Understanding in-video dropouts and interaction peaks in online lecture videos

Understanding in-video dropouts and interaction peaks in online lecture videos

With thousands of learners watching the same online lecture videos, analyzing video watching patterns provides a unique opportunity to understand how students learn with videos. This paper reports a large-scale analysis of in-video dropout and peaks in viewership and student activity, using second-b...

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Bibliographic Details
Main Authors: Guo, Philip J. (Author), Seaton, Daniel T. (Contributor), Mitros, Piotr (Author), Gajos, Krzysztof Z. (Author), Miller, Robert C. (Contributor), Kim, Ju Ho (Contributor)
Other Authors: Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory (Contributor), Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science (Contributor), Massachusetts Institute of Technology. Office of Digital Learning (Contributor)
Format: Article
Language:English
Published: Association for Computing Machinery (ACM), 2014-09-26T18:23:46Z.
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