GELT: A graph embeddings based lite-transformer for knowledge tracing

被引:0
|
作者
Liang, Zhijie [1 ]
Wu, Ruixia [2 ]
Liang, Zhao [3 ]
Yang, Juan [1 ]
Wang, Ling [1 ]
Su, Jianyu [1 ]
机构
[1] Sichuan Normal Univ, Sch Comp Sci, Chengdu, Sichuan, Peoples R China
[2] Sichuan Water Conservancy Vocat Coll, Sch Marxism, Chengdu, Sichuan, Peoples R China
[3] Southwest Petr Univ, Network & Informat Ctr, Chengdu 610500, Peoples R China
来源
PLOS ONE | 2024年 / 19卷 / 05期
关键词
CONVERGENCE; TUTORS;
D O I
10.1371/journal.pone.0301714
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
The development of intelligent education has led to the emergence of knowledge tracing as a fundamental task in the learning process. Traditionally, the knowledge state of each student has been determined by assessing their performance in previous learning activities. In recent years, Deep Learning approaches have shown promising results in capturing complex representations of human learning activities. However, the interpretability of these models is often compromised due to the end-to-end training strategy they employ. To address this challenge, we draw inspiration from advancements in graph neural networks and propose a novel model called GELT (Graph Embeddings based Lite-Transformer). The purpose of this model is to uncover and understand the relationships between skills and questions. Additionally, we introduce an energy-saving attention mechanism for predicting knowledge states that is both simple and effective. This approach maintains high prediction accuracy while significantly reducing computational costs compared to conventional attention mechanisms. Extensive experimental results demonstrate the superior performance of our proposed model compared to other state-of-the-art baselines on three publicly available real-world datasets for knowledge tracking.
引用
收藏
页数:22
相关论文
共 50 条
  • [31] Continual Learning of Knowledge Graph Embeddings
    Daruna, Angel
    Gupta, Mehul
    Sridharan, Mohan
    Chernova, Sonia
    IEEE ROBOTICS AND AUTOMATION LETTERS, 2021, 6 (02) : 1128 - 1135
  • [32] Geometry Interaction Knowledge Graph Embeddings
    Cao, Zongsheng
    Xu, Qianqian
    Yang, Zhiyong
    Cao, Xiaochun
    Huang, Qingming
    THIRTY-SIXTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE / THIRTY-FOURTH CONFERENCE ON INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE / THE TWELVETH SYMPOSIUM ON EDUCATIONAL ADVANCES IN ARTIFICIAL INTELLIGENCE, 2022, : 5521 - 5529
  • [33] Hyperbolic Knowledge Graph Embeddings for Knowledge Base Completion
    Kolyvakis, Prodromos
    Kalousis, Alexandros
    Kiritsis, Dimitris
    SEMANTIC WEB (ESWC 2020), 2020, 12123 : 199 - 214
  • [34] Geometric Algebra Based Embeddings for Static and Temporal Knowledge Graph Completion
    Xu, Chengjin
    Nayyeri, Mojtaba
    Chen, Yung-Yu
    Lehmann, Jens
    IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, 2023, 35 (05) : 4838 - 4851
  • [35] Graph-based effective knowledge tracing via subject knowledge mapping
    Yang, Ziyan
    Hu, Jia
    Zhong, Shaochun
    Yang, Lan
    Min, Geyong
    EDUCATION AND INFORMATION TECHNOLOGIES, 2024,
  • [36] Generative adversarial networks based on Wasserstein distance for knowledge graph embeddings
    Dai, Yuanfei
    Wang, Shiping
    Chen, Xing
    Xu, Chaoyang
    Guo, Wenzhong
    KNOWLEDGE-BASED SYSTEMS, 2020, 190
  • [37] Heterogeneous graph-based knowledge tracing with spatiotemporal evolution
    Yang, Huali
    Hu, Shengze
    Geng, Jing
    Huang, Tao
    Hu, Junjie
    Zhang, Hao
    Zhu, Qiang
    EXPERT SYSTEMS WITH APPLICATIONS, 2024, 238
  • [38] Knowledge Tracing Model Based on Graph Temporal Fusion Networks
    Huang, Meng
    Wei, Ting
    INTERNATIONAL JOURNAL OF DATA WAREHOUSING AND MINING, 2024, 20 (01) : 1 - 17
  • [39] GIKT: A Graph-Based Interaction Model for Knowledge Tracing
    Yang, Yang
    Shen, Jian
    Qu, Yanru
    Liu, Yunfei
    Wang, Kerong
    Zhu, Yaoming
    Zhang, Weinan
    Yu, Yong
    MACHINE LEARNING AND KNOWLEDGE DISCOVERY IN DATABASES, ECML PKDD 2020, PT I, 2021, 12457 : 299 - 315
  • [40] Towards Understanding the Impact of Graph Structure on Knowledge Graph Embeddings
    Dave, Brandon
    Christou, Antrea
    Shimizu, Cogan
    NEURAL-SYMBOLIC LEARNING AND REASONING, PT II, NESY 2024, 2024, 14980 : 41 - 50