MixerKT:A Knowledge Tracing Model Based on Pure MLP Architecture  

作　　者：Jun Wang Mingjie Wang Zijie Li Ken Chen Jiatian Mei Shu Zhang 

机构地区：[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

出　　处：《Computers, Materials & Continua》2025年第1期485-498,共14页计算机、材料和连续体（英文）

基　　金：supported by National Natural Science Foundation of China(Nos.62266054 and 62166050);Key Program of Fundamental Research Project of Yunnan Science and Technology Plan(No.202201AS070021);Yunnan Fundamental Research Projects(No.202401AT070122);Yunnan International Joint Research and Development Center of China-Laos-Thailand Educational Digitalization(No.202203AP140006);Scientific Research Foundation of Yunnan Provincial Department of Education(No.2024Y159).

摘　　要：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 knowledgemastery and tailoring instructional strategies.Traditional knowledge tracingmethods,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 proposeMixerKT,a knowledge tracing model based on theHyperMixer framework,which uniquely integrates global and localMixer feature extractors.This architecture enables more effective extraction of both long-terminteraction trends and recent learning behaviors,addressing limitations in currentmodels thatmay overlook these key aspects.Empirical evaluations on twowidely-used datasets,ASSIS Tments2009 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.

关 键 词：Knowledge tracing multilayer perceptron channel mixer sequence mixer 

分 类 号：TP391.1[自动化与计算机技术—计算机应用技术]