LEAD - Identify student performance in relation to a threshold

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.

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 the current semester of LEAD at go.wisc.edu/lead. You will be able to log in by following the instructions on the screen. 

For easiest access to prior semesters of LEAD (each semester has a separate link) as well as other learning analytics resources, add the Learning Analytics for Instructors Widget to your MyUW page.

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

Scatter plot visualization

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.

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

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.

LEAD data is not refreshed in real-time; each tool has a different frequency for updating their analytics. There may be a lag time of up to 5 days for when students' access data appears in LEAD.

• This frequency of updates may be useful for reviewing patterns of access across several days or weeks, but does not completely show the most recent activity.

• For example, don't use LEAD to see if students accessed a course resource or assignment immediately before today's class
.

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 that the data won't reflect whether a student downloaded content to read later, read the materials in-depth, skimmed or read superficially, or accessed reading material but didn't read at all.
• A lack of access data does not necessarily mean a lack of access to course materials. For example, 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. For example, it does not show how much time a student spent on a specific course page or activity (duration).

There may be nuances in what data are logged for content stored outside of the Canvas course, due to how the data are captured or how the course was created.

• For example, links to some embedded content, and some videos or external websites will not be included. 
If you value this type of access data, become familiar with how this data is recorded in your course before interpreting it.