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
  • [1] Improving Transformer-based Sequential Conversational Recommendations through Knowledge Graph Embeddings
    Petruzzelli, Alessandro
    Martina, Alessandro Francesco Maria
    Spillo, Giuseppe
    Musto, Cataldo
    de Gemmis, Marco
    Lops, Pasquale
    Semeraro, Giovanni
    PROCEEDINGS OF THE 32ND ACM CONFERENCE ON USER MODELING, ADAPTATION AND PERSONALIZATION, UMAP 2024, 2024, : 172 - 182
  • [2] Heterogeneous Graph Based Knowledge Tracing
    Luo, Yingtao
    Xiao, Bing
    Jiang, Hua
    Ma, Junliang
    2022 11TH INTERNATIONAL CONFERENCE ON EDUCATIONAL AND INFORMATION TECHNOLOGY (ICEIT 2022), 2022, : 226 - 231
  • [3] Inductive Graph-based Knowledge Tracing
    Han, Donghee
    Kim, Daehee
    Hank, Keejun
    Yit, Mun Yong
    2023 IEEE INTERNATIONAL CONFERENCE ON BIG DATA AND SMART COMPUTING, BIGCOMP, 2023, : 92 - 99
  • [4] Medical Knowledge Graph Completion Based on Word Embeddings
    Gao, Mingxia
    Lu, Jianguo
    Chen, Furong
    INFORMATION, 2022, 13 (04)
  • [5] Ultrahyperbolic Knowledge Graph Embeddings
    Xiong, Bo
    Zhu, Shichao
    Nayyeri, Mojtaba
    Xu, Chengjin
    Pan, Shirui
    Zhou, Chuan
    Staab, Steffen
    PROCEEDINGS OF THE 28TH ACM SIGKDD CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, KDD 2022, 2022, : 2130 - 2139
  • [6] Complex Knowledge Graph Embeddings Based on Convolution and Translation
    Shi, Lin
    Yang, Zhao
    Ji, Zhanlin
    Ganchev, Ivan
    MATHEMATICS, 2023, 11 (12)
  • [7] Bias in Knowledge Graph Embeddings
    Bourli, Styliani
    Pitoura, Evaggelia
    2020 IEEE/ACM INTERNATIONAL CONFERENCE ON ADVANCES IN SOCIAL NETWORKS ANALYSIS AND MINING (ASONAM), 2020, : 6 - 10
  • [8] Hypernetwork Knowledge Graph Embeddings
    Balazevic, Ivana
    Allen, Carl
    Hospedales, Timothy M.
    ARTIFICIAL NEURAL NETWORKS AND MACHINE LEARNING - ICANN 2019: WORKSHOP AND SPECIAL SESSIONS, 2019, 11731 : 553 - 565
  • [9] Quaternion Knowledge Graph Embeddings
    Zhang, Shuai
    Tay, Yi
    Yao, Lina
    Liu, Qi
    ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 32 (NIPS 2019), 2019, 32
  • [10] Debiasing knowledge graph embeddings
    Fisher, Joseph
    Mittal, Arpit
    Palfrey, Dave
    Christodoulopoulos, Christos
    PROCEEDINGS OF THE 2020 CONFERENCE ON EMPIRICAL METHODS IN NATURAL LANGUAGE PROCESSING (EMNLP), 2020, : 7332 - 7345