John Fritz headshot

This is a guest blog post by John Fritz, Ph.D, Assistant Vice President for Instructional Technology and New Media at University of Maryland Baltimore County. His recently published dissertation explores how learning analytics can be implemented to encourage student responsibility for learning and identify effective faculty course designs that help.

Student success means more than graduating students on time. It means preparing learners for what’s next. Graduation is a means, and not an end.

Much talk about the use of analytics in higher education revolves around identifying at-risk students in order to reach them with targeted interventions. But what if we could make analytics and intervention one and the same thing? Moreover, what if we could use learning analytics, not just to increase student performance, but to also cultivate a strong sense of personal responsibility for learning?

At the University of Maryland Baltimore County, we developed a tool, Check My Activity (CMA), that allows learners to compare their own level of activity in Blackboard Learn (number of sessions, number of clicks by tool type, amount of time in class) against an anonymous summary of course peers. If instructors use the LMS grade book, students can also compare their own activity with peers earning the same, higher, or lower grade on any assignment.

Why is this important?

Looking back at LMS activity patterns since 2007, we have found that UMBC students earning a D or F consistently use Blackboard Learn about 40% less than students earning a C or higher. This happens every semester. By providing students with the ability to effectively benchmark their activity against others in their same classes, underperforming students are encouraged to spend more time engaging with course materials and developing effective study habits. In other words, the power of Check My Activity lies in its ability to raise awareness that fosters self-regulated learning. At UMBC, the use of student dashboards through Check My Activity is an important part of our overall student success strategy.

Do student-facing learning analytics actually work?

Check My Activity makes use of data collected by Blackboard Learn. At UMBC, our adoption rates are incredibly high.  Blackboard Learn is used by 95% of all students, 87% of all instructors, and in 82% of all course sections. While only 54% of UMBC students actually use Check My Activity, they are about 1.5 times more likely to earn a final course grade of C or higher compared to peers who do not use the tool. When asked what Check My Activity showed students about their LMS activity, more than 40% of nearly 200 student respondents to an opt-in survey since 2008 have said they were “surprised how my activity compared to peers.” In this same survey, we find that more than 60% of respondents are “more inclined” to use Check My Activity before future assignments are due — an indication of students’ willingness to take more responsibility for their own learning. Interestingly, women are more than twice as likely to report using the CMA than men. 

Why might student-facing learning analytics work?

There are three reasons why I think that student-facing analytics like those displayed in Check My Activity work for students: 

  1. Self-regulation. Discrepancies in how we see ourselves compared to peers can spur changes in awareness, motivation and behavior. Nobody learns from a position of comfort.
  2. Scaling Feedback. Without burdening faculty to assign and grade more student work, Check My Activity can amplify the feedback effect of existing assignments, especially before a term’s add/drop date when students need to self-assess their likelihood of success.
  3. Course Design. A recent predictive modeling pilot that was 87% accurate by week 4 of the FA16 term showed that faculty use of the LMS grade book was the most important factor in predicting UMBC students who earned a C or better final grade in Fall 2016. These findings echo research by John Whitmer at Blackboard, who found that grade-checking behavior is the most consistent predictor of student achievement.

Learning management systems leave data trails akin to “classroom walls that talk” about who’s engaged during a given term, when it’s not too late for a change in student behavior to make a difference. If faculty assign and grade student work, tools like Check My Activity can help nudge students into a greater sense of awareness and responsibility for their own learning. True, we can only lead a horse to water, but as Maryellen Weimer recalls a colleague’s twist of this familiar metaphor, “the horse who has had salt put in his oats does not have to be forced to drink. He is thirsty, knows he is thirsty, and is looking for water.” Nudging students with and about their own learning data, especially in the context of higher performing peers they wish to emulate, may be one of the most scalable ways we can salt the horse’s oats.

Download the full report: The impact of student-facing dashboards

Further Reading

Bandura, A. (1986). Social foundations of thought at action: A social cognitive theory. Englewoold Cliffs, N.J.: Prentice-Hall.

Bandura, A. (1997). Self-efficacy: The exercise of control. Macmillan.

Fritz, J. (2016). Using analytics to encourage student responsibility for learning and identify course designs that help (Ph.D.). University of Maryland, Baltimore County, United States — Maryland. Retrieved from http://umbc.box.com/johnfritzdissertation.

Weimer, M. (2002). Learner-centered teaching: Five key changes to practice. John Wiley & Sons.

Zimmerman, B. J., Bandura, A., & Martinez-Pons, M. (1992). Self-motivation for academic attainment: The role of self-efficacy beliefs and personal goal setting. American Educational Research Journal, 29(3), 663–676. https://doi.org/10.3102/00028312029003663

Zimmerman, B. J., & Schunk, D. H. (2001). Self-regulated learning and academic achievement: Theoretical perspectives. Routledge.

 

Related Posts

Share This Article

Twitter Facebook LinkedIn Pinterest Email