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Best Practices | Canvas Analytics


In a face-to-face course, an instructor often promotes student retention and success by assessing students' engagement or participation in the course. Are students showing up to class? Are they taking notes? Participating in discussions? Are assignments being turned in on time? Have students read the material? Do they look confused? Are they awake? While some of these cues are observed behaviors, many can also be measured in an online course.

Learning Analytics (LA) is the collection and analysis of data that is gathered while students are engaging with and in the learning management system (LMS) (Pappas, 2014). Instructors should be aware of how students are using the online course so that they can look for patterns and predict outcomes, make meaningful adjustments to the course, and proactively intervene on behalf of at-risk students, if necessary. Knowing when and how to use course analytics can empower the instructor and help to prevent students from falling through the cracks (Avella, Kebritchi, Nunn, & Kanai, 2016).


In Practice

Canvas features course-wide and individual student analytics reports for published courses. These reports can provide a snapshot of how and when the system is being used, when submissions are taking place relative to set due dates, and what student achievement looks like in terms of scores.

In Canvas' New Analytics, there are four main sections that can be displayed graphically or as a table.


"Weekly Online Activity" reports the number of page views and participations (posting to a discussion, submitting an assignment or quiz, joining a conference, etc.) by date.  An instructor may look for and consider trends such as greater activity as a due date approaches. This information may prompt the instructor to ensure availability accordingly (Lorenzetti, 2016). An instructor may also notice a week where activity is significantly lower and wish to add an element to motivate students to interact with the content or fellow classmates. Unlike previous versions of Analytics in Canvas, activity on a mobile device is now recorded and included in the data. In addition, consider the time students may have spent with content or activities taken outside of the system (for example downloading and reading a PDF or working on a project using an external program or website).


The"Reports" allows the instructor to run a report that generates a .csv file to view a list of missing assignments that have not been submitted yet, a list of late assignments, excused assignments, a class roster, or all course activity.


The“Course Grade” tab provides both the average and the point distribution for each activity, including the lowest, median, and highest scores. An instructor may wish to consider the activities that deviate from a normal distribution. Was the assignment too challenging or not challenging enough? Were the objectives, content, and assessment all in alignment? Be wary of late assignments for which you have not yet entered a grade of zero. The distribution only features scores that have been entered and could be misinterpreted for that reason. This tab also has a “Message Students Who” feature that permits the instructor to send a group message to all students who are missing a given assignment, those who submitted the assignment late, or those who scored within a particular range.


The "Students" table gives information on the page views, participations, submissions, and score for each individual student. It is sortable by page views, participations, or score. This could be helpful in determining which students are low scoring and why. Is it because not enough time is being spent in the course or some other reason?


Additional Resources



Avella, J., Kebritchi, M., Nunn, S., Kanai, T. (2016). Learning Analytics Methods, Benefits, and Challenges in Higher Education: A Systematic Literature Review. In Online Learning, 20 (2),7-9. Retrieved from


Lorenzetti, J. (2016, May 13). Using Student Analytics for Online Course Improvement. Faculty Focus. Retrieved from


Pappas, C. (2014, June 3). 5 Reasons Why Learning Analytics Are Important For eLearning. eLearning Industry. Retrieved from