Personalized Student Knowledge Modeling for Future Learning Resource Prediction

TL;DR

This paper introduces KMaP, a multi-task model that personalizes student knowledge and behavior modeling by clustering students, improving future learning resource prediction and addressing limitations of existing methods.

Contribution

The paper presents KMaP, a novel multi-task approach that incorporates clustering-based student profiling for personalized knowledge and behavior modeling in education.

Findings

KMaP significantly improves prediction accuracy of learning resource preferences.

Distinct behavioral patterns are identified across different student clusters.

The model effectively captures personalized learning behaviors in real-world datasets.

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.