Case Study Reviews (2 of 2)

This is the second in a two-part series reviewing case studies of learning analytics.  The examples in this and the previous post come directly from the LAK 2013 online course by George Seimens.  The purpose of these posts is to look more closely at the kind of data being used and the ways these data are being used.

Building a Purpose Network to Increase Student Engagement and Retention
Purpose: Improve Retention
How: Increasing student engagement, parent engagement, tracking success measures
Data: (from picture) late to class, missed assignments, absence, illness/fatigue, substance abuse, other

This system provides dashboards and comparisons grouped by course, instructor, year in school. Also, this system tracks action plans and recurrence of issues.  I can't help but think that most intervention-focused systems, such as this one, implement intervention outside of the instructor.  This may not be a bad thing, but it limits the effectiveness of the intervention to generalizable academic skills or responsibility or social scaffolds, which are different from the academic experience the student has with the learning content. 



Efficiencies, Learning Outcomes Bolstered by Analytics, Data-Informed Decision Making
Purpose: Improve scheduling (room resources), success rate for Math 010, course offerings,
Data: Student enrollment (by course), success rate for Math 010, career interest data, transferability (to other schools)

This article is a list of 3 or 4 efforts of applying data to making school-level decisions--how to allocate campus resources more effectively, how to improve the success rate of Math 010, how to align course offerings to meet students' transfer objectives.


Improving Retention by Identifying and Supporting "At-Risk" Students
Purpose: Identify, support "at-risk" students
Data: Virtual Learning Environment(VLE) for online course provides interesting data from students

This kind of data is relatively new compared to a traditional classroom.  This data affords new measures of engagement for the student.  Interestingly, the recommendation and observation is that comparing students to a single engagement profile is not as effective at identifying risk because one student may have a high engagement profile in the VLE, but may not be learning as well as other students who measure low on VLE activity, but score high in the course.  Thus, student VLE activity should be compared to previous VLE activity for that student.  But the article also notes that different modules in the course will also affect the student's VLE activity.  Overall, the application introduces new data that is harder to obtain in traditional face-to-face classes, and provides initial recommendations for how to use that data to identify at-risk students.