Modeling Behavior Change for Multi-model At-Risk Students Early Prediction (extended version)

• URL: http://arxiv.org/abs/2503.05734v1
• Date: Wed, 19 Feb 2025 11:16:46 GMT
• Title: Modeling Behavior Change for Multi-model At-Risk Students Early Prediction (extended version)
• Authors: Jiabei Cheng, Zhen-Qun Yang, Jiannong Cao, Yu Yang, Kai Cheung Franky Poon, Daniel Lai,
• Abstract summary: Current models primarily identify students with consistently poor performance through simple and discrete behavioural patterns.<n>We have developed an innovative prediction model, Multimodal- ChangePoint Detection (MCPD), utilizing the textual teacher remark data and numerical grade data from middle schools.<n>Our model achieves an accuracy range of 70- 75%, with an average outperforming baseline algorithms by approximately 5-10%.
• Score: 10.413751893289056
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In the educational domain, identifying students at risk of dropping out is essential for allowing educators to intervene effectively, improving both academic outcomes and overall student well-being. Data in educational settings often originate from diverse sources, such as assignments, grades, and attendance records. However, most existing research relies on online learning data and just extracting the quantitative features. While quantification eases processing, it also leads to a significant loss of original information. Moreover, current models primarily identify students with consistently poor performance through simple and discrete behavioural patterns, failing to capture the complex continuity and non-linear changes in student behaviour. We have developed an innovative prediction model, Multimodal- ChangePoint Detection (MCPD), utilizing the textual teacher remark data and numerical grade data from middle schools. Our model achieves a highly integrated and intelligent analysis by using independent encoders to process two data types, fusing the encoded feature. The model further refines its analysis by leveraging a changepoint detection module to pinpoint crucial behavioral changes, which are integrated as dynamic weights through a simple attention mechanism. Experimental validations indicate that our model achieves an accuracy range of 70- 75%, with an average outperforming baseline algorithms by approximately 5-10%. Additionally, our algorithm demonstrates a certain degree of transferability, maintaining high accuracy when adjusted and retrained with different definitions of at-risk, proving its broad applicability.

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