Knowledge Gap Extraction Based on Learner Interaction with Training Videos

Document Type : Original Article

Authors

School of Electrical and Computer Engineering, University of Tehran, Tehran, Iran

Abstract

In recent years, with the advancement of information technology in education, e-learning quality promotion has received increased attention. Numerous criteria exist for promoting learning quality, such as fitness for purpose, which refers to the extent to which service fits its intended purpose. Multiple purposes are considered in e-learning. One is reducing the knowledge gap between the learner’s perception of educational concepts and what should be understood of training concepts. Identifying and calculating the learner’s knowledge gap is the first step in reducing the knowledge gap. Consequently, this paper presents a new method for calculating the learner’s knowledge gap concerning each concept in the training video content based on the learner’s click behavior. The association between the learner’s knowledge gap and click behavior was determined by categorizing the learner’s click behaviors. Similarly, the Apriori algorithm extracted rules for each behavioral category. The results demonstrated that learning outcome correlated with the learner’s click behavior. Therefore, four behavioral rules regarding the compatibility between the knowledge gap and learner’s click behavior are presented. Experiments were performed by 52 students enrolled in the micro-processing course at Tehran University’s e-Learning Center.  

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Main Subjects


  • G. Brinton, “Technology and Pedagogy: Using Big Data to Enhance Student Learning”, ProQuest LLC. 2016.
  • Lin, C. Liu, Y. Li, L. Cui, R. Wang, X. Lu, Y. Zhang, and J. Lian. “Automatic Knowledge Discovery in Lecturing Videos via Deep Representation”, IEEE Access, vol. 7, pp. 33957-63, 2019.
  • J. Lemay and T. Doleck, “Grade prediction of weekly assignments in MOOCS: mining video-viewing behavior”, Education and Information Technologies, vol. 25, no. 2, pp. 1333-1342, 2020.
  • Hu, G. Zhang, W. Gao, and M. Wang, “Big data analytics for MOOC video watching behavior based on Spark”, Neural Computing and Applications, vol. 32, no. 11, pp. 6481-6489, 2020.
  • Belarbi, N. Chafiq, M. Talbi, A. Namir, and E. Benlahmar, “User profiling in a SPOC: A method based on user video clickstream analysis”, International Journal of Emerging Technologies in Learning (iJET), vol. 14, no. 1, pp. 110-124, 2019.
  • C. Goulden, E. Gronda, Y. Yang, Z. Zhang, J. Tao, C. Wang, X. Duan, G. A. Ambrose, K. Abbott, and P. Miller, “CCVis: Visual analytics of student online learning behaviors using course clickstream data”, IS&T International Symposium on Electronic Imaging Conference, 2019.
  • Kadi, A. Idri, and J. L. Fernandez-Aleman, “Knowledge discovery in cardiology: A systematic literature review”, International journal of medical informatics, vol. 97, pp. 12-32, 2017.
  • Patil, V. Gaikwad, D. Pawar, and M. Nikam, “Intelligence Extraction Using Machine Learning Technics”, Intelligence. Vol. 6, no. 6, pp. 35-37, 2019.
  • Gu, B. Ma, H. Chang, S. Shan, and X. Chen, “Temporal knowledge propagation for image-to-video person re-identification”, In Proceedings of the IEEE International Conference on Computer Vision, 2019, pp. 9647-9656.
  • Yoo, M. Cho, T. Kim, and U. Kang, “Knowledge extraction with no observable data”, In Advances in Neural Information Processing Systems, 2019, pp. 2705-2714.
  • H. Kay, “Exploring the use of video podcasts in education: A comprehensive review of the literature”, Computers in Human Behavior, vol. 28, no. 3, pp. 820–831, 2012. doi:10.1016/j.chb.2012.01.011,2012.
  • N. Giannakos, L. Jaccheri, and J. Krogstie, “Exploring the relationship between video lecture usage patterns and students’ attitudes”, British Journal of Educational Technology, vol. 47, no. 6, pp. 1259–1275, 2016. doi:10.1111/bjet.12313,2016.
  • Y. Li, “Effect of prior knowledge on attitudes, behavior, and learning performance in video lecture viewing”, International Journal of Human–Computer Interaction, vol. 35, no. 4-5, pp. 415-26, 2019.
  • Varol Altay and B. Alatas, “Performance analysis of multi-objective artificial intelligence optimization algorithms in numerical association rule mining”, Journal of Ambient Intelligence and Humanized Computing, vol. 11, no. 8, pp. 3449-3469, 2020.
  • Zhang, and S. Zhang (eds.), Association rule mining: models and algorithms, Springer, 2003.
  • Y. Kuo, J. Luo, and J. Brielmaier, “Investigating students’ use of lecture videos in online courses: A case research for understanding learning behaviors via data mining”, In F. Li, R. Klamma, M. Laanpere, J. Zhang, B. Manjón, R. Lau (eds), Advances in web-based learning–ICWL 2015. Lecture Notes in Computer Science (vol. 9412). Springer, Cham, 2015.
  • S. Song, A. L. Kalet, and J. L. Plass, “Interplay of prior knowledge, self-regulation and motivation in complex multimedia learning environments”, Journal of Computer Assisted Learning, vol. 32, no. 1, pp. 31–50, 2016. doi:10.1111/jcal.12117
  • Sinha, P. Jermann, N. Li, and P. Dillenbourg, “Your Click Decides your Fate: Inferring Information Processing and Attrition Behavior from MOOC Video Clickstream Interactions”, In Conference on Empirical Methods in Natural Language Processing (EMNLP),2014, pp. 3–14.
  • G Brinton, S. Buccapatnam, M. Chiang, and H. V. Poor, “Mining MOOC Clickstreams: Video-Watching Behavior vs In-Video Quiz Performance”, IEEE Transactions on Signal Processing, vol. 8, no. 1, pp. 136–148, 2016.
  • G. Brinton, and M. Chiang, “MOOC Performance Prediction via Clickstream Data and Social Learning Networks”, IEEE Conference on Computer Communications (INFOCOM), 2015, pp. 2299–2307.
  • A. Filvà, M. A. Forment, F. J. García-Peñalvo, D. F. Escudero, and M. J. Casañ, “Clickstream for learning analytics to assess students’ behavior with Scratch”, Future Generation Computer Systems, vol. 93, pp. 673-86, 2019.
  • Harvey and D. Green, “Defining quality”, Assessment & evaluation in higher education, vol. 18, no. 1, pp. 9-34, 1993.
  • Jung, “The dimensions of e-learning quality: from the learner’s perspective”, Educational Technology Research and Development, vol. 59, no. 4, 445-464, 2011.
  • McNaught, Quality assurance for online courses: From policy to process to improvement?, ERIC Clearinghouse, 2001, pp. 435-442.
  • Phipps and J. Merisotis, Quality on the line: Benchmarks for success in internet-based distance education, ERIC, 2000.
  •