Using LEAD Page Views by Activity Type

The Learner Engagement Analytics Dashboard (LEAD) is a course-level dashboard that provides visualizations of student access to materials in Canvas courses. The LEAD tab named “Page Views by Activity Type” offers bar chart visualizations that show data regarding student access activity by category of activity type or by specific activities.

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

The LEAD Page Views by Activity Type data could help offer insight into students access of course materials. This information may be useful to you to find out: 

  • which documents, media, or activities students are accessing. 
  • the amount of student access to categories of activity types, for example Kaltura videos or Canvas Discussions. 
  • the amount of student access to specific course materials, such as an individual content page or video.

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.

Data visualization format: Bar chart

The visualization format is a Bar chart. Horizontal bars represent quantitative data, the lengths of the bars will vary, depending on how many times an activity is accessed. For example the longer bars reflect that student are accessing that content more than the shorter bars.

Depending on the types of content and activity you have in your course, as well as how you have your course setup, you will see different bars and counts displayed here. For example in the screenshot below, the instructor may be using few Discussions, resulting in a relatively low count of page views of this activity type. On the other hand, there is a much higher page view count of Canvas Quizzes indicating more student access to this activity type. The category of Engage E-Text is not present in the visualization, suggesting that this activity type is not used in the course.LEAD Page Views by Activity Type screenshot

Filtering the data

The default view is to show the Page View count data for all students in a course, counted from course activities from the start of the semester to within 72 hours* of the current time. This may be good to see general trends of access to the course materials.
If you want to see the Page View counts for a specific piece of content, or for an individual student, you can use the filtering functions on the left of the screen.

*(Note: Please keep in mind that LEAD data is aggregated from multiple sources that are updated at various frequencies, depending on the tool. Data can take up to three days to appear in LEAD.)

Filtering by date range

You can filter the data to a date range by choosing a start date, end date, or both. The Page View counts will adjust according to the selected dates. This may be useful for reviewing access trends by time.LEAD Page Views by Activity Type - filter by date

Filtering by Activity Type or specific Activity

You can filter the results to those of a single Main Activity Type. This step is optional when you are interested in a specific activity, and will filter the choices in a second filtering step to be limited to those of the selected activity type. This example shows filtering on Kaltura Videos.LEAD Page Views by Activity Type - filter by Activity Type

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 shows the data filtered to a single Kaltura Video activity, and the Page View count has changed.LEAD Page Views by Activity Type - filter by specific Activity

Filtering by student

If you want to check on a student of interest, you can select them from the Student Name filter. The visualization will update to reflect the student's Page View counts.LEAD Page Views by Activity Type - filter by student

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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See Also:




Keywords:LEAD, learning analytics, guiding principles, data, FERPA, Data governance   Doc ID:107165
Owner:CID F.Group:Learning Analytics
Created:2020-11-13 12:26 CDTUpdated:2020-12-18 14:05 CDT
Sites:Learn@UW-Madison, Learning Analytics
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