TY - GEN
T1 - Probabilistic Modeling Towards Understanding the Power Law Distribution of Video Viewing Behavior in Large-Scale e-Learning
AU - Xue, Ni
AU - He, Huan
AU - Liu, Jun
AU - Zheng, Qinghua
AU - Ma, Tian
AU - Ruan, Jianfei
AU - Dong, Bo
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/12/2
Y1 - 2015/12/2
N2 - In the era of internet, e-Learning has become vastly widespread and generated huge amount of log data of video viewing behavior. Through analyzing and mining these log data, significant Power Law Distribution (PLD) of viewing behavior is observed, which is different from small-scale e-Learning or traditional classroom environment. In this paper, we apply the mechanisms for generating the PLDs in analyzing log data of a large-scale e-Learning platform to discover the factors influencing the video viewing behavior. Firstly, four factors correlated to the video viewing behavior are discovered from log data, including the number of videos viewed, the start date of viewing videos, the date of final exam, and the duration of enrollment. Furthermore, we present a probabilistic model of viewing behavior based on the four factors. Finally, the accuracy of the model is validated with nine online courses in which each course enrolled more than 1,000 students. In addition, we analyze the application of the proposed model and provide some valuable suggestions for teachers to improve the performance of students.
AB - In the era of internet, e-Learning has become vastly widespread and generated huge amount of log data of video viewing behavior. Through analyzing and mining these log data, significant Power Law Distribution (PLD) of viewing behavior is observed, which is different from small-scale e-Learning or traditional classroom environment. In this paper, we apply the mechanisms for generating the PLDs in analyzing log data of a large-scale e-Learning platform to discover the factors influencing the video viewing behavior. Firstly, four factors correlated to the video viewing behavior are discovered from log data, including the number of videos viewed, the start date of viewing videos, the date of final exam, and the duration of enrollment. Furthermore, we present a probabilistic model of viewing behavior based on the four factors. Finally, the accuracy of the model is validated with nine online courses in which each course enrolled more than 1,000 students. In addition, we analyze the application of the proposed model and provide some valuable suggestions for teachers to improve the performance of students.
KW - factors influencing video viewing behavior
KW - large-scale e-Learning
KW - log data
KW - power law distribution
KW - probabilistic modeling of viewing behavior
UR - https://www.scopus.com/pages/publications/84969256742
U2 - 10.1109/Trustcom.2015.572
DO - 10.1109/Trustcom.2015.572
M3 - 会议稿件
AN - SCOPUS:84969256742
T3 - Proceedings - 14th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, TrustCom 2015
SP - 136
EP - 142
BT - Proceedings - 9th IEEE International Conference on Big Data Science and Engineering, BigDataSE 2015
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 14th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, TrustCom 2015
Y2 - 20 August 2015 through 22 August 2015
ER -