Auto Analysis System of Students Behavior in MOOC Teaching

被引：0
|
作者：
Dai Y.-P. [1 ,2 ]
Yang F.-F. [1 ,2 ]
Zhao H.-Y. [3 ]
Jia Z.-Y. [1 ,2 ]
Hirota K. [1 ,2 ]
机构：
[1] School of Automation, Beijing Institute of Technology, Beijing
[2] National Laboratory of Intelligent Control and Decision of Complex Systems, Beijing
[3] The 6th Research Institute of China Electronics Corporation, Beijing
来源：
Zidonghua Xuebao/Acta Automatica Sinica | 2020年 / 46卷 / 04期
关键词：
Decision fusion; Feature extraction; Massive open online course (MOOC); Student attention modeling;
D O I：
10.16383/j.aas.c170416
中图分类号：
学科分类号：
摘要：
Aiming at solving the problems of students learning behavior tracking and instructors teaching evaluation in massive open online course (MOOC), a modeling approach of student attention is proposed first, then an automatic behavior analysis and decision making fusion algorithm (ABA) is proposed to evaluate the concentration of the students during lectures. The proposed method can effectively track the student' learning state and acquire the characteristic parameters of the student, and then give the concentration evaluation of the student after data fusion and decision making. Multiple experiments are carried out using the approach proposed in this paper, the results show that the proposed method can effectively reduce the uncertainty in student behavior decision making. Copyright © 2020 Acta Automatica Sinica. All rights reserved.
引用
收藏
页码：681 / 694
页数：13
相关论文
共 20 条
  • [1] Wong K., Patzelt M., Poulette B., Hathaway R., Scenario-based learning in a MOOC specialization capstone on software product management, Proceedings of the IEEE/ACM 39th International Conference on Software Engineering Companion (ICSE-C), pp. 317-318, (2017)
  • [2] Staubitz T., Willems C., Hagedorn C., Meinel C., The gamification of a MOOC platform, Proceedings of the 2017 IEEE Global Engineering Education Conference (EDUCON), pp. 383-892, (2017)
  • [3] Lei C.U., Yeung Y.C.A., Kwok T.T.O., Lau R., Ang A., Leveraging videos and forums for small-class learning experience in a MOOC environment, Proceedings of the 2016 IEEE International Conference on Teaching, Assessment, and Learning for Engineering (TALE), pp. 409-411, (2016)
  • [4] Luo H., Millet A., Alley R., Zuo M., Dealing with ethical issues in MOOC design and delivery: a case study, Proceedings of the 2016 Blended Learning: Aligning Theory with Practices. ICBL 2016. Lecture Notes in Computer Science, 9757, pp. 128-138, (2016)
  • [5] Kearney R.C., Premaraj S., Smith B.M., Olson G.W., Williamson A.E., Romanos G., Massive open online courses in dental education: two viewpoints: viewpoint 1: massive open online courses offer transformative technology for dental education and viewpoint 2: massive open online courses are not ready for primetime, Journal of Dental Education, 80, 2, pp. 121-127, (2016)
  • [6] Brinton C.G., Buccapatnam S., Wong F.M.F., Chiang M., Poor H.V., Social learning networks: efficiency optimization for MOOC forums, Proceedings of the IEEE INFOCOM 2016-The 35th Annual IEEE International Conference on Computer Communications, pp. 1-9, (2016)
  • [7] Jaouedi N., Boujnah N., Htiwich O., Bouhlel M.S., Human action recognition to human behavior analysis, Proceedings of the 7th International Conference on Sciences of Electronics, Technologies of Information and Telecommunications (SETIT), pp. 263-266, (2016)
  • [8] Xiao B., Georgiou P., Baucom B., Narayanan S.S., Head motion modeling for human behavior analysis in dyadic interaction, IEEE Transactions on Multimedia, 17, 7, pp. 1107-1119, (2015)
  • [9] Tsai H.C., Chuang C.H., Tseng S.P., Wang J.F., The optical flow-based analysis of human behavior-specific system, Proceedings of the 1st International Conference on Orange Technologies (ICOT), pp. 214-218, (2013)
  • [10] Batchuluun G., Kim J.H., Hong H.G., Kang J.K., Park K.R., Fuzzy system based human behavior recognition by combining behavior prediction and recognition, Expert Systems with Applications, 81, pp. 108-133, (2017)
12