of the grading assignments so that no one was responsible for grading two first appearances of an outcome on the same exam, as the first appearance required the most work.

• Communication: The instructional team met weekly to discuss assessment problems, what would constitute a Minor Revision vs. a Not Yet designation, and to share common mistakes occurring on the assessments.

• Final exam: As mentioned, the final exam consisted of 5 core outcomes and counted for 13% of the final grade. These 5 problems were graded traditionally, with partial credit, using a rubric from previous semesters. They could also work on any other learning outcomes during the final exam period6.

So….how did it go?

We are currently analyzing lots of data and will be submitting our results and analysis for publication. In addition to pre- and post-course surveys, we are able to analyze grades in Calculus I as well as performance in subsequent courses7 for both the Fall 2024 cohort (pilot) and a Fall 2023 cohort (“traditionally” graded). We can distinguish in the data students who have seen Calculus before and those who haven’t. We can summarize some of the preliminary data as well as lessons from our own experience!

• Positive - Grades: Preliminary data indicate that the overall Calculus I GPA of students in the Fall 2024 group is greater than that of the Fall 2023 group, with bigger gains occurring for the group of students who haven’t seen Calculus before. Additionally, students who took Calculus I in Fall 2024 are generally performing at least as well in Calculus II and Physics I as students who took Calculus I before Fall 2024, again, with bigger gains occurring for students who hadn’t seen Calculus before.

• Positive - Student Benefits: Students from Fall 2024 report reduced stress, that they benefited from clear expectations, and that they felt the system was fair and helped them learn. Additionally, many recognized that PBG allowed them to learn from mistakes.

• Positive - Instructor Workload: We had support from our teaching and learning center to have a focus group with the instructional team to get feedback on the experience at the end of the term. The group unanimously reported that while grading occurred more often, it was not an increase in workload as the grading was easier.

• Challenge - Rubric: Although we had instructor meetings to discuss differences between a Minor Revision versus a Not Yet designation, the implementation of those discussions was not always consistent. Some of the discrepancies in grading from one outcome to another was frustrating to students8.

• Challenge - Feedback: Also related to the grading was the fact that we didn’t clearly discuss what kind of feedback to give. The feedback different instructors gave varied quite a bit. Some feedback was vague (e.g. “you have an error in part a or b”) or incomplete. This was a challenge as a coordinator, as Becky didn’t want to be too prescriptive as to what faculty should do, but I think we all struggled with answering some student questions about outcomes we didn’t grade.

• Challenge - Revisions: Possibly due to the feedback given, students would sometimes submit revisions that were partially complete but didn’t address all of the issues. We would try to give students a chance to fix these (Minor Revisions on Minor Revisions), but this required an even tighter timeline, causing some stress for faculty.

Overall, faculty had a positive experience, as did many students. Our goal in the current semester was to address some of the challenges to make improvements.

What are we doing differently this semester?

Our second iteration is currently running (Fall 2025). We have 21 sections, about 700 students, and 8 instructors. These are some of the changes we made:

• We need clear communication about the difference between Minor Revision, and Not Yet - both for students and faculty. We added two items to the course to support faculty and students: (1) for each outcome, we now provide on our LMS a detailed description of the outcome and a list of common reasons for a Minor Revision and for a Not Yet mark, and (2) we created Assessment Prep Quizzes for engagement points. These online quizzes consist of 5-6 solutions to a problem, and students analyze each and mark each as Proficient, Minor Revision, or Not Yet. The quizzes introduce common errors9, and students can retake them until they get the right answer. Before making them available to students, faculty discuss the responses and agree on how to mark borderline cases.

• We need to be (more) organized! Robert wrote about the sweet spot of biweekly assessment, which we had independently discovered in Linear Algebra! But in a large course with many students, there isn’t much flexibility in the schedule. We added a policy about excused absences past the Monday following an assessment. In particular, we are giving an option to have extra time on a later assessment10. Additionally, we no longer allow Minor Revisions on Minor Revisions - the timeline makes it too challenging.

If you want to see how we described the system and outcomes, as well as a copy of our syllabus, check out this folder.

Closing Thoughts

At the time of this writing, we are halfway through Fall 2025 semester and are seeing positive improvements from our first term while still facing some challenges. For instance, the Assessment Prep Quizzes and discussions about them have helped us be more consistent about differences between marks, but inconsistencies still occur. Relatedly, while much of the feedback to students appears to be more detailed than last year, there are still times where students aren’t sure what they did wrong.

Communicating the concept of alternative grading to students remains challenging. Becky provides a Day 1 slide template to help other instructors have this conversation, but it isn’t clear how each instructor talks about the system. It didn’t seem to be a problem in Fall 2024, but one current instructor said they spent a quarter of a class fielding student complaints about the lack of partial credit, which makes us wonder how we can support better communication with students. We are considering making a video about the system with a short assignment related to the video.

While there is still work ahead, we’re heartened by what we’ve learned and accomplished in scaling alternative grading across a large, coordinated course. We hope our experience offers insight for others navigating the complexities of coordination, consistency, and meaningful assessment change.