Today’s guest post comes from Melanie Butler. Melanie is a Professor of Mathematics at Mount St. Mary’s University, a small Catholic liberal arts college in Emmitsburg, Maryland. She teaches across the mathematics curriculum, with a particular focus on core courses. Her work centers on alternative grading practices and the design of hybrid, blended, and asynchronous learning environments.
Introduction
Mount St. Mary’s University is a small, Catholic, liberal arts school located in Emmitsburg, Maryland, with an enrollment of approximately 2,000 students. The university serves a diverse student body, including many first-generation college students. The course I teach is a general education mathematics course required for all undergraduates, many of whom bring diverse backgrounds and varying levels of math anxiety. It is designed to introduce students to mathematical thinking through topics such as probability, financial math, graph theory, and problem solving. Each section typically enrolls about 25 students, and I teach multiple sections each academic year in a fully asynchronous format. For the past five academic years, I’ve taught the course using a specifications (specs) grading system.
My grading approach draws from other alternative grading systems, emphasizing mastery, student agency, and clear expectations. Each unit is structured around Bloom’s taxonomy, guiding students from foundational understanding to creative application. Canvas, our learning management system, controls the flow, requiring students to demonstrate mastery at each level before progressing.
This article reflects on the successes and challenges of implementing specs grading in this context. I’ll share how the system supports engagement and learning, where it falls short, and how student feedback has shaped its evolution.
Course Design and Implementation
The course begins with a mini-unit to familiarize students with the structure. Students are introduced to the grading system, with make-ups, redos, and Bloom’s taxonomy, through a series of pages in the LMS. As an example, the ntable later in this post explains to students how to navigate making up or redoing assignments. Students then complete a course policies quiz, which helps to reinforce the big ideas about the course (perfect score required, unlimited attempts). Periodic “course policies check” quizzes reinforce expectations throughout the semester.
The course is then divided into six two-week units: Data and Statistics, Financial Math, Graph Theory, Prime Numbers, Probability, and Problem Solving. Each unit is structured around a simplified version of Bloom’s taxonomy: Remember/Understand, Apply, Analyze/Evaluate, and Create, allowing students to build mastery progressively.
Canvas controls the flow of content, requiring students to complete tasks in sequence. Week one of each unit focuses on building foundational understanding through lecture videos, discussion boards, and auto-graded assignments at the Remember/Understand and Apply levels. Time estimates are provided for each activity, helping students manage their workload. For example, in the Data and Statistics unit, students complete an Apply assignment where they use course concepts to work with real graphs, identifying patterns and drawing conclusions. The questions are designed so students must actively apply what they’ve learned, rather than relying on guessing.
On the Remember/Understand and Apply assignments, there are about 5 to 10 questions, each worth 1 point, and students must answer each question correctly to advance, but they have unlimited attempts for the Remember/Understand assignments and three attempts for the Apply-level tasks. If they struggle, they’re encouraged to seek help via discussion boards, office hours, or tutoring, and then reflect on their learning to earn additional attempts.
Week two of each unit introduces higher-order tasks: Analyze/Evaluate and Create. These are pass/fail assignments and are graded as 1 point each with students earning a 0 or a 1, using clear specifications and rubrics, with sample successful submissions provided. For example, in the problem solving unit, students are asked to analyze a problem in their own life that involves mathematics, using the problem solving process we have discussed. Students are given sample topics (if I’m an elementary school teacher after I graduate, how much money will I have for rent) and guided through picking a topic. Specifications for the assignment are as follows:
All five steps of the problem-solving process are present, clearly labeled, in order, and each step is relevant to the problem.
Mathematical calculations are correct (minor errors can be revised) and clearly organized in a labeled table.
Each calculation is accompanied by an explanation of what it represents and how it was derived.
All external sources are properly cited.
The solution demonstrates clear communication: organized, readable, and consistent with expectations from the Communication Rubric.
Students can revise and resubmit within the grace period, which is one week during a 16-week semester. Past the grace period, students use a token system, three tokens per semester, to redo assignments, although the grace period makes this largely unnecessary. This structure balances rigor with flexibility, guiding students through increasingly complex tasks while offering multiple paths to success. Below is what I share with students to help them navigate the course expectations and opportunities.
Assignment Make-Up and Redo Options
Instructional Materials and Engagement Activities
Available and can be completed until the last day of class
Remember/Understand level assignments
If you miss the deadline, you have a grace period to continue your work.
If you miss the grace period, you need to use one of your three Tokens (for makeups and redos past the grace period) to complete the assignment.
Apply level assignments
You are limited to three attempts.
If you haven’t reached a perfect score after three attempts, it’s a sign that some extra guidance could help you master the material.
To get this help, I’ll ask you to use the course discussion boards or visit a Student Instructor (SI) Help Session.
After that you can get an extra attempt by completing: Extra Attempts on Assignments.
You will want to complete this by the due date or within the grace period
If you miss the grace period, you need to use one of your three Tokens (for makeups and redos past the grace period) to complete the assignment.
Analyze/Evaluate level assignments
If you don’t pass the assignment, I’ll provide feedback and you can redo it by the due date or within the grace period.
