Using LEAD Page Views by Date and Hour

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 Date and Hour” offers a heat map visualization that shows data regarding the timing of student access activity to a course in Canvas.

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

The LEAD Page Views by Date and Hour data could help offer insight into when students are most active in course access. This information may be useful to you to find out: 

  • if students are accessing content, such as a page or video, before class. 
  • when several students are likely to be available for attending a synchronous session, such as for office hours or review. 
  • when students are most active in participating in online discussions.

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 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: Heat map

This visualization format is a Heat Map. It is similar to a table or spreadsheet, but instead of numbers, it shows colors based on associated date values. The visualization's X axis is similar to columns in a spreadsheet, and is organized by the day of the week. The Y axis is similar to rows in a spreadsheet, and is organized by the times of the day, in one-hour blocks. A single cell in the heat map shows the cumulative number of Page Views during the day/time, for the selected activities, for the selected students, during the selected time period. Hovering your cursor over a single cell reveals the Page View value for that day/time.
LEAD Page Views by Date and Hour screenshot - example of cursor hover

Colors in the Heat map

Each cell in the heat map is colored based on the number of Page Views for the associated day/time combination. In the range of colors, gray is fewer counts of access, and the reds are more counts. The darker the red, the higher the counts of page views. In effect, the darker the red, the more students are accessing the materials in the course. White appears when there are no counts of access during the day/time.

Seeing student access data organized by day of week, time of day could help offer insight into patterns of when students are most active in your course.

LEAD Page Views by Date and Hour screenshot

Filtering the data

The default view is to show the Page View data for all students in a course, from the start of the semester to within 72 hours* of the current time. Note that the visualization shows an aggregate of the counts for all the Mondays, Tuesdays, etc., across all the weeks included in the date range. This may be good to see general patterns over multiple weeks.For example, if you wanted to offer synchronous review sessions before an exam, so students could ask questions, you might look to see what days and times had a lot of access. This could indicate a pattern of when students are generally available.  

If you want to see the data for a particular week, for a category of activity types, for a specific document, or for selected students, you can use the filtering functions available on the left. 

*(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 and heat map colors will adjust according to the selected date. This may be useful for reviewing access trends by time.

LEAD Page Views by Date and Hour screenshot - filter by date

Filtering by Activity Type or specific Activity

You can filter by category of Main Activity Type, this example shows Kaltura Videos. The Page View counts and access day/times will adjust according to the category. This may be useful to review for access trends to a Main Activity Type.LEAD Page Views by Date and Hour screenshot - filter by Activty Type You can further filter by a specific activity. The Page View counts and access day/times will adjust according to the activity selected. This may be useful to review for access trends to a specific activity. In this example, there was no student access to the selected activity on a Tuesday or Wednesday during the time period. Since there was no Page View data for these days, the heat map does not show Tuesday or Wednesday.LEAD Page Views by Date and Hour screenshot - filter by activity

Filtering by student

If you want to check on a student of interest, you can select them from the Student Name filter.LEAD Page Views by Date and Hour screenshot - 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

See Also:

Keywords:LEAD, learning analytics, guiding principles, data, FERPA, Data governance   Doc ID:107175
Owner:CID F.Group:Learning Analytics
Created:2020-11-13 16:36 CDTUpdated:2020-12-18 14:05 CDT
Sites:Learn@UW-Madison, Learning Analytics
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