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Kloft, M., Stiehler, F., Zheng, Z. and Pinkwart, N. (2014) Predicting MOOC Dropout over Weeks Using Machine Learning Methods. Proceedings of the EMNLP 2014 Workshop on Analysis of Large Scale Social Interaction in MOOCs, Doha, October 2014, 60-65.
http://dx.doi.org/10.3115/v1/W14-4111

has been cited by the following article:

• TITLE: Student Performance Prediction via Attention-Based Multi-Layer Long-Short Term Memory

  AUTHORS: Yanqing Xie

  KEYWORDS: Data Science Applications in Education, Distance Education and Online Learning, LSTM

  JOURNAL NAME: Journal of Computer and Communications, Vol.9 No.8, August 18, 2021

  ABSTRACT: Online education has attracted a large number of students in recent years, because it breaks through the limitations of time and space and makes high-quality education at your fingertips. The method of predicting student performance is to analyze and predict the student’s final performance by collecting demographic data such as the student’s gender, age, and highest education level, and clickstream data generated when students interact with VLE in different types of specific courses, which are widely used in online education platforms. This article proposes a model to predict student performance via Attention-based Multi-layer LSTM (AML), which combines student demographic data and clickstream data for comprehensive analysis. We hope that we can obtain a higher prediction accuracy as soon as possible to provide timely intervention. The results show that the proposed model can improve the accuracy of 0.52% - 0.85% and the F1 score of 0.89% - 2.30% on the four-class classification task as well as the accuracy of 0.15% - 0.97% and the F1 score of 0.21% - 2.77% on the binary classification task from week 5 to week 25.