Analysis of Learning Behavior in Massive Open Online Courses (MOOCs): An Application of Machine Learning and Deep Learning
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Abstract
The rapid growth of Massive Open Online Courses (MOOC) has led to a rapid increase in the volume and variety of information. Big data has beginning to demonstrate its value in distance learning. The content in this paper is organized into five sections. The first section presents the problems encountered with MOOC. The second section introduces the concept of machine learning and the classification of learning methods. The third section presents the concept of deep learning related to learning behaviors in MOOC; Part 4, how to apply deep learning to predict learning outcomes based on learner behavior; and Part 5, the conclusions.
Article Details
ข้อความและบทความในวารสารการวัด ประเมินผล สถิติ และการวิจัยทางสังคมศาสตร์ เป็นแนวคิดของผู้เขียน มิใช่ความคิดเห็นของกองบรรณาธิการวารสาร จึงมิใช่ความรับผิดชอบของวารสารการวัด ประเมินผล สถิติ และการวิจัยทางสังคมศาสตร์ บทความในวารสารต้องไม่เคยตีพิมพ์ที่ใดมาก่อน และสงวนสิทธิ์ตามกฎหมายไทย การจะนำไปเผยแพร่ ต้องได้รับอนุญาตเป็นลายลักษณ์อักษรจากกองบรรณาธิการ
References
Del Vento, D., & Fanfarillo, A. (2019). Traps, pitfalls and misconceptions of machine learning applied to scientific disciplines. Pearc '19 Proceedings of The Practice And Experience In Advanced Research Computing on Rise of The Machines, 75. doi:10.1145/3332186.3332209
El Naqa, I., & Murphy, M. J. (2015). What is machine learning? in I. El Naqa, R. Li, & M. J. Murphy (Eds.), Machine Learning in Radiation Oncology: Theory and Applications (pp. 3-11). Cham: Springer International Publishing.
El Said, G. R. (2017). Understanding how learners use massive open online courses and why they drop out: thematic analysis of an interview study in a developing country. Journal of Educational Computing Research, 55(5), 724-752. doi:10.1177/0735633116681302
Henderikx, M. A., Kreijns, K., & Kalz, M. (2017). Refining success and dropout in massive open online courses based on the intention–behavior gap. Distance Education, 38(3), 353-368. doi:10.1080/01587919.2017.1369006
Hollands, F. M., & Tirthali, D. (2014). MOOCs: Expectations and reality. Full Report. Retrieved from http://files.eric.ed.gov/fulltext/ED547237.pdf
Huang, N., Chen, C., Tzeng, J., Fang, T. T., & Lee, C. (2018, 26-28 Sept. 2018). Concept assessment system integrated with a knowledge map using deep learning. Paper presented at the 2018 Learning With MOOCS (LWMOOCS).
Kim, J., Guo, P. J., Seaton, D. T., Mitros, P., Gajos, K. Z., & Miller, R. C. (2014). Understanding in-video dropouts and interaction peaks inonline lecture videos. Paper presented at the Proceedings of the First ACM Conference on Learning @ scale conference, Atlanta, Georgia, USA. https://doi.org/10.1145/2556325.2566237
Lee, C.-A., Tzeng, J.-W., Huang, N.-F., & Su, Y.-S. (2021). Prediction of student performance in massive open online courses using deep learning system based on learning behaviors. Educational Technology & Society, 24(3), 130-146. Retrieved from https://www.jstor.org/stable/27032861
Lee, Y. (2018). Effect of uninterrupted time-on-task on students’ success in Massive Open Online Courses (MOOCs). Computers in Human Behavior, 86, 174-180. doi:https://doi.org/10.1016/j.chb.2018.04.043
Lee, Y., & Choi, J. (2011). A review of online course dropout research: implications for practice and future research. Educational Technology Research and Development, 59(5), 593-618. doi:10.1007/s11423-010-9177-y
Shah, D. (2020). Analysis by the numbers: MOOCs in 2020. Retrieved from https://www.class-central.com/report/mooc-stats-2020/
Wang, W., Yu, H., & Miao, C. (2017). Deep model for dropout prediction in MOOCs. Paper presented at the Proceedings of the 2nd International Conference on Crowd Science and Engineering, Beijing, China. https://doi.org/10.1145/3126973.3126990
