Abstract
Based on the datasets from the MOOCs of Peking University running on the Coursera platform, we extract 19 major features of tune in after analyzing the log structure. To begin with, we focus on the characteristics of start and dropout point of learners through the statistics of their start time and dropout time. Then we construct two models. First, several approaches of machine learning are used to build a sliding window model for predicting the dropout probabilities in a certain course. Second, SVM is used to build the model for predicting whether a student can get a score at the end of the course. For instructors and designers of MOOCs, dynamically tracking the records of the dropouts could be helpful to improve the course quality in order to reduce the dropout rate.
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References
Amnueypornsakul, B., Bhat, S., Chinprutthiwong, P.: Predicting attrition along the way: the UIUC model. In: Proceedings of the EMNLP 2014 Workshop on Analysis of Large Scale Social Interaction in MOOCs, pp. 55–59 (2014)
Bienkowski, M., Feng, M., Means, B.: Enhancing teaching and learning through educational data mining and learning analytics: an issue brief. US Department of Education, Office of Educational Technology, pp. 1–57 (2012)
Elouazizi, N.: Point-of-view mining and cognitive presence in MOOCs: a (computational) linguistics perspective. In: EMNLP 2014, p. 32 (2014)
Guo, P.J., Reinecke, K.: Demographic differences in how students navigate through MOOCs. In: Proceedings of the first ACM Conference on Learning@ Scale Conference, pp. 21–30. ACM (2014)
Jordan, K.: MOOC completion rates: the data (2013). http://www.katyjordan.com/MOOCproject.html. Accessed 27 Aug 2014
Kloft, M., Stiehler, F., Zheng, Z., Pinkwart, N.: Predicting MOOC dropout over weeks using machine learning methods. In: Proceedings of the EMNLP 2014 Workshop on Analysis of Large Scale Social Interaction in MOOCs, pp. 60–65 (2014)
Mi, F.: Machine learning models for some learning analytics issues in massive open online courses. Ph.D. thesis, The Hong Kong University of Science and Technology (2015)
Mining, T.E.D.: Enhancing teaching and learning through educational data mining and learning analytics: an issue brief. In: Proceedings of Conference on Advanced Technology for Education (2012)
Moon, S., Potdar, S., Martin, L.: Identifying student leaders from MOOC discussion forums through language influence. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 15–20 (2014)
Rosé, C.P., Siemens, G.: Shared task on prediction of dropout over time in massively open online courses. In: Proceedings of EMNLP, vol. 14, p. 39 (2014)
Sharkey, M., Sanders, R.: A process for predicting MOOC attrition. In: Proceedings of the EMNLP 2014 Workshop on Analysis of Large Scale Social Interaction in MOOCs. pp. 50–54 (2014)
Sinha, T., Jermann, P., Li, N., Dillenbourg, P.: Your click decides your fate: inferring information processing and attrition behavior from MOOC video clickstream interactions. arXiv preprint (2014). arXiv:1407.7131
Taylor, C., Veeramachaneni, K., O’Reilly, U.M.: Likely to stop? Predicting stopout in massive open online courses. arXiv preprint (2014). arXiv:1408.3382
Yang, D., Sinha, T., Adamson, D., Rosé, C.P.: Turn on, tune in, drop out: anticipating student dropouts in massive open online courses. In: Proceedings of the 2013 NIPS Data-Driven Education Workshop, vol. 11, p. 14 (2013)
Yang, D., Wen, M., Rose, C.: Towards identifying the resolvability of threads in MOOCs. In: EMNLP 2014, p. 21 (2014)
Acknowledgments
This paper is supported by Peking University Education Foundation (2015ZD05) and National Natural Science Foundation of China (61472013).
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Lu, X., Wang, S., Huang, J., Chen, W., Yan, Z. (2017). What Decides the Dropout in MOOCs?. In: Bao, Z., Trajcevski, G., Chang, L., Hua, W. (eds) Database Systems for Advanced Applications. DASFAA 2017. Lecture Notes in Computer Science(), vol 10179. Springer, Cham. https://doi.org/10.1007/978-3-319-55705-2_25
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DOI: https://doi.org/10.1007/978-3-319-55705-2_25
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