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dc.contributor.authorGuo, Philip J.
dc.contributor.authorSeaton, Daniel T.
dc.contributor.authorMitros, Piotr
dc.contributor.authorGajos, Krzysztof Z.
dc.contributor.authorMiller, Robert C.
dc.contributor.authorKim, Ju Ho
dc.date.accessioned2014-09-26T18:23:46Z
dc.date.available2014-09-26T18:23:46Z
dc.date.issued2014-03
dc.identifier.isbn9781450326698
dc.identifier.urihttp://hdl.handle.net/1721.1/90413
dc.description.abstractWith 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-by-second user interaction data from 862 videos in four Massive Open Online Courses (MOOCs) on edX. We find higher dropout rates in longer videos, re-watching sessions (vs first-time), and tutorials (vs lectures). Peaks in re-watching sessions and play events indicate points of interest and confusion. Results show that tutorials (vs lectures) and re-watching sessions (vs first-time) lead to more frequent and sharper peaks. In attempting to reason why peaks occur by sampling 80 videos, we observe that 61% of the peaks accompany visual transitions in the video, e.g., a slide view to a classroom view. Based on this observation, we identify five student activity patterns that can explain peaks: starting from the beginning of a new material, returning to missed content, following a tutorial step, replaying a brief segment, and repeating a non-visual explanation. Our analysis has design implications for video authoring, editing, and interface design, providing a richer understanding of video learning on MOOCs.en_US
dc.language.isoen_US
dc.publisherAssociation for Computing Machinery (ACM)en_US
dc.relation.isversionofhttp://dx.doi.org/10.1145/2556325.2566237en_US
dc.rightsCreative Commons Attribution-Noncommercial-Share Alikeen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/en_US
dc.sourceOther univ. web domainen_US
dc.titleUnderstanding in-video dropouts and interaction peaks in online lecture videosen_US
dc.typeArticleen_US
dc.identifier.citationJuho Kim, Philip J. Guo, Daniel T. Seaton, Piotr Mitros, Krzysztof Z. Gajos, and Robert C. Miller. 2014. Understanding in-video dropouts and interaction peaks inonline lecture videos. In Proceedings of the first ACM conference on Learning @ scale conference (L@S '14). ACM, New York, NY, USA, 31-40.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratoryen_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Scienceen_US
dc.contributor.departmentMassachusetts Institute of Technology. Office of Digital Learningen_US
dc.contributor.mitauthorKim, Ju Hoen_US
dc.contributor.mitauthorSeaton, Daniel T.en_US
dc.contributor.mitauthorMiller, Robert C.en_US
dc.relation.journalProceedings of the first ACM conference on Learning @ scale conference (L@S '14)en_US
dc.eprint.versionAuthor's final manuscripten_US
dc.type.urihttp://purl.org/eprint/type/ConferencePaperen_US
eprint.statushttp://purl.org/eprint/status/NonPeerRevieweden_US
dspace.orderedauthorsKim, Juho; Guo, Philip J.; Seaton, Daniel T.; Mitros, Piotr; Gajos, Krzysztof Z.; Miller, Robert C.en_US
dc.identifier.orcidhttps://orcid.org/0000-0001-6348-4127
dc.identifier.orcidhttps://orcid.org/0000-0002-0442-691X
mit.licenseOPEN_ACCESS_POLICYen_US


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