Abstract
MOOCs (massive open online courses) are developing rapidly, but they also face many problems. As the MOOC’s most important resource, the course videos have a very important influence on the learning. This article defines the ratio R (R=Average viewing duration/Video length), which reflects the popularity of the video. By analyzing the relationship between the video length, release time, and R, we found a significant negative linear correlation between video length and R and video release time and R. However, when the number of videos is less than the threshold, the release time has less influence on R. This paper presents a video viewing behavior analysis algorithm based on multiple linear regression. The residual independence test proved that the algorithm has a good approximation to the data. It can predict the popularity of similar course videos to help producers optimize video design.
Highlights
MOOCs [1] are the massive open online courses, and these courses are usually provided by university and shared in the network [2, 3]
Our research shows that there are significant negative linear correlations between R and video length, and between R and release time
Because the absolute value of the correlation coefficient represents the strength of correlation, the video length has a stronger linear correlation with R than the video number
Summary
MOOCs [1] are the massive open online courses, and these courses are usually provided by university and shared in the network [2, 3]. Tanmay Sinha [8] operationalizes video lecture clickstreams of students into cognitively plausible higher-level behaviors Their results illustrate how such a metric inspired by cognitive psychology can help answer critical questions regarding students engagement, their future click interactions, and participation trajectories that lead to in-video and course dropouts. Through data analysis, they found the peak period for students to think about issues. Qing Chen et al [13] introduce a comprehensive visualization system called Peak Vizor This system enables course instructors and education experts to analyze the peaks or the video segments that generate numerous clickstreams. These studies provided many analysis methods for video viewing behavior and proposed some options to optimize video design. The algorithm proposed in this paper can predict the attractiveness video and help providers optimize video segmentation
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