Using LEAD Grades by Page Views

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 “Grades by Page Views” offers a scatter plot visualization that shows data regarding students’ scores from the Canvas Gradebook, plotted in relationship to a count of their course Page Views.

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

The LEAD Grades vs Page Views data could help offer insight into students course access in relationship with grades. This information may be useful to you to find out: 

  • Current broad patterns of distribution of grades (as recorded in the Canvas gradebook) among all students 
  • Relative number of students with grades within a range of values 
  • Range of student Page Views counts 
  • Trend of the relationship between student grades and counts of Page Views 
  • Instances where students fall outside of particular ranges, such as having grades lower than a threshold of interest, or differing from the majority of their classmates' grades or Page Views

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: Scatter plot

This visualization format is a Scatter plot. It plots two different measures for each student -- their grade and their number of Page Views, representing each student with a dot. The dot’s placement on the X (horizontal) axis is based on the number of Page Views, the placement on the Y (vertical) axis is based on their current numerical grade.

LEAD screenshot of Grades by Page Views scatterplot


Values for individual students

Hovering your cursor over a dot reveals data about the individual student's grade and Page Views.

LEAD screenshot example of hovering cursor over individual value

Trend line

The visualization also draws a trend line for your whole course comparing grades to Page Views. This line is a representation of the relationship between the two variables of Page Views and grades. A line that slopes upwards toward the right implies a strong relationship between Page Views and grades, implying that when one increases, the other increases. A horizontal line implies little relationship, and a line that slopes downward implies a negative relationship.

There are two factors to keep in mind when interpreting the trend line:

  • While there is usually a general, course-wide relationship between grades and Page Views, it does not prove that one causes the other.
  • When considering results, you may find that individual students do not follow the trend. As with any comparison, the relationship between grades and Page Views should not be viewed as determinative for any individual student.

Hovering your cursor over the trend line shows values of the statistical relationship between the two variables. LEAD screenshot - Grades by Page View  Trend Line

Filtering the data

The default view is to show the data for all students in a course, with their Canvas Gradebook Percentage Score, and all Page Views 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 grade distribution and levels of access to the course materials.
*(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 date, and the grade displayed will remain as the current grade. This may be useful for reviewing access trends by time.LEAD Grades by Page Views screenshot example. Filter by date.

Filtering by Main Activity Type or Name of Activity

You can filter by category using the Main Activity Type, this example shows Kaltura Videos. The Page View counts will adjust according to the category, and the grade displayed will remain as the current grade. This may be useful to review for access trends to a Main Activity Type, and for changes in the Trend Line showing the relationship between grades and Page Views of the selected Main Activity Type.LEAD Grades by Page Views screenshot example. Filter by activity type

You can further filter by a specific activity. All of the Kaltura Videos in this example appear in the Name of Activity dropdown; however only one specific video is selected, below. Notice how the scatter plot visualization has changed.

LEAD Grades by Page Views screenshot example. Filter by activity

Filtering by Student Name

If you want to check on a student of interest, you can select them from the Student Name filter.LEAD Grades by Page Views screenshot example. 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:106987
Owner:CID F.Group:Learning Analytics
Created:2020-11-04 19:47 CDTUpdated:2020-12-18 14:05 CDT
Sites:Learn@UW-Madison, Learning Analytics
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