How do I grade and measure mastery using Insights for Canvas Outcomes?

This guide compares traditional grading and mastery/outcome assessment with Insights for Canvas Outcomes to help educators and administrators make informed decisions about their assessment practices in Canvas.

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Introduction

Section 1: Using the Canvas Gradebooks

Section 2: Assessment Options

Final Thoughts

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Introduction

Grading is a fundamental aspect of education, serving to evaluate student learning and provide feedback. Traditional grading systems, often based on points and percentages, have been the norm for many years. However, alternative approaches like mastery-based or outcome-based assessment are gaining traction, focusing on students' demonstration of specific knowledge and skills. This guide compares traditional grading and mastery/outcome assessment, specifically within the context of the Canvas Learning Management System (LMS) and Insights for  Canvas Outcomes, to help educators and administrators make informed decisions about their assessment practices in Canvas.

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Section 1: Using the Canvas Gradebooks

Traditional Gradebook: Points and Percents with Rubrics

The Traditional Gradebook in Canvas is what most instructors are familiar with: assignments are graded using a points-based system, and those points are translated into percentages. Scores are then aggregated to calculate a final grade, often represented by a letter (e.g., A, B, C) based on a predetermined scale. Instructors can define rating scales or use the Canvas default to convert numerical scores into letter grades. Each assignment defaults to the point value set in the assignment; however, the Enter Grades as menu allows you to view the scores as percentages. We recommend setting the gradebook view to percentages, especially when using rubrics as grading criteria for students.

Instructors can create rubrics for assignments that assign specific point values to different performance levels. When students submit work, instructors use SpeedGrader to evaluate submissions and assign point-based grades.

Key Features:

• Assignments are scored out of a total number of (arbitrary) points.
• Final grades are calculated using total points and/or weighted categories.
• Rubrics help standardize grading but primarily serve the purpose of calculating grades.

Best Use Cases:

• Courses focused on completion and task-based grading.

• Instructors who prioritize numerical scores and overall course averages.

• Traditional grading structures without the focus of tracking mastery. 

Example: Traditional Grading for a History Course

• Outcomes: Not heavily used.

• New Quizzes Outcomes: Do not populate the Learning Mastery Gradebook at this time, unless they are manually assessed on a rubric attached to the New Quiz. 

• Assignments:

• • • Quiz 1: 10% of final grade

    • Essay 1: 20% of final grade

    • Midterm Exam: 30% of final grade

    • Essay 2: 20% of final grade

    • Final Exam: 20% of final grade

• Gradebook: Points are assigned for each assignment, and weighted assignment groups are used to calculate the final grade.

• Grading Scheme: 

  • • A standard grading scheme (e.g., 90-100% = A, 80-89% = B) is used to convert the final numerical grade to a letter grade.

    • Canvas’ Assignment Grading options of Letter Grade, Complete/Incomplete, and Percentage all ultimately convert to a points scale in their final calculations. 

Learning Mastery Gradebook: Rating Scales and Points

The Learning Mastery Gradebook (LMG) provides a more nuanced way to evaluate student progress by focusing on outcome alignment and achievement levels. Instead of total points, mastery is assessed based on how well students perform against specific learning outcomes using predefined rating scales (e.g., 1–4 or 1–5). Instructors can use this to see which students have met, not met, or exceeded expectations for each outcome. Scores and color coding in the Learning Mastery Gradebook reflect students’ mastery of individual learning outcomes. These scores do not convert to the traditional gradebook or to a letter grade.

Key Features:

• Outcomes must either be created or imported into each course in Canvas.

• Outcomes are aligned to rubric rows.

• Rubric ratings are mapped to mastery levels (e.g., Developing, Proficient, Mastered).

• The LMG aggregates student performance across assignments aligned to the same outcomes.

 

Best Use Cases:

• Institutions focused on accreditation or standards-based assessment.

• Programs trying to move to a competency-based model and/or award microcredentials.

• Programs measuring student growth over time.

• Faculty using common outcomes across sections or departments.

 

Note: Faculty using these outcomes can make the Outcomes page in a course shell visible to students, giving them the opportunity to examine what they will be assessed on.

Example: Mastery-Based Assessment for an English 101 Course

• Outcomes:

  • ILO1.4: Demonstrate skillful use of high-quality, credible, relevant sources to develop ideas that are appropriate for the discipline and genre of the writing.

  • ILO1.5: Use graceful language that skillfully communicates meaning to readers with clarity and fluency and is virtually error-free.

  • ILO1.2: Use appropriate, relevant, and compelling content to illustrate mastery of the subject, conveying the writer's understanding, and shaping the whole work

• Assignments: Assignments are designed to assess specific outcomes and are typically created to test student knowledge or to require students to demonstrate a specific skill resulting from a learning activity, which is aligned to learning outcomes using rubrics. For example:
  

• New Quizzes: Outcomes aligned to individual questions do not populate the Learning Mastery Gradebook at this time. Rubrics added to the New Quiz will populate the LMG, but will not be automated in its grading. 

