Using LMS Data to Improve Self-regulated Learning

A recent study examined learning management system (LMS) log files to look at course interactions for 530 online students and found that the students’ self-regulated study habits significantly influenced course achievement. The study focused on those interactions related to self-regulated learning, including such habits  as maintaining a regular study schedule, timely submission of assignments, frequency of course logins, and proof of reading course content (You, 2016).

It may seem obvious that students who possess good study habits are more likely to succeed in the online learning environment (or any learning environment for that matter). However, for me, the important take-away from the study is the potential for leveraging the data collection systems and early alert functionality within our LMS toward reinforcing self-regulating learning habits with those students who may be at-risk of dropping or failing their online course.

The Retention Center is an early alert feature included in our campus LMS – Blackboard Learn. The Retention Center comes set up with four default rules: Course Access, Activity, Grades, and Missed Deadlines. Although these “rules” align fairly well with the study habits mentioned in the study, we are able to customize, as well as to design new rules that can be added to the Risk Table to further support and reinforce study habits.

Screen Shot - Blackboard Retention Center - Rules Customization
Blackboard Retention Center – Rules Customization

The course access and activity rules align with the frequency of course logins habit. The default rule is five days since last access, but in light of the research, I suggest shortening the number to two or three days. The User Activity default rule is set to twenty percent below average for a week – again, I would suggest changing it to three or four days.  The timely submission of assignments and proof of reading course content will depend on setting up corresponding columns in the grade book. By developed assignments that require students to review material (possibly video content) and then to complete a related assessment, grade alerts can be triggered in the corresponding grade book column. By tweaking the default rules or adding new rulee, the instructor can quickly identify students who display poor study habits and immediately reach out to reinforce good habits that support student success.

In addition to the Retention Center, the Performance Dashboard tool can be used to view the content items the student has accessed along with the number and length of posts a student has submitted to course discussion forums. By requiring students to review content and submit substantive posts within a discussion forum, the instructor can encourage the reading of course content – another study habit predictive of student achievement. Encouraging students to subscribe to the discussion forums can further support regular and substantive interaction with classmates.

In a survey of unsuccessful online students at Monroe Community College, students reported the number one challenge they experienced in their online courses… “I got behind and it was too hard to catch up” (Fetzner, 2013). By designing online courses that leverage the LMS analytics features to identify and support at-risk students within the first few weeks, new-to-online students can develop the skills and habits required to be successful in the online environment.

References:

You, J. W. (2016). Identifying significant indicators using LMS data to predict course achievement in online learning. The Internet and Higher Education, 29, 23-30.

Fetzner, M. (2013). What Do Unsuccessful Online Students Want Us to Know?. Journal of Asynchronous Learning Networks, 17(1), 13-27.

 

 

 

 

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2 thoughts on “Using LMS Data to Improve Self-regulated Learning

  1. According to an article in today’s InsideHigherEd the frequency of logins to the online course space are predictive of student retention.

    “…the university’s persistence rate dropped to 76 percent for students who interacted with the learning management system on fewer than five days during the first two weeks of the term, versus 92 percent for students with five or more days of activity during that period. The percentage dropped to 48 percent, meaning more than half will drop out, for students who use the software on one day or fewer.” – Paul Fain, Inside Higher Ed

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