LEAD - Identify student performance in relation to a threshold

The Learner Engagement Analytics Dashboard (LEAD) is a course-level dashboard that provides visualizations of student access to materials in Canvas courses.

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.

Using LEAD Grades by Page Views

 

Scatter plot visualization

Using the Grades by Page Views tab, 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.

You can use LEAD's Grades by Page Views tab for quick visual analyses to look at general patterns of students' grade ranges.thresholdOverview.png

Example: Students with grades lower than 70 percent

In this example, the threshold of interest is a grade of 70 or below. The visualization shows approximately eleven students with grades in this range.thresholdStudentsLT70.png

Hovering your cursor over the dot will reveal a window with the student name, and values of Page Views and grade.

thresholdHover.png


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
.


See Also:




Keywords:LEAD, learning analytics, guiding principles, data, FERPA, Data governance   Doc ID:107265
Owner:Kari J.Group:Learning Analytics
Created:2020-11-18 12:11 CDTUpdated:2021-08-20 10:01 CDT
Sites:Learn@UW-Madison, Learning Analytics
Feedback:  1   0