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 for insights into student access to online course content, as well as some reminders and caveats for use.
Note: This document describes a learning analytics approach to help support student success.
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:
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.
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.
The LEAD Page Views by Activity Type visualization can offer data related to students' online access to course materials.
The LEAD Page Views by Activity Type visualization can offer data related to students' online access to course materials. The default view is to show the counts of all students' access to all the main categories of content in your course.
You can filter the results to those of a single Main Activity Type (for example Kaltura Video or Canvas Wiki Page). This step is optional but can help you more quickly locate a specific video, assignment or page. This example shows filtering on the Canvas Wiki Pages type. This is the Main Activity Type most often used for course content or resources.
Filtering first by Main Activity Type will help you quickly locate a specific activity (without having to scroll through a list of all your course Announcements, Discussions, Kaltura Videos or other types of content). This example below shows the data filtered to a single page of content (How to Learn at a Distance...), and notice how the Page View count has changed.
You may be able to gain additional insights into patterns of how often, and when students view your course content by using additional LEAD visualizations.
In this example, the Grades by Page Views scatter plot, filtered for a single page of content, shows a general pattern that most students who have viewed the selected page have viewed it between 10 and 35 times.
In this example, the Page Views by Date and Hour heat map, filtered for a single page of content over the first week of class, shows a general pattern that most students who viewed the content page did so from Tuesday afternoon through early Wednesday morning.
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.
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.
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.