Apples to apples: Differences in viewer retention when longer content is chopped into smaller bites

Jennifer S. Kay, Colin Lambe, Tyler J. Nolan, Thomas M. Grello, Anthony Breitzman

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Numerous studies have concluded that viewer retention decreases as video length increases. However, we are not aware of any prior work in which a set of longer MOOC (Massive Open Online Course) videos are compared with the same content split into multiple shorter videos. We are fortunate to be in the unique position to have two separate MOOCs that teach essentially the same content using two different platforms (the LEGO Mindstorms NXT and EV3 robots). In our NXT MOOC, videos are quite long, with over 20% of the videos having a running time of more than ten minutes. The EV3 MOOC has very similar content; EV3 MOOC scripts were written by modifying NXT scripts as appropriate. However, many of the EV3 lessons were split into two or three shorter videos in place of a single longer one. NXT videos that are very close in terms of both content and duration to EV3 videos have similar average percentage viewed. This suggests that the two populations watching the videos are similar and that we have a promising setup for analyzing the relationship between NXT lessons whose EV3 counterparts consist of multiple shorter videos. We present an analysis of our data, along with various interpretations some, but not all, of which support the “shorter videos are better” hypothesis.

Original languageEnglish (US)
Title of host publicationProceedings of the 6th 2019 ACM Conference on Learning at Scale, L@S 2019
PublisherAssociation for Computing Machinery, Inc
ISBN (Electronic)9781450368049
DOIs
StatePublished - Jun 24 2019
Event6th ACM Conference on Learning at Scale, L@S 2019 - Chicago, United States
Duration: Jun 24 2019Jun 25 2019

Publication series

NameProceedings of the 6th 2019 ACM Conference on Learning at Scale, L@S 2019

Conference

Conference6th ACM Conference on Learning at Scale, L@S 2019
CountryUnited States
CityChicago
Period6/24/196/25/19

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Science Applications
  • Computer Networks and Communications
  • Education

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