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Withdrawals Prediction in Virtual Learning Environments with Deep Self-paced Learning

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Computer Science and Education (ICCSE 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1813))

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Abstract

Withdrawals prediction in virtual learning environments aims to predict student dropout by modeling student behaviour when utilizing e-learning platforms. Classic machine learning approaches lack sufficient expression ability. Deep learning methods are inclined to get stuck in the local minimum. In addition, there is not any public source code platform to comprehensively compare all the baselines. In this paper, we propose a new Withdrawals Prediction method in virtual learning environments with Deep Self-Paced Learning (WPDSPL) to deal with these two problems. Specifically, WPDSPL overcomes the bad local minimum problem by introducing self-paced learning into LSTM to gradually add data from easy ones to more complex ones during the training procedure. In addition, we deal with the inconvenient comparison problem by releasing the source code to comprehensively compare all the baselines. Comprehensive experiments demonstrate the superiority of our proposed approach.

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Acknowledgment

This work is partly supported by Youth Project of Shanghai Philosophy and Social Science Planning (No: 2019EKS007), General project of Shanghai Educational Research (No: C2-2020103) and National Science Foundation, China (No:615723156151101179, No: 62102150 and No: 62201213), and the China Postdoctoral Science Foundation under No: 2020M681237.

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Authors and Affiliations

  1. School of Marxism, Shanghai University of Political Science and Law, Shanghai, China

    Xianyue Li

  2. School of Communication and Electronic Engineering, East China Normal University, Shanghai, China

    Wei Xu & Shuo Xie

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  1. Xianyue Li
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  2. Wei Xu
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  3. Shuo Xie
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Correspondence to Wei Xu .

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Editors and Affiliations

  1. Xiamen University, Xiamen, China

    Wenxing Hong

  2. Sichuan University, Chengdu, China

    Yang Weng

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Li, X., Xu, W., Xie, S. (2023). Withdrawals Prediction in Virtual Learning Environments with Deep Self-paced Learning. In: Hong, W., Weng, Y. (eds) Computer Science and Education. ICCSE 2022. Communications in Computer and Information Science, vol 1813. Springer, Singapore. https://doi.org/10.1007/978-981-99-2449-3_7

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  • DOI: https://doi.org/10.1007/978-981-99-2449-3_7

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