Knowledge-Associated Embedding for Memory-Aware Knowledge Tracing
Bibliographic Data
| ID | 22106940 |
|---|---|
| Authors | Jiawei Li (0000-0001-9801-4715, South China Normal University), Yuanfei Deng (0000-0003-3912-6383, South China Normal University), Shun Mao (0000-0001-6625-2348, South China Normal University), Yixiu Qin (0000-0002-8306-4194, South China Normal University), Yuncheng Jiang (0000-0002-4540-387X, South China Normal University) |
| Year | 2024 |
| Volume | 11 |
| Issue | 3 |
| Pages | 4016-4028 |
| Publication date | 2024-06-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | IEEE Transactions on Computational Social Systems (JOURNAL) |
| Journal identifiers | ISSN: 2329-924X • E-ISSN: 2373-7476 |
| Publisher | Institute of Electrical and Electronics Engineers (IEEE) (PUBLISHER) |
| DOI | 10.1109/tcss.2023.3306909 |
| OpenAlex | W4386432034 |
| Language | EN |
| Citations received | 1 |
| References cited | 54 |
Knowledge tracing (KT) refers to predicting learners’ performance in the future according to their historical learning interactions, which has become an essential task for the computer-aided education (CAE) system. Recent studies alleviate the data sparsity problem by mining higher-order information between questions and skills. However, the effect of multiple skills in the question is not distinguished, and various learning behaviors need to be better modeled. In this article, we propose a knowledge-associated embedding for the memory-aware KT (KMKT) framework. Specifically, we first construct a question-skill bipartite graph with attribute features. A knowledge-associated embedding (KAE) module is proposed to capture the distinctiveness of multiskills via the process of knowledge propagation and knowledge aggregation based on predefined knowledge-paths. Then, to simulate the memory recall phenomenon of the learners in KT, we design a memory-aware module for long short-term memory (MA-LSTM) networks. A temporal attention layer in MA-LSTM is proposed to learn the forgetting mechanism of the human brain. Finally, we introduce a learning-gain (LG) layer to obtain learners’ benefits after each exercise. Extensive experiments on four real-world datasets illustrate that our KMKT model performs better than the other baseline models, which verifies the effectiveness of our work
Cognitive psychology · Domain knowledge · Embedding · Forgetting · Knowledge graph · Machine learning · Optimal distinctiveness theory · Recall · Tracing · Computer Science · Intelligent Tutoring Systems and Adaptive Learning · Online Learning and Analytics · Psychology · Topic Modeling · Artificial Intelligence
| Unique citing works | 1 |
|---|---|
| Citations per year | 0,5 |
| Citation span | 2024 - 2024 (1) |
| Citation velocity | recent |
| Highly cited | No |
| Citation types | Neutral: 1 |