Towards the Differentiation of Initial and Final Retention in Massive Open Online Courses

Author:

Al-Shabandar Raghad,Hussain Abir,Laws Andy,Keight Robert,Lunn Janet

Publisher

Springer International Publishing

Reference16 articles.

1. Qiu, J., Tang, J., Liu, T.X., Gong, J., Zhang, C., Zhang, Q., Xue, Y.: Modeling and predicting learning behavior in MOOCs. In: Proceedings of the Ninth ACM International Conference on Web Search and Data Mining, pp. 93–102 (2016)

2. Yang, D., Rose, C.P.: “ Turn on, Tune in, Drop out”: Anticipating student dropouts in Massive Open Online Courses. 1–8

3. Kloft, M., Stiehler, F., Zheng, Z., Pinkwart, N., Mattingly, K.D., Rice, M.C., Berge, Z.L.: Predicting MOOC dropout over weeks using machine learning methods. Knowl. Manag. E-Learn. 4, 60–65 (2014)

4. United Kingdom: Dropout Rates Of Massive Open Online Courses: Behavioural Patterns Mooc Dropout and Completion: Existing Evaluations

5. Baker, R.S.J.D., Siemens, G.: Educational data mining and learning analytics. In: Cambridge Handbook of the Learning Sciences (2014)

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