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- W2896905057 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 <mml:math xmlns:mml=http://www.w3.org/1998/Math/MathML id=M1><mml:mrow><mml:mi>R</mml:mi></mml:mrow></mml:math> (<mml:math xmlns:mml=http://www.w3.org/1998/Math/MathML id=M2><mml:mi>R</mml:mi><mml:mo>=</mml:mo><mml:mrow><mml:mrow><mml:mi>A</mml:mi><mml:mi>v</mml:mi><mml:mi>e</mml:mi><mml:mi>r</mml:mi><mml:mi>a</mml:mi><mml:mi>g</mml:mi><mml:mi>e</mml:mi><mml:mo> </mml:mo><mml:mo> </mml:mo><mml:mi>v</mml:mi><mml:mi>i</mml:mi><mml:mi>e</mml:mi><mml:mi>w</mml:mi><mml:mi>i</mml:mi><mml:mi>n</mml:mi><mml:mi>g</mml:mi><mml:mo> </mml:mo><mml:mo> </mml:mo><mml:mi>d</mml:mi><mml:mi>u</mml:mi><mml:mi>r</mml:mi><mml:mi>a</mml:mi><mml:mi>t</mml:mi><mml:mi>i</mml:mi><mml:mi>o</mml:mi><mml:mi>n</mml:mi></mml:mrow><mml:mo>/</mml:mo><mml:mrow><mml:mi>V</mml:mi><mml:mi>i</mml:mi><mml:mi>d</mml:mi><mml:mi>e</mml:mi><mml:mi>o</mml:mi><mml:mo> </mml:mo><mml:mo> </mml:mo><mml:mi>l</mml:mi><mml:mi>e</mml:mi><mml:mi>n</mml:mi><mml:mi>g</mml:mi><mml:mi>t</mml:mi><mml:mi>h</mml:mi></mml:mrow></mml:mrow></mml:math>), which reflects the popularity of the video. By analyzing the relationship between the video length, release time, and <mml:math xmlns:mml=http://www.w3.org/1998/Math/MathML id=M3><mml:mrow><mml:mi>R</mml:mi></mml:mrow></mml:math>, we found a significant negative linear correlation between video length and <mml:math xmlns:mml=http://www.w3.org/1998/Math/MathML id=M4><mml:mrow><mml:mi>R</mml:mi></mml:mrow></mml:math> and video release time and <mml:math xmlns:mml=http://www.w3.org/1998/Math/MathML id=M5><mml:mrow><mml:mi>R</mml:mi></mml:mrow></mml:math>. However, when the number of videos is less than the threshold, the release time has less influence on <mml:math xmlns:mml=http://www.w3.org/1998/Math/MathML id=M6><mml:mrow><mml:mi>R</mml:mi></mml:mrow></mml:math>. 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." @default.
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- W2896905057 date "2018-10-16" @default.
- W2896905057 modified "2023-09-24" @default.
- W2896905057 title "A MOOC Video Viewing Behavior Analysis Algorithm" @default.
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- W2896905057 doi "https://doi.org/10.1155/2018/7560805" @default.
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