Work

Mutil-View Contrastive Learning for Knowledge Tracing Based on Multi-Faceted Exercise Feature Fusion

Year: 2024

Type: conference-paper

Abstract: To address the issues of inadequate consideration of multifaceted features of exercises and dependence on large scale labeled data in existing deep knowledge tracing models, this paper proposes a multi-view contrastive learning method for knowledge tracing based on multi-faceted exercise feature fusion. This method achieves the extraction and integration of internal and external features from various aspects of exercises by constructing four views: the relationship between the exercise knowledge concept, the correlation of the exercise, the difficulty of the exercise and the discrimination of the exercise. By incorporating positive and negative instances from four views along with contrastive learning, this method enhances the relevance of similar exercise representations at both content and evaluation levels, thus obtaining high-quality latent state representations of student knowledge mastery. Experimental results demonstrate that this approach significantly improves model performance and predictive accuracy, particularly in limited data scenarios with education benchmark data set.

Source: International Conference on Information Education and Artificial Intelligence (ICIEAI)

DOI: 10.1145/3724504.3724563

Authors: Hanming Chen, Yijia Tang, Le Li

Institution: Central China Normal University

Language: English

Topic: Intelligent Tutoring Systems and Adaptive Learning

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Quality education

Open Access status: bronze

Funders: National Natural Science Foundation of China, Fundamental Research Funds for the Central Universities, Wuhan Science and Technology Project