CMKT: Concept Map Driven Knowledge Tracing

被引:18
|
作者
Lu, Yu [1 ]
Chen, Penghe [1 ]
Pian, Yang [2 ]
Zheng, Vincent W. [3 ]
机构
[1] Beijing Normal Univ, Fac Educ, Adv Innovat Ctr Future Educ, Sch Educ Technol, Beijing 100875, Peoples R China
[2] Beijing Normal Univ, Fac Educ, Sch Educ Technol, Beijing 100875, Peoples R China
[3] Adv Digital Sci Ctr, Singapore 138602, Singapore
来源
基金
中国国家自然科学基金;
关键词
Mathematical models; Topology; Predictive models; Knowledge engineering; Data models; Network topology; State estimation; Concept map; constraint learning; knowledge tracing; learner modeling; network embedding; GRAPH; PREDICTION; SYSTEM;
D O I
10.1109/TLT.2022.3196355
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
In this article, we advocate for and propose a novel concept map driven knowledge tracing (CMKT) model, which utilizes educational concept map for learner modeling. This article particularly addresses the issue of learner data sparseness caused by the unwillingness to practice and irregular learning behaviors on the learner side. CMKT considers the concept map as a new information source and explicitly exploits its inherent information to help the estimation of the learner's knowledge state. Specifically, the pairwise educational relations in the concept map are formulated as the ordering pairs and are used as mathematical constraints for model construction. The topology information in the concept map is extracted and used as the model input by employing the network embedding techniques. Integrating both educational relation information and topology information in the concept map, CMKT adopts the recurrent neural network to perform knowledge tracing tasks. Comprehensive evaluations conducted on five public educational datasets of four different subjects (more than 8000 learners and their 300 000 records) demonstrate the promise and effectiveness of CMKT: The average area under ROC curve (AUC) and overall prediction accuracy (ACC) achieve 0.82 and 0.75, respectively, and CMKT outperforms all the baselines by at least 12.2% and 9.2% in terms of AUC and ACC.
引用
收藏
页码:467 / 480
页数:14
相关论文
共 50 条
  • [1] Enhancing knowledge tracing with concept map and response disentanglement
    Park, Soonwook
    Lee, Donghoon
    Park, Hogun
    [J]. KNOWLEDGE-BASED SYSTEMS, 2024, 302
  • [2] Concept Map and Knowledge
    Ullah, A. M. M. Sharif
    [J]. EDUCATION SCIENCES, 2020, 10 (09):
  • [3] Transcribing from the Mind To the Map: Tracing the Evolution of a Concept
    Curtis, Jacqueline W.
    [J]. GEOGRAPHICAL REVIEW, 2016, 106 (03) : 338 - 359
  • [4] Diversified Concept Attention Method for Knowledge Tracing
    Wu, Hao
    Cai, Yuekang
    [J]. COMPUTER SUPPORTED COOPERATIVE WORK AND SOCIAL COMPUTING, CHINESECSCW 2021, PT II, 2022, 1492 : 418 - 430
  • [5] Prerequisite-Driven Deep Knowledge Tracing
    Chen, Penghe
    Lu, Yu
    Zheng, Vincent W.
    Pian, Yang
    [J]. 2018 IEEE INTERNATIONAL CONFERENCE ON DATA MINING (ICDM), 2018, : 39 - 48
  • [6] Concept map-based knowledge modeling
    Coffey, JW
    [J]. 8TH WORLD MULTI-CONFERENCE ON SYSTEMICS, CYBERNETICS AND INFORMATICS, VOL I, PROCEEDINGS: INFORMATION SYSTEMS, TECHNOLOGIES AND APPLICATIONS, 2004, : 361 - 365
  • [7] Continuous Student Knowledge Tracing Using SVD and Concept Maps
    Teodorescu, Oana Maria
    Popescu, Paul Stefan
    Mocanu, Lucian Mihai
    Mihaescu, Marian Cristian
    [J]. ADVANCES IN ELECTRICAL AND COMPUTER ENGINEERING, 2021, 21 (01) : 75 - 82
  • [8] Concept Map for Clinical Recommendations Data and Knowledge Structuring
    Shakhrnarnetova, Giyzel
    Yusupova, Nafisa
    Zulkarneev, Rustem
    Khudoba, Yevgeniy
    [J]. PROCEEDINGS OF THE 8TH INTERNATIONAL CONFERENCE ON APPLIED INNOVATIONS IN IT, 2020, 8 (01): : 71 - 76
  • [9] Nodes and arcs: concept map, semiotics, and knowledge organization
    Friedman, Alon
    Smiraglia, Richard P.
    [J]. JOURNAL OF DOCUMENTATION, 2013, 69 (01) : 27 - 48
  • [10] Exercise recommendation method based on knowledge tracing and concept prerequisite relations
    Yu He
    Hailin Wang
    Yigong Pan
    Yinghua Zhou
    Guangzhong Sun
    [J]. CCF Transactions on Pervasive Computing and Interaction, 2022, 4 : 452 - 464