Automated Analysis of Lecture Video Engagement Using Student Posts
This work explores the feasibility of a learning analytic that would provide high level engagement data to instructors based on students’ text artifacts in online learning systems. Student posts from an online lecture video system were collected and manually coded by engagement using the ICAP framework. Analyses show what features are most indicative of engagement and the performance of using a neural network to classify posts by engagement.
This work is funded by National Science Foundation grant IIS-1318345. Any opinions, findings, or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the NSF.
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