Knowledge Tracing Model Based on the Fusion of Sequence Features and Learning Processes
Knowledge tracing is a novel field that merges artificial intelligence and education with the aim of evaluating the knowledge state of students through an interactive sequence of exercises that they have completed in the past.This is a core technology for implementing large-scale personalized learning services.With the widespread application of deep learning in computer vision,Natural Language Processing(NLP),recommendation systems,and other fields,a significant number of neural network-based methods,known as Deep Knowledge Tracing(DKT)models,have emerged in the field of knowledge tracing.To address the shortcomings of existing DKT models in terms of interpretability and accuracy,this study proposes a knowledge tracing model called SLKT that integrates sequence features with learning processes and includes knowledge state,sequence feature,and prediction modules.The knowledge state module is used to simulate the learning process of students,and the sequence feature module captures their recent learning status.Through the integration of sequence features and the learning process,the SLKT model effectively solves the problems of knowledge state modeling methods that cannot consider learners'recent learning status.A dynamic Q matrix with constraints is also proposed to represent the relationship between exercises and knowledge points to better model the learning process of the learner.This ensures better interpretability,and effectively improves model accuracy.In this study,the superior performance of the proposed model is verified using the Area Under Curve(AUC)and accuracy metrics,comparing multiple sets of experiments using depth tracing models such as DKT,Dynamic Key-Value Memory Network(DKVMN),Self-Attentive Knowledge Tracing(SAKT),and Convolutional Knowledge Tracing(CKT)on public datasets in three knowledge tracing fields.
smart educationdeep learningknowledge trackinglearning process modelingQ matrix
万方数据
维普
