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Feature Extraction and Learning Effect Analysis for MOOCs Users Based on Data Mining
ARTICLE

, Xi'an International University, Department of Teaching and Scientific Research of Ideological and Political Theory Course

iJET Volume 13, Number 10, ISSN 1863-0383 Publisher: International Journal of Emerging Technology in Learning, Kassel, Germany

Abstract

This paper aims to predict the user dropout rate in MOOC learning based on the features extracted from user learning behaviours. For this purpose, some learning behaviour features were extracted from the data of MOOC platforms. Then two machine learning algorithms, respectively based on support vector machine (SVM) and the artificial neural network (ANN), were introduced to predict the dropout rate of MOOC course. The two algorithms were contrasted with some commonly used prediction methods. The comparison results show that our algo-rithms outperformed others in the prediction of MOOC user dropout rate. The re-search sheds new light on the feature extraction and learning effect of MOOC programs.

Citation

Li, Y. (2018). Feature Extraction and Learning Effect Analysis for MOOCs Users Based on Data Mining. International Journal of Emerging Technologies in Learning (iJET), 13(10), 108-120. Kassel, Germany: International Journal of Emerging Technology in Learning. Retrieved May 8, 2021 from .

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