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.
Campus tools such as Canvas, Kaltura MediaSpace (video/audio/images), and Unizin Engage eText are connected to student roster information. This allows student data to be connected 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 with activities such as assignment submissions, or discussion posting
- Times of access
More information about LEAD is provided in the Learner Engagement Analytics Dashboard Overview KB doc, including more details about the data and the official data definitions.
Please note: Unizin’s Engage eReader is now powered by RedShelf. This recent change has temporarily disrupted the data stream that flows into LEAD. Thus, eText user data will not be included in LEAD for the summer sessions and fall 2022 term.
LEAD is currently available for instructors teaching for-credit courses who are enrolled in Canvas as a principal instructor, auxiliary instructor, or supervisory instructor.
Instructors can access LEAD at go.wisc.edu/lead. Follow the instructions on the screen to log in.
Please note: beginning in Fall 2022, all semesters of LEAD can be accessed from this link. You now can select which term you would like to view, from any of the visualization pages.
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 Activity Type
For easy access to other learning analytics resources, add the Learning Analytics for Instructors Widget to your MyUW page.
You can use all three of the visualizations to view data related to students' online access to course materials.
LEAD Page Views by Activity Type: Bar chart visualization
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.
Example: Student access to a course content document
You can filter the results to those of a single Main Activity Type (for example Kaltura Video or Canvas 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 Pages type. This is the Main Activity Type most often used for course content or resources.
(Note: the screenshot below shows filtering by Canvas Wiki Pages, which have been renamed Canvas Pages in LEAD to better align with how Canvas labels pages.)
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.
Example: Patterns of access to course content
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.
- 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.