Churn Prediction Task in MOOC

Lisitsyna Liubov (ITMO University, Kronvrkskiy pr. 49, Saint Petersburg, 197101, Russia)
Oreshin S. A. (ITMO University, Kronvrkskiy pr. 49, Saint Petersburg, 197101, Russia)

Article ID: 537

Abstract


Churn prediction is a common task for machine learning applications in business. In this paper, this task is adapted for solving problem of low efficiency of massive open online courses (only 5% of all the students finish their course). The approach is presented on course “Methods and algorithms of the graph theory” held on national platform of online education in Russia. This paper includes all the steps to build an intelligent system to predict students who are active during the course, but not likely to finish it. The first part consists of constructing the right sample for prediction, EDA and choosing the most appropriate week of the course to make predictions on. The second part is about choosing the right metric and building models. Also, approach with using ensembles like stacking is proposed to increase the accuracy of predictions. As a result, a general approach to build a churn prediction model for online course is reviewed. This approach can be used for making the process of online education adaptive and intelligent for a separate student.

Keywords


Machine learning; Data science; Exploratory data analysis; Logistic regression; Gradient boosting on trees; Stacking; Classification; Ranking

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References


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DOI: https://doi.org/10.30564/jcsr.v1i1.537

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