Main Article Content
Abstract
Massive Open Online Courses (MOOC) allow students to learn online at any time and from any location. Unfortunately, poor completion rates and a large student group make it difficult for teachers to keep track of their student’s progress. Due to a lack of adequate counselling, students who perform poorly are more likely to give up. The goal of this study was to predict student’s certification by analyzing data on student’s learning behavior. The initial data on learning behavior was obtained from edX, a well-known MOOC platform. Based on this data, three statistical models such as logistic regression, graph convolutional network, and cluster analysis were utilized to predict student’s performance. The proposed model’s usefulness was demonstrated by using a testing set of data from the actual courses. Our findings showed that tracking student activity in terms of number of unique days active, watching videos, participating in forum discussions, and exploring more courseware content might help predict student’s performance in MOOC and enhance completion rates.
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Article Details
References
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- J.L. Santos, J. Klerkx, E. Duval, D. Gago, and L. Rodríguez, “Success, Activity and Drop-Outs in MOOCs an Exploratory Study on the UNED COMA Courses,” in Proceedings of the Fourth International Conference on Learning Analytics and Knowledge, 2014, pp. 98–102, doi: 10.1145/2567574.2567627.
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- L. Breslow, D.E. Pritchard, and J. Deboer, “Studying Learning in the Worldwide Classroom: Research into edX’s First MOOC,” Research & Practice in Assessment, Vol. 8, pp. 1–25, Jun. 2013. [Online]. Available: https://www.rpajournal.com/dev/wp-content/uploads/2013/05/SF2.pdf
References
P.G. de Barba, G.E. Kennedy, and M.D. Ainley, “The Role of Students’ Motivation and Participation in Predicting Performance in a MOOC,” Journal of Computer Assisted Learning, Vol. 32, No. 3, pp. 218–231, Jun. 2016, doi: 10.1111/jcal.12130.
G. Hughes and C. Dobbins, “The Utilization of Data Analysis Techniques in Predicting Student Performance in Massive Open Online Courses (MOOCs),” Research and Practice in Technology Enhanced Learning, Vol. 10, pp. 1–18, Jul. 2015, Art. no. 10, doi: 10.1186/s41039-015-0007-z.
R. Al-Shabandar, A.J. Hussain, P. Liatsis, and R. Keight, “Analyzing Learners Behavior in MOOCs: An Examination of Performance and Motivation Using a Data-Driven Approach,” IEEE Access, Vol. 6, pp. 73669–73685, 2018, doi: 10.1109/ACCESS.2018.2876755.
B. Xu and D. Yang, “Motivation Classification and Grade Prediction for MOOCs Learners,” Computational Intelligence and Neuroscience, Vol. 2016, pp. 1–7, 2016, Art. no. 2174613, doi: 10.1155/2016/2174613.
H. Nen-Fu, H. I-Hsien, L. Chia-An, C. Hsiang-Chun, T. Jian-Wei, and F. Tung-Te, “The Clustering Analysis System Based on Students’ Motivation and Learning Behavior,” in 2018 Learning With MOOCS (LWMOOCS), 2018, pp. 117–119, doi: 10.1109/LWMOOCS.2018.8534611.
R. Conijn, A. Van den Beemt, and P. Cuijpers, “Predicting Student Performance in a Blended MOOC,” Journal of Computer Assisted Learning, Vol. 34, No. 5, pp. 615–628, Oct. 2018, doi: 10.1111/jcal.12270.
J.L. Santos, J. Klerkx, E. Duval, D. Gago, and L. Rodríguez, “Success, Activity and Drop-Outs in MOOCs an Exploratory Study on the UNED COMA Courses,” in Proceedings of the Fourth International Conference on Learning Analytics and Knowledge, 2014, pp. 98–102, doi: 10.1145/2567574.2567627.
P. Duffy, “Engaging the YouTube Google-Eyed Generation: Strategies for Using Web 2.0 in Teaching and Learning,” The Electronic Journal of e-Learning, Vol. 6, No. 2, pp. 119–130. [Online]. Available: https://academic-publishing.org/index.php/ejel/article/view/1535/1498
L. Breslow, D.E. Pritchard, and J. Deboer, “Studying Learning in the Worldwide Classroom: Research into edX’s First MOOC,” Research & Practice in Assessment, Vol. 8, pp. 1–25, Jun. 2013. [Online]. Available: https://www.rpajournal.com/dev/wp-content/uploads/2013/05/SF2.pdf