NTM-Based Skill-Aware Knowledge Tracing for Conjunctive Skills

Abstract

Knowledge tracing (KT) is the task of modelling students’ knowledge state based on their historical interactions on intelligent tutoring systems. Existing KT models ignore the relevance among the multiple knowledge concepts of a question and characteristics of online tutoring systems. This paper proposes a neural Turing machine-based skill-aware knowledge tracing (NSKT) for conjunctive skills, which can capture the relevance among the knowledge concepts of a question to model students’ knowledge state more accurately and to discover more latent relevance among knowledge concepts effectively. We analyze the characteristics of the three real-world KT datasets in depth. Experiments on real-world datasets show that NSKT outperforms the state-of-the-art deep KT models on the AUC of prediction. This paper explores details of the prediction process of NSKT in modelling students’ knowledge state, as well as the relevance of knowledge concepts and conditional influences between exercises.

Research on Knowledge Tracing-Based Classroom Network Characteristic Learning Engagement and Temporal-Spatial Feature Fusion

“…With the rapid development of graph neural networks and their progress in spatial information feature extraction, Wu et al [27] proposed a KT model based on session graphs, and the gated graph neural network was utilized to obtain the students' knowledge state from the session graphs. To address the problem that existing KT models ignore the correlation between multiple knowledge concepts in exercise, Huang et al [28] proposed neural Turing machine-based skill-aware knowledge tracing (NSKT). It modeled students' states of knowledge more accurately by capturing potential correlations between knowledge concepts in the exercises.…”