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Open Access

ARTICLE

MixerKT: A Knowledge Tracing Model Based on Pure MLP Architecture

Jun Wang1,2, Mingjie Wang1,2, Zijie Li1,2, Ken Chen1,2, Jiatian Mei1,2, Shu Zhang1,2,*
1 Key Laboratory of Education Informatization for Nationalities, Ministry of Education, Yunnan Normal University, Kunming, 650000, China
2 Yunnan Key Laboratory of Smart Education, Yunnan Normal University, Kunming, 650000, China
* Corresponding Author: Shu Zhang. Email: email

Computers, Materials & Continua https://doi.org/10.32604/cmc.2024.057224

Received 11 August 2024; Accepted 07 October 2024; Published online 18 November 2024

Abstract

In the field of intelligent education, the integration of artificial intelligence, especially deep learning technologies, has garnered significant attention. Knowledge tracing (KT) plays a pivotal role in this field by predicting students’ future performance through the analysis of historical interaction data, thereby assisting educators in evaluating knowledge mastery and tailoring instructional strategies. Traditional knowledge tracing methods, largely based on Recurrent Neural Networks (RNNs) and Transformer models, primarily focus on capturing long-term interaction patterns in sequential data. However, these models may neglect crucial short-term dynamics and other relevant features. This paper introduces a novel approach to knowledge tracing by leveraging a pure Multilayer Perceptron (MLP) architecture. We propose MixerKT, a knowledge tracing model based on the HyperMixer framework, which uniquely integrates global and local Mixer feature extractors. This architecture enables more effective extraction of both long-term interaction trends and recent learning behaviors, addressing limitations in current models that may overlook these key aspects. Empirical evaluations on two widely-used datasets, ASSISTments2009 and Algebra2005, demonstrate that MixerKT consistently outperforms several state-of-the-art models, including DKT, SAKT, and Separated Self-Attentive Neural Knowledge Tracing (SAINT). Specifically, MixerKT achieves higher prediction accuracy, highlighting its effectiveness in capturing the nuances of learners’ knowledge states. These results indicate that our model provides a more comprehensive representation of student learning patterns, enhancing the ability to predict future performance with greater precision.

Keywords

Knowledge tracing; multilayer perceptron; channel mixer; sequence mixer

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