Affiliation:
1. School of Computer Science and Engineering, Northeast University, Shenyang 110819, China
2. School of Computing, Faculty of Science and Engineering, Macquarie University, Sydney, NSW 2109, Australia
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.
Funder
National Natural Science Foundation of China
Subject
General Mathematics,Engineering (miscellaneous),Computer Science (miscellaneous)
Reference55 articles.
1. Li, T., Chen, L., Jensen, C.S., Pedersen, T.B., Gao, Y., and Hu, J. (2022, January 9–12). Evolutionary clustering of moving objects. Proceedings of the 2022 IEEE 38th International Conference on Data Engineering (ICDE), Kuala Lumpur, Malaysia.
2. Compression of uncertain trajectories in road networks;Li;Proc. VLDB Endow.,2020
3. TRACE: Real-time compression of streaming trajectories in road networks;Li;Proc. VLDB Endow.,2021
4. Closest Pairs Search Over Data Stream;Zhu;Proc. ACM Manag. Data,2023
5. Event-triggered-based distributed cooperative energy management for multienergy systems;Li;IEEE Trans. Ind. Inform.,2018