Personalized Student Knowledge Modeling for Future Learning Resource Prediction

2025/05/20 by Soroush Hashemifar, Hashemifar, Soroush, Sherry Sahebi +1
Computer Science · #Artificial Intelligence (cs.AI) #Educational Technology and Assessment #FOS: Computer and information sciences #Intelligent Tutoring Systems and Adaptive Learning #Machine Learning (cs.LG) #Online Learning and Analytics

paper · pdf · doi:10.48550/arxiv.2505.14072

openalex publication_date 2025/05/20 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

Citations

• Leveraging Deep Reinforcement Learning for Metacognitive Interventions across Intelligent Tutoring Systems
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• Long Short-Term Memory
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Related

• Personalized Knowledge Tracing through Student Representation Reconstruction and Class Imbalance Mitigation
• BKT-LSTM: Efficient Student Modeling for knowledge tracing and student performance prediction
• Multi-Layer Personalized Federated Learning for Mitigating Biases in Student Predictive Analytics
• Modeling Knowledge Acquisition from Multiple Learning Resource Types
• Do we need to go Deep? Knowledge Tracing with Big Data