Abstract
Massive open online courses (MOOCs) have emerged as a powerful platform for imparting education in the last few years. Discussion forums in online courses connect various geographically separated MOOC participants and serve as the primary means of communication between them. The text in the forums reflects many important aspects of the course such as the student population and their changing interests, parts of the course that were well received and parts needing attention, and common misconceptions faced by students. In order to improve the quality of online courses and students’ interaction and learning experience, instructors need to actively monitor and discern patterns in previous iterations of the course and mold the course to suit the needs of the ever-changing student population. To enable this, in this work, we perform a systematic detailed analysis of the evolution of fine-grained topics in online course discussion forums across repeated MOOC offerings using seeded topic models and draw important insights on the nature of students, types of issues, and student satisfaction. We present topic evolution results on two successful long-running MOOCs: (i) a business course, and (ii) a computer science course. Our models uncover interesting topic trends in both courses including the decline of logistic issues in both courses as iterations unfold, decline in grading related issues when automatic grading is adopted in the business course, and prevalence of technical issues in the computer science course in comparison to the business course. Our models throw light on the different ways students interact on MOOCs and their changing needs, and are useful for instructors to understand the progression of courses and accordingly fine-tune courses to meet student expectations.
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Agrawal, A., Venkatraman, J., Leonard, S., Paepcke, A.: YouEDU: addressing confusion in MOOC discussion forums by recommending instructional video clips. In: Proceedings of the International Conference on Educational Data Mining (EDM) (2015)
Anderson, A., Huttenlocher, D., Kleinberg, J., Leskovec, J.: Engaging with massive online courses. In: Proceedings of the International Conference on World Wide Web (WWW) (2014)
Blei, D.M., Lafferty, J.D.: Dynamic topic models. In: Proceedings of the International Conference on Machine Learning (ICML) (2006)
Chaturvedi, S., Goldwasser, D., Daumé III, H.: Predicting instructor’s intervention in MOOC forums. In: Proceedings of the Annual Meeting of the Association for Computational Linguistics (ACL) (2014)
Cui, Y., Wise, A.F.: Identifying content-related threads in MOOC discussion forums. In: Proceedings of the ACM Conference on Learning @ Scale (L@S) (2015)
Ezen-Can, A., Boyer, K.E., Kellogg, S., Booth, S.: Unsupervised modeling for understanding MOOC discussion forums: a learning analytics approach. In: Proceedings of the International Conference on Learning Analytics And Knowledge (LAK) (2015)
Gohr, A., Hinneburg, A., Schult, R., Spiliopoulou, M.: Topic evolution in a stream of documents. In: Proceedings of the SIAM International Conference on Data Mining (SDM) (2009)
Hall, D., Jurafsky, D., Manning, C.D.: Studying the history of ideas using topic models. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP) (2008)
Jagarlamudi, J., Daumé III, H., Udupa, R.: Incorporating lexical priors into topic models. In: Proceedings of the European Chapter of the Association for Computational Linguistics (EACL) (2012)
Ramesh, A., Goldwasser, D., Huang, B., Daume, H., Getoor, L.: Understanding MOOC discussion forums using seeded LDA. In: 9th ACL Workshop on Innovative Use of NLP for Building Educational Applications. ACL (2014)
Ramesh, A., Kumar, S.H., Foulds, J., Getoor, L.: Weakly supervised models of aspect-sentiment for online course discussion forums. In: Proceedings of the Annual Meeting of the Association for Computational Linguistics (ACL) (2015)
Wang, X., McCallum, A.: Topics over time: a non-Markov continuous-time model of topical trends. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2006)
Wong, J.-S., Pursel, B., Divinsky, A., Jansen, B.J.: An analysis of MOOC discussion forum interactions from the most active users. In: Agarwal, N., Xu, K., Osgood, N. (eds.) SBP 2015. LNCS, vol. 9021, pp. 452–457. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-16268-3_58
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Ramesh, A., Getoor, L. (2018). Topic Evolution Models for Long-Running MOOCs. In: Hacid, H., Cellary, W., Wang, H., Paik, HY., Zhou, R. (eds) Web Information Systems Engineering – WISE 2018. WISE 2018. Lecture Notes in Computer Science(), vol 11234. Springer, Cham. https://doi.org/10.1007/978-3-030-02925-8_29
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DOI: https://doi.org/10.1007/978-3-030-02925-8_29
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