TY - GEN
T1 - On the likes and dislikes of Youtube's educational videos
T2 - 18th Annual Conference on Information Technology Education, SIGITE 2017
AU - Shoufan, Abdulhadi
AU - Mohamed, Fatma
N1 - Publisher Copyright:
© 2017 Association for Computing Machinery.
PY - 2017/9/27
Y1 - 2017/9/27
N2 - As major product of information technology, YouTube is a ubiquitous source for education, also in the field of information technology. Learners, however, are facing the increasing problem of finding appropriate videos on YouTube efficiently. Users' rating in terms of Likes and Dislikes could provide a starting point. However, it is unclear what the number of Likes and Dislikes reveal about the video. This paper tries to create links between different video features and users' rating of YouTube's educational content. For this purpose, 300 educational videos were collected and analyzed and regression models were established that describe the number of Likes per view and the number of Dislikes per view as functions of different video features and production styles. Results show that the number of Likes per view can be predicted more reliably than the number of Dislikes per view. The number of Likes per view increases with higher video resolution and higher talking rate (words per second), and when the instructor or tutor speaks English as a native language. Videos using explanations on paper or whiteboard as well as videos that use more than one style attract more Likes per view. In contrast, the model that describes the number of Dislikes per view has a low adjusted R-squared and the contribution of its significant variables is rather difficult to interpret. This suggests that further research is required to understand users' behavior in terms of disliking an educational video.
AB - As major product of information technology, YouTube is a ubiquitous source for education, also in the field of information technology. Learners, however, are facing the increasing problem of finding appropriate videos on YouTube efficiently. Users' rating in terms of Likes and Dislikes could provide a starting point. However, it is unclear what the number of Likes and Dislikes reveal about the video. This paper tries to create links between different video features and users' rating of YouTube's educational content. For this purpose, 300 educational videos were collected and analyzed and regression models were established that describe the number of Likes per view and the number of Dislikes per view as functions of different video features and production styles. Results show that the number of Likes per view can be predicted more reliably than the number of Dislikes per view. The number of Likes per view increases with higher video resolution and higher talking rate (words per second), and when the instructor or tutor speaks English as a native language. Videos using explanations on paper or whiteboard as well as videos that use more than one style attract more Likes per view. In contrast, the model that describes the number of Dislikes per view has a low adjusted R-squared and the contribution of its significant variables is rather difficult to interpret. This suggests that further research is required to understand users' behavior in terms of disliking an educational video.
KW - Dislikes
KW - Likes
KW - User behavior
KW - Video features
KW - Video production style
UR - http://www.scopus.com/inward/record.url?scp=85037148768&partnerID=8YFLogxK
U2 - 10.1145/3125659.3125692
DO - 10.1145/3125659.3125692
M3 - Conference contribution
AN - SCOPUS:85037148768
T3 - SIGITE 2017 - Proceedings of the 18th Annual Conference on Information Technology Education
SP - 127
EP - 132
BT - SIGITE 2017 - Proceedings of the 18th Annual Conference on Information Technology Education
Y2 - 4 October 2017 through 7 October 2017
ER -