If you miss the grace period, you need to use one of your three Tokens (for makeups and redos past the grace period) to complete or redo the assignment.
Create level assignments
If you don’t pass the assignment, I’ll provide feedback and you can redo it by the due date or within the grace period.
If you miss the grace period, you need to use one of your three Tokens (for makeups and redos past the grace period) to complete or redo the assignment.
Final grades for the course are calculated using the Canvas gradebook and weighted categories: 50% for Remember/Understand, 10% for Apply, 10% for Analyze/Evaluate, 17% for Create, and 13% for Engagement. These percentages do not change the underlying mastery-based requirements of the course; they are primarily a way to give students a visible percentage grade throughout the semester, which many students requested. These percentages were chosen so that completing the Remember/Understand assignments and the Engagement assignments would allow a student to pass the class (with a D). To earn a C, the student would also need to pass the Apply level assignments, and so on.
What’s Working
Over time, I’ve seen clear benefits to using specifications grading in this asynchronous course. Students consistently report lower stress levels, greater clarity about expectations, and a stronger sense of control over their learning. The structure, especially the breakdown by Bloom’s taxonomy, helps students understand not just what they’re learning, but how they’re learning it.
The auto-graded assignments at the lower levels allow students to build confidence and mastery before moving on. Because they must score perfectly to proceed, they’re encouraged to revisit material and seek help when needed. This aligns well with the growth mindset messaging embedded in the course, and students appreciate the opportunity to learn from mistakes without penalty.
At the higher levels, the pass/fail grading with rubrics and sample assignments gives students a clear target. The ability to revise work, especially with the grace period and token system, reduces anxiety and encourages persistence. Students often comment on how much they value being able to redo assignments and receive meaningful feedback, especially on the Analyze/Evaluate and Create tasks.
The asynchronous format doesn’t seem to hinder engagement. In fact, students often express appreciation for the pacing and flexibility. Creative assignments, like designing a probability-based game, help students connect math to their own interests and experiences. Discussion boards remain active and authentic, suggesting that students feel safe sharing ideas and asking questions.
Overall, the system fosters a supportive learning environment where students feel empowered to succeed.
Challenges and Adjustments
Despite its strengths, specifications grading in an asynchronous format isn’t without its challenges. Early iterations of the course revealed two major student concerns: the lack of partial credit and the inability to track a running numerical grade. These issues created stress for some students, especially those accustomed to traditional grading systems.
To address this, I revised the grading structure. Higher-level assignments (Analyze/Evaluate and Create) remain pass/fail, while auto-graded Remember/Understand, and Apply tasks now award partial credit that counts toward the final grade. Students can also redo these assignments using the grace period or tokens, and can access discussion boards for guidance and help. To progress through a unit, students must still achieve a passing level on each activity according to Bloom’s taxonomy. This approach preserves mastery-based progression while reducing the impact of all-or-nothing grading.
For the first several semesters that I used specifications grading in the course, I did not use any percentage grades. Instead, final grades were assigned based on bundles of assignments. Each bundle was a level of Bloom’s taxonomy. To earn a D, students needed to pass all of the Remember/Understand bundle. To earn a C, students needed to pass all of the Remember/Understand bundle and four or more of the Apply bundle, and so on. However, feedback from students indicated that the lack of a running percentage grade for the course was stressful. To address this concern, I adjusted the grading to what, on the surface, is a more typical weighted grading structure as described above. However, the weighting reflects the original intent of the bundled grading: for example, passing all Remember/Understand assignments and contributing to the engagement discussion boards, is equivalent to earning a D, while meeting those requirements and passing four Apply assignments corresponds roughly to a C. Previously, pluses and minuses were based on discussion board participation, but this system was simplified and incorporated into the Engagement category to make grading clearer. In short, the percentages in Canvas track progress but still honor the specifications grading model and mastery-based requirements.
Another challenge is the strict progression requirement. Some students feel frustrated when they can’t move forward without passing a prior assignment. While this structure supports mastery, it can feel punitive. The grace period and token system help, but I continue to explore ways to balance structure with flexibility.
Finally, feedback, especially on auto-graded tasks, can feel limited, and students want to understand their mistakes. I’ve worked to provide clearer explanations and opportunities for reflection. Narrative feedback on higher-level tasks remains a priority, and I often use comments to guide revisions without requiring full resubmissions. For example, in the Create assignment for the Data and Statistics unit, students collect and analyze data and must choose a measure of central tendency (mean, median, or mode) and explain their choice. Some students struggle to select an appropriate measure or justify their decision. I provide targeted feedback on this part of the assignment and then ask students to revisit it and comment with a corrected response, without redoing the entire assignment. This approach also allows for a focused conversation about their work.
Conclusion
Specifications grading in an asynchronous liberal arts math course has proven to be both effective and adaptable. While the system isn’t perfect, and requires thoughtful adjustments to meet student needs, it fosters a learning environment centered on mastery, reflection, and growth. Students appreciate the clarity, flexibility, and opportunity to revise their work, and I’ve found that the structure encourages deeper engagement with the material.
As I continue refining the course, I remain committed to balancing rigor with compassion, and structure with flexibility. Specs grading, when thoughtfully implemented, can be a powerful tool for supporting student success, even in asynchronous settings.
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