Personalized Student Knowledge Modeling for Future Learning Resource Prediction

• URL: http://arxiv.org/abs/2505.14072v1
• Date: Tue, 20 May 2025 08:23:50 GMT
• Title: Personalized Student Knowledge Modeling for Future Learning Resource Prediction
• Authors: Soroush Hashemifar, Sherry Sahebi,
• Abstract summary: We propose Knowledge Modeling and Material Prediction (KMaP) for personalized and simultaneous modeling of student knowledge and behavior.<n>KMaP employs clustering-based student profiling to create personalized student representations, improving predictions of future learning resource preferences.<n>Experiments on two real-world datasets confirm significant behavioral differences across student clusters.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Despite advances in deep learning for education, student knowledge tracing and behavior modeling face persistent challenges: limited personalization, inadequate modeling of diverse learning activities (especially non-assessed materials), and overlooking the interplay between knowledge acquisition and behavioral patterns. Practical limitations, such as fixed-size sequence segmentation, frequently lead to the loss of contextual information vital for personalized learning. Moreover, reliance on student performance on assessed materials limits the modeling scope, excluding non-assessed interactions like lectures. To overcome these shortcomings, we propose Knowledge Modeling and Material Prediction (KMaP), a stateful multi-task approach designed for personalized and simultaneous modeling of student knowledge and behavior. KMaP employs clustering-based student profiling to create personalized student representations, improving predictions of future learning resource preferences. Extensive experiments on two real-world datasets confirm significant behavioral differences across student clusters and validate the efficacy of the KMaP model.

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