Learner Control in Online Environments: Understanding Learner Behaviors via Learning Analytics

• In Event: Understanding and Using Learner Data in Technology Environments

Abstract

The effect of learner control is not fully understood. This study examined the connection between learner-controlled features and learning behaviors in a fully online course using analytics of students’ usage data collected in fall 2014 and summer 2015. Students’ usages of content pages were extracted from the backend database of Blackboard Learn 9.1, the learning management system (LMS). A few data mining techniques were applied and the learning analytics further identified three types of video users —adaptive viewer, self-regulating viewer, and infrequent viewer. Student learning satisfaction and academic achievement were statistically significantly different among these three groups. These findings highlight the usefulness of learning analytics as an objective measure of learner behavior in online environments.

Authors

• Ying-Ying Kuo, George Mason University

• Juan Luo

• Jennifer Brielmaier