Enhancing Dropout Prediction in Distributed Educational Data Using Learning Pattern Awareness: A Federated Learning Approach

Tiancheng Zhang (), Hengyu Liu, Jiale Tao, Yuyang Wang, Minghe Yu, Hui Chen and Ge Yu
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Tiancheng Zhang: School of Computer Science and Engineering, Northeast University, Shenyang 110819, China
Hengyu Liu: School of Computer Science and Engineering, Northeast University, Shenyang 110819, China
Jiale Tao: School of Computer Science and Engineering, Northeast University, Shenyang 110819, China
Yuyang Wang: School of Computer Science and Engineering, Northeast University, Shenyang 110819, China
Minghe Yu: School of Computer Science and Engineering, Northeast University, Shenyang 110819, China
Hui Chen: School of Computing, Faculty of Science and Engineering, Macquarie University, Sydney, NSW 2109, Australia
Ge Yu: School of Computer Science and Engineering, Northeast University, Shenyang 110819, China

Mathematics, 2023, vol. 11, issue 24, 1-18

Abstract: Learning patterns are crucial for predicting student dropout in educational settings, providing insights into students’ behaviors and motivations. However, existing mainstream dropout prediction models have limitations in effectively mining these learning patterns and cannot mine these learning patterns in large-scale, distributed educational datasets. In this study, we analyze the representations of mainstream models and identify their inability to capture students’ distinct learning patterns and personalized variations across courses. Addressing these challenges, our study adopts a federated learning approach, tailoring the analysis to leverage distributed data while maintaining privacy and decentralization. We introduce the Federated Learning Pattern Aware Dropout Prediction Model (FLPADPM), which utilizes a one-dimensional convolutional neural network (CNN) and a bidirectional long short-term memory (LSTM) layer within a federated learning framework. This model is designed to effectively capture nuanced learning patterns and adapt to variations across diverse educational settings. To evaluate the performance of LPADPM, we conduct an empirical evaluation using the KDD Cup 2015 and XuetangX datasets. Our results demonstrate that LPADPM outperforms state-of-the-art models in accurately predicting student dropout behavior. Furthermore, we visualize the representations generated by LPADPM, which confirm its ability to effectively mine learning patterns in different courses. Our results showcase the model’s ability to capture and analyze learning patterns across various courses and institutions within a federated learning context.

Keywords: data analysis; federated learning; machine learning; deep learning; dropout prediction (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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