• Final Grade: The final grade might be based on the number of outcomes the student has mastered at a certain level. For instance, mastering all outcomes at the "Meets Expectations" level could earn a "B," while mastering all at the "Exceeds Expectations" level earns an "A."

Best Practices for Combining: Rating Scales (1–4, 1–5) and Percentages

Blending both gradebooks allows instructors to meet institutional reporting needs while still assigning traditional grades. This approach can be powerful when done correctly but requires careful calibration. Assignments are graded using the rubric in SpeedGrader. Points awarded for the assignment go to the Traditional Gradebook, while points awarded in the rubric are captured in the Learning Mastery Gradebook. Rubrics can be set up as non-scoring rubrics, which allows for outcome-based grading without points.

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Section 2: Assessment Options

Rubric for Generating Grade Percentages and Learning Mastery

Rubrics are the central tool for capturing both traditional grades and mastery-level data. When outcomes are linked to a rubric, Canvas automatically records performance data into the Learning Mastery Gradebook.

Dual-Purpose Rubrics:

• Add outcomes directly to rubric rows.

• Enable the outcome to contribute to the Learning Mastery Gradebook.

• Ensure point values reflect percentage expectations for traditional grading, but DO NOT use decimals in the point values set within the mastery rating scales. Insights does not support the use of decimals. If an instructor enters a decimal score for some reason, Insights will collect the score and indicate if mastery was attained, but it will not show the mastery level.

Assignment Settings for Mastery Capture (Highest, Most Recent; Submission Types We Handle)

Canvas Outcomes supports multiple calculation methods for tracking mastery, used in conjunction with the Learning Mastery Gradebook.

Calculation Settings:

• Weighted Average: 

  • Splits the total percentage between the most recent assessment item and an average of all prior assessment items.

  • Averages are not ideal to use in aggregated data as they may not accurately represent the overall performance of a data set. Additionally, averages do not represent mastery or competency and can obscure seeing student progress and the true extent of their learning.

• Decaying Average: 

  • Uses a formula to determine proficiency based on students' average scores, giving more weight to the most recently scored. 

  • This calculation method is even more complex than weighted average and can produce unexpected and skewed results in the aggregated data that are difficult to explain and understand.

• Highest: Takes the best performance across multiple assessments.

• Most Recent: Uses the latest attempt or submission.

• n Most Recent: Average of the last n submissions.

Assignment Types Supported by Insights:

• Native Canvas Assignment with a rubric populated with outcomes.

  • File uploads.

  • Text entries.

  • No submission.

  • On paper.

• Classic Quizzes (outcomes are aligned only to a question bank).

• New Quizzes (only if outcomes are aligned to a rubric that attaches to the quiz as a whole, not to individual questions. The rubric needs to be manually used to assess the outcomes).

• Discussions with a rubric populated with outcomes.

• External Tool Assignments (Including Lucid) that can still have a Canvas-native rubric aligned and populated with outcomes.

Note: LTI tools that do NOT write to Canvas Outcomes API or use Canvas-native rubrics do not currently have a way to get outcomes mastery data into Insights OR Canvas. As 1EdTech Standards like CASE, CLR, and LTI converge, it is likely that vendors will have an easier path to do so: consult your LTI provider on their CASE and CLR roadmap to learn more about specific LTI tools and learning mastery support.

 

Thinking About Mastery Level Mappings and Using Data Across Curriculum

Mapping rubric scores to mastery levels is the bridge between classroom performance and institutional learning goals.

What to Consider:

• What does “Mastery” look like in your program or institution? Define it clearly.

• Think about Mastery Levels in Insights as your data buckets. How will you want to look at and utilize these buckets down the road? Like your rubrics, keep it simple – about five is sufficient.

• Map your Canvas rating scales to the Mastery Levels – which rubric level equals “Mastery”?

• Use the same outcomes in multiple courses to see student growth longitudinally.

Using Insights Data:

• Track outcomes across departments and programs.

• Identify bottlenecks or gaps in student learning.

• Support curriculum improvement and accreditation reporting.

Best Practices:

• Train faculty early on outcome alignment and using rubrics.

• Standardize parameters for rubrics and scales across departments.

• Use data to facilitate conversations, not just reports.

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Final Thoughts

Combining traditional grading with outcome assessment can feel complex at first, but with the right rubric design, consistent rating scales, and thoughtful use of both Canvas Gradebooks, you can support meaningful measurement and reporting of student mastery. Insights for Canvas Outcomes helps surface the data you need — but it starts with intentional setup and faculty buy-in. Start simple. Align a few outcomes. Use a shared rubric. Then iterate. 

Questions? Reach out to your Canvas administrator or your Insights CSM.

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