Estimating the cognitive value of YouTube's educational videos: A learning analytics approach. (March 2019)

• Record Type: Journal Article
• Authors: Shoufan, Abdulhadi
• Is Part Of: Computers in human behavior. Volume 92(2019)
• Page Start: 450
• Page End: 458
• Publication Date: 2019-03
• Subjects: Learning analytics -- Educational videos -- Cognitive theory of multimedia learning -- Youtube; Interactive computer systems -- Periodicals; Man-machine systems -- Periodicals; 004.019
• DOI: 10.1016/j.chb.2018.03.036
• Languages: English
• ISSNs: 0747-5632

Abstract

Abstract: The literature provides some evidence for the effectiveness of online educational videos in students' learning. Also, general features of educational videos such as production style and video length were investigated to some extent. However, the actual cognitive features of educational videos available on the Internet have remained almost untreated, so far. The main goal of this study is to use learning analytics to investigate how far educational videos on YouTube support cognitive features –as formulated in the cognitive theory of multimedia learning– and how far these features support students' learning. We measure the Video Cognitive Value ( V C V ) by viewers' rating, particularly using the number of Likes based on a survey that we conducted to understand the semantics of Likes and Dislikes of YouTube's educational videos. Then, a sample of 105 videos was collected and analyzed with respect to cognitive features. A regression analysis showed that only four out of ten investigated features are significant for V C V (pretraining, modality, spatial contiguity, and embodiment) and the regression model could only explain 63% of the data variance. Further tests were performed to include other factors such as the video production style, the video length, the talking speed, the gender of the speaker, and whether she/he speaks English as native language. The expanded model showed only a slightly improved adjusted R-square value (68%). These results suggest that further research is required to identify and specify additional cognitive features in educational videos towards deeper understanding of the video cognitive value. Highlights: Principles of Cognitive Theory of Multimedia Learning are only partially significant for online videos'cognitive value. Content understanding seems to be the most relevant reason for liking an educational video on YouTube. The speaker's gender is not relevant for liking or disliking an educational video on YouTube. Videos by native speakers or with higher talking rates are more likely to be liked. Disliking YouTube's educational videos seems to be arbitrary and useless for analytics.