LEAD - Are students struggling in your course?

The Learner Engagement Analytics Dashboard (LEAD) is a course-level dashboard that provides visualizations of student access to materials in Canvas courses. This document describes a process you can use with LEAD to help identify struggling students, as well as some reminders and caveats for use.

Note: This document describes a learning analytics approach to help support student success.

What data are available in LEAD?

Campus tools such as Canvas, Kaltura MediaSpace (video), and Unizin Engage eText are connected to roster information. This provides potential for connecting student identity data with a record of their course access and interaction, such as:

  • Course pages or videos they’ve clicked on
  • Grades stored in the Canvas gradebook
  • Participation such as assignment submissions, or discussion posting
  • Time of access or participation

How to access LEAD

To access LEAD, you must be a Principal Instructor in your Canvas Course. Navigate to https://go.wisc.edu/LEAD. You will be able to log in by following the instructions on the screen. 

Once inside LEAD you will have access to a home page and three visualization pages.

  • Page Views by Date and Hour 
  • Grades by Page Views 
  • Page Views by Canvas Activity Type

Please review the Official Data Definitions for the Learner Engagement Analytics Dashboard at the end of this document that provides an overview of LEAD. Those definitions explain what LEAD is and describe and define what data you can see in the visualizations.

How can you use the data available in LEAD?

Some indicators of students struggling with your course may be shown in LEAD data as low grades, low access to materials online, or both. In your teaching, you may use other signs as well that give clues about which students are struggling, such as their attendance or participation. When considering data in this example, think of it as a potential complement to other indicators of struggling.

The LEAD tab named “Grades vs Page Views” offers a Scatter plot visualization that shows data regarding students’ scores from the Canvas gradebook, plotted in relationship to a count of their course Page Views. This visualization can be useful to check for potentially struggling students since the plot style can allow you to see instances of students with low measures of page views, grades, or both.

Scatter plot visualization

The scatter plot visualization plots two different measures for each student -- their grade and their number of Page Views, representing each student with a dot. The dot’s placement on the X (horizontal) axis is based on the number of Page Views, students with fewer page views will be plotted toward the left. The placement on the Y (vertical) axis is based on their grade in the Canvas gradebook, students with lower grades will be plotted toward the bottom.

LEAD Grades by Page Views screenshot

Example: Student with low Page Views and low grade

In this example the scatter plot shows an example of one student whose sum of Page Views and their current Canvas Gradebook Score are each lower than their classmates, shown by a dot far to the left and bottom of the plot. Hovering your cursor over the dot will reveal a window with the student name, and values of Page Views and grade.

LEAD screenshot Grades by Page Views individual student values

Example: Students with grades lower than 60

You can also use the scatter plot to look at general patterns of students' Page Views and grades. In this example, a pattern can be seen that several students have a grade of less 60, although most students' grade is greater than 60.

LEAD screenshot Grades by Page Views emphasizing grades lower than 60

Using the data

Consider what student engagement looks like in your course, and what indicators you look for in addition to online access. For example, you may consider quality of work, interactions with classmates, types of questions and comments made.

  • You could take a ‘wait and see’ approach, and check back on the situation in the future
  • You could consider reaching out to individual students
  • If you see broad patterns among several students, you may consider taking whole-class actions, such as reminders of participation expectation, or revisiting challenging content
  • This data may be useful to you between semesters as part of considering course redesign
Wise, Alyssa Friend, and Yeonji Jung. "Teaching with analytics: Towards a situated model of instructional decision-making." Journal of Learning Analytics 6.2 (2019): 53-69.

Caveats and cautions when using learning analytics data

Data may report that a student has logged in, and accessed a course item, but cannot indicate how a student intellectually engaged with the course.

  • Keep in mind the data won't reflect whether a student downloaded content to read later, read the materials in-depth, skimmed or read superficially, or accessed but didn't read at all.
  • A lack of access data in the report does not necessarily mean a lack of access.
    • Data would not reflect instances where students may have been studying together, if only one student was logged in. 

  • Data gives general information about the amount of access to a course item
    • This may limit the insights you could gain regarding timing and patterns of access.

  • There may be nuances in what data is logged for content stored outside of the Canvas course due to the way in which the data are captured.
    • For example: links to embedded content, videos or external websites.
If you value this type of access data, it is recommended that you become familiar with how this data is recorded in your course before interpreting it.
There may be a lag time of approximately 72 hours from students' access activity, and the availability of the data in LEAD. The data is not refreshed in real-time; each tool has a different frequency for updating their analytics.
    • This frequency of updates may be useful for reviewing patterns of access across several days or weeks, but cannot be considered complete to accurately show recent activity at a moment in time.

    • For example, LEAD data is not a good choice to check for current access status immediately before class

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