White Paper: Improving Student Risk Predictions: Assessing the Impact of Learning Data Sources


Authors: Diego Forteza, Blackboard | John Whitmer, Ed.D., Blackboard | John Fritz, Ph.D., University of Maryland, Baltimore County (UMBC) | Daniel Green, VitalSource

Using IMS Caliper Analytics® with Blackboard Learn & VitalSource at the University of Maryland Baltimore County


Educators are increasingly using multiple systems to provide technology-enhanced learning experiences for students (e.g., LMS, eTextbooks, clickers, etc.). Each of these systems creates a data stream that describes user activity. When student engagement data from these systems are combined with conventional student demographic data from a Student Information System, it becomes possible to investigate complex trends in the relationship between student background and learning behaviors that can impact their outcomes.

Better understanding these relationships enables institutions to take actions to help students, whether those are by changing student behaviors or revising the underlying learning experiences. We frequently hear from institutions that in order to “do” learning analytics, we need to have data from all of these systems. If not, it is argued, we will only have partial understandings of student activity that could be misleading. For example, if a student appeared to not log into Blackboard Learn, they might be choosing to use their study time working with publisher resources, which could be a better use of their time. Intuitively, this idea is reasonable—but to our knowledge, the added value of data from multiple system sources hasn’t been demonstrated empirically.

In this era of “big data,” there’s often an assumption that more is better, but what is the actual difference to our understanding and prediction of student outcomes? Is it worth the extra effort and resources required to integrate and analyze this data?

Click the link below to read the complete white paper.                                                                                                                                                               



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