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Modelling and Statistical Analysis of YouTube’s Educational Videos:A Channel Owner’s Perspective

17 Sep 2018

Samant Saurabh and Sanjana Gautam, "Modelling and Statistical Analysis of YouTube’s Educational Videos: A Channel Owner’s Perspective", Computers & Education, Elsevier (5 year impact factor: 5.568)

YouTube is one of the most popular websites. It is a vast resource for educational content. To better understand the characteristics and impact of YouTube on education, we have analyzed a popular YouTube educational channel owned by the author of this paper. It has thousands of subscribers, millions of views, and hundreds of video lectures. We have used our private YouTube analytics data to provide an in-depth study of YouTube educational videos. Our analysis provides valuable information that can have major technical and commercial implications in the field of education. We perform in-depth time-series analysis of the channel data to reveal the trend, seasonality and temporal pattern for the educational videos on YouTube. In our study, we find the relationship between video uploading activity, channel’s age and its popularity. We use an entropy-based decision tree classifier to find the features that are most important for the popularity of videos. We show that video rank and number of views follow the Zipf distribution for educational videos. We observe a strong correlation between the geographical location of viewers and the location of industry the channel caters to. Besides, we also provide knowledge regarding the popular devices and operating systems used for viewing the educational videos, main traffic sources, playback locations, translation activity, and demography of viewers. Overall, we believe that the results presented in this paper are crucial in understanding YouTube EDU videos characteristics which can be utilized for making well-informed decisions for improving educational content and learning technologies. The image depicts the seasonal decomposition of the time series data for the views per day of the channel. It shows the trend, seasonality or periodicity of data and the randomness involved in the time series.