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Personalized Learning Resource Recommendation Framework Based on Knowledge Map

Published:09 January 2024Publication History

ABSTRACT

The knowledge space theory can better understand the knowledge concept and its internal connection by analyzing and understanding the connection and combination of knowledge concepts. A personalized learning resource recommendation framework based on knowledge space theory is designed to help students better understand and master subject knowledge in this paper. The framework mainly includes the following three steps: firstly, analyze the students' learning behavior, and establish the student knowledge space model; secondly, establish the subject knowledge space model by analyzing the structure and relationship of subject knowledge; finally, according to the student's The knowledge space model and subject knowledge space model design personalized learning resource recommendation algorithms to provide students with the most suitable learning resources. Experiments are conducted to verify the validity and feasibility of the framework, as well as its impact on student learning outcomes.

References

  1. Abdelrahman, G., & Wang, Q. (2021). Deep graph memory networks for forgetting-robust knowledge tracing. arXiv:2108.08105, https://doi.org/10.48550/arXiv.2108.08105Google ScholarGoogle ScholarCross RefCross Ref
  2. Zhang, J., Shi, X., King, I., & Yeung, D. Y. (2017). Dynamic key-value memory networks for knowledge tracing. International World Wide Web Conferences Steering Committee, 765-774.Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Gang Huang, Man Yuan, Chun-Sheng Li and Yong-he Wei. (2020) Personalized Knowledge Recommendation Based on Knowledge Graph in Petroleum Exploration and Development. International Journal of Pattern Recognition and Artificial Intelligence. DOI: 10.1142/S0218001420590338Google ScholarGoogle ScholarCross RefCross Ref
  4. Jinjiao Lin, Yanze Zhao, Weiyuan Huang, Chunfang Liu, Haitao Pu. (2021) Domain knowledge graph-based research progress of knowledge representation. Neural Computing and Applications (2021) 33:681–690 https://doi.org/10.1007/s00521-020-05057-5Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. Li, Y., Shao, Z., Wang, X., Zhao, X., & Guo, Y. (2018). A concept map-based learning paths automatic generation algorithm for adaptive learning systems. IEEE Access. DOI:10.1109/ACCESS.2018.2885339.Google ScholarGoogle ScholarCross RefCross Ref
  6. Ashraf E, Manickam S, Karuppayah S. A COMPREHENSIVE REVIEW OF COURSE RECOMMENDER SYSTEMS IN E-LEARNING[J]. Journal of Educators Online, 2021, 18(1).Google ScholarGoogle Scholar
  7. Nakagawa H, Iwasawa Y, Matsuo Y. Graph-based Knowledge Tracing: Modeling Student Proficiency Using Graph Neural Network[C]//Proc of the Int Conf on Web Intelligence (WI). Piscataway, NJ: IEEE, 2019: 156-163.Google ScholarGoogle Scholar
  8. Chen, N. S, Kinshuk, Wei, C. W. (2008). Mining e-learning domain concept map from academic articles. Computers & Education, 50(3), 1009-1021.Google ScholarGoogle Scholar
  9. T.Y Zhu, Z.Y Huang, E.H Chen, Cognitive Diagnosis Based Personalized Question Recommendation. Chinese Journal of Computers, 2017,40(01):176-191.Google ScholarGoogle Scholar
  10. Dela TorreJ. DINA model and parameter estimation: A didactic. Journal of Educational and Behavioral statistics, 2009,34(1):115-130Google ScholarGoogle ScholarCross RefCross Ref
  11. H.M Guo and X.C Cheng. Individual recommendation method of college physical education resources based on cognitive diagnosis model. EAI Endorsed Transactions on Scalable Information Systems.2022 Doi: 10.4108/eai.10-2-2022.173379Google ScholarGoogle Scholar
  12. Embretson, S. E., & Yang, X. D. (2013). A multicomponent latent trait model for diagnosis. Psychometrika, 78, 14–36.Google ScholarGoogle Scholar
  13. W.T Wang, H.F, Ma, Y. Zhao, Tracking knowledge proficiency of students with calibrated Q-matrix. Expert Systems with Applications, Volume 192, 2022, 116454. DOI: 10.1016/j.eswa.2021.116454Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. Xie Zhenping, Jin Chen, Liu Yuan. Personalized Knowledge Recommendation Model based on Constructivist Learning Theory. Journal of Computer Research and Development (in China),2018,55(1):125-138, Doi: 10.7544/issn100-1239. 201820160547Google ScholarGoogle Scholar
  15. Hiromi Nakagawa, Yusuke Iwasawa, and Yutaka Matsuo (2019) Graph-based Knowledge Tracing: Modeling Student Proficiency Using Graph Neural Network. In IEEE/WIC/ACM International Conference on Web Intelligence (WI’19), October 14–17, 2019, https://doi.org/10.1145/3350546.3352513Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. Shyi-Ming Chen and Po-Jui Sue. (2013) Constructing knowledge graphs for adaptive learning systems based on data mining techniques. Expert Systems with Applications. http://dx.doi.org/10.1016/j.eswa.2012.11.018Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. Nakagawa H., Iwasawa Y., Matsuo Y.: Graph-based Knowledge Tracing: Modeