Big data analytics for MOOC video watching behavior based on Spark

被引：25
|
作者：
Hu, Hui [1 ,2 ]
Zhang, Guofeng [1 ,2 ]
Gao, Wanlin [1 ,2 ]
Wang, Minjuan [1 ,2 ]
机构：
[1] China Agr Univ, Coll Informat & Elect Engn, Beijing 100083, Peoples R China
[2] China Agr Univ, Key Lab Agr Informatizat Standardizat, Minist Agr & Rural Affairs, Beijing 100083, Peoples R China
来源：
NEURAL COMPUTING & APPLICATIONS | 2020年 / 32卷 / 11期
关键词：
MOOC; Big data; Video watching behavior; Spark;
D O I：
10.1007/s00521-018-03983-z
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
The purpose of this study is to measure the effectiveness of courses delivered using MOOCs in China Agricultural University. Video watching is considered to be the most important way to disseminate knowledge in Massive Open Online Course (MOOC). Its mission is to understand the degree of students' learning engagement and to provide suggestions for teachers to construct courses. This paper proposes the analysis methods of students' video watching behavior in MOOCs platform and verifies it with the data of the cauX platform. Initially, a detailed statistical analysis of video watching data and behavior was performed. Later, data preprocessing algorithms based on Spark platform were developed and used to calculate the number of video watching behaviors in every hour and every minute. Then, the entropy weight method was used to calculate the weight of pause video, seek video and speed change video. Finally, we analyze and discuss the results of experiment. The results show that the proposed method based on Spark platform can quickly and accurately analyze the characteristics of video watching behavior.
引用
收藏
页码：6481 / 6489
页数：9
相关论文
共 50 条
  • [21] Big data analytics for behavior monitoring of students
    Baig, Abdul Rauf
    Jabeen, Hajira
    4TH SYMPOSIUM ON DATA MINING APPLICATIONS (SDMA2016), 2016, 82 : 43 - 48
  • [22] Big Data Analytics and Management Forecasting Behavior
    Goh, Beng Wee
    Li, Na
    Ranasinghe, Tharindra
    ACCOUNTING HORIZONS, 2024, 38 (03) : 59 - 76
  • [23] Mining MOOC Clickstreams: Video-Watching Behavior vs. In-Video Quiz Performance
    Brinton, Christopher G.
    Buccapatnam, Swapna
    Chiang, Mung
    Poor, H. Vincent
    IEEE TRANSACTIONS ON SIGNAL PROCESSING, 2016, 64 (14) : 3677 - 3692
  • [24] An insight into tree based machine learning techniques for big data Analytics using Apache Spark
    Sheshasaayee, Ananthi
    Lakshmi, J. V. N.
    2017 INTERNATIONAL CONFERENCE ON INTELLIGENT COMPUTING, INSTRUMENTATION AND CONTROL TECHNOLOGIES (ICICICT), 2017, : 1740 - 1743
  • [25] Big data Predictive Analytics for Apache Spark using Machine Learning
    Junaid, Muhammad
    Wagan, Shiraz Ali
    Qureshi, Nawab Muhammad Faseeh
    Nam, Choon Sung
    Shin, Dong Ryeol
    2020 GLOBAL CONFERENCE ON WIRELESS AND OPTICAL TECHNOLOGIES (GCWOT), 2020,
  • [26] Benchmarking of Distributed Computing Engines Spark and GraphLab for Big Data Analytics
    Wei, Jian
    Chen, Kai
    Zhou, Yi
    Zhou, Qu
    He, Jianhua
    PROCEEDINGS 2016 IEEE SECOND INTERNATIONAL CONFERENCE ON BIG DATA COMPUTING SERVICE AND APPLICATIONS (BIGDATASERVICE 2016), 2016, : 10 - 13
  • [27] Mobile Big Data Analytics Using Deep Learning and Apache Spark
    Abu Alsheikh, Mohammad
    Niyato, Dusit
    Lin, Shaowei
    Tan, Hwee-Pink
    Han, Zhu
    IEEE NETWORK, 2016, 30 (03): : 22 - 29
  • [28] Spark versus Flink: Understanding Performance in Big Data Analytics Frameworks
    Marcu, Ovidiu-Cristian
    Costan, Alexandra
    Antoniu, Gabriel
    Perez-Hernandez, Maria S.
    2016 IEEE INTERNATIONAL CONFERENCE ON CLUSTER COMPUTING (CLUSTER), 2016, : 433 - 442
  • [29] Big Data is watching youBig data is watching you
    A. Larena-Avellaneda
    Gefässchirurgie, 2019, 24 (3) : 216 - 217
  • [30] Hierarchical Video Surveillance Architecture - A Chassis for Video Big Data Analytics and Exploration
    Ajiboye, Sola O.
    Birch, Philip
    Chatwin, Christopher
    Young, Rupert
    VIDEO SURVEILLANCE AND TRANSPORTATION IMAGING APPLICATIONS 2015, 2015, 9407
12345