Personalized Student Knowledge Modeling for Future Learning Resource Prediction
This work addresses personalized student modeling for education, but it is incremental as it builds on existing deep learning methods with specific enhancements.
The paper tackled the problem of limited personalization and inadequate modeling of diverse learning activities in student knowledge tracing by proposing KMaP, a stateful multi-task approach that uses clustering-based student profiling to predict future learning resource preferences, with experiments on two real-world datasets confirming significant behavioral differences and validating its efficacy.
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.