Multi-Factors Aware Dual-Attentional Knowledge Tracing

• URL: http://arxiv.org/abs/2108.04741v1
• Date: Tue, 10 Aug 2021 15:03:28 GMT
• Title: Multi-Factors Aware Dual-Attentional Knowledge Tracing
• Authors: Moyu Zhang (1), Xinning Zhu (1), Chunhong Zhang (1), Yang Ji (1), Feng Pan (1), Changchuan Yin (1) ((1) Beijing University of Posts and Telecommunications)
• Abstract summary: We propose Multi-Factors Aware Dual-Attentional model (MF-DAKT) to model students' learning progress. To enrich questions representations, we use a pre-training method to incorporate two kinds of question information. We also apply a dual-attentional mechanism to differentiate contributions of factors and factor interactions to final prediction.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the increasing demands of personalized learning, knowledge tracing has become important which traces students' knowledge states based on their historical practices. Factor analysis methods mainly use two kinds of factors which are separately related to students and questions to model students' knowledge states. These methods use the total number of attempts of students to model students' learning progress and hardly highlight the impact of the most recent relevant practices. Besides, current factor analysis methods ignore rich information contained in questions. In this paper, we propose Multi-Factors Aware Dual-Attentional model (MF-DAKT) which enriches question representations and utilizes multiple factors to model students' learning progress based on a dual-attentional mechanism. More specifically, we propose a novel student-related factor which records the most recent attempts on relevant concepts of students to highlight the impact of recent exercises. To enrich questions representations, we use a pre-training method to incorporate two kinds of question information including questions' relation and difficulty level. We also add a regularization term about questions' difficulty level to restrict pre-trained question representations to fine-tuning during the process of predicting students' performance. Moreover, we apply a dual-attentional mechanism to differentiate contributions of factors and factor interactions to final prediction in different practice records. At last, we conduct experiments on several real-world datasets and results show that MF-DAKT can outperform existing knowledge tracing methods. We also conduct several studies to validate the effects of each component of MF-DAKT.

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