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

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 patt...

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Bibliographic Details
Published inMathematics (Basel) Vol. 11; no. 24; p. 4977
Main Authors Zhang, Tiancheng, Liu, Hengyu, Tao, Jiale, Wang, Yuyang, Yu, Minghe, Chen, Hui, Yu, Ge
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.12.2023
Subjects
Accuracy
Algorithms
Analysis
Artificial intelligence
Artificial neural networks
data analysis
Data mining
Datasets
deep learning
dropout prediction
Education
Empirical analysis
Federated learning
Investigations
Machine learning
Mathematical models
Neural networks
Online instruction
Performance evaluation
Personalized learning
Prediction models
Representations
Student behavior
Student participation
Student retention
Students
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Summary: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.
ISSN:2227-7390
2227-7390
DOI:10.3390/math11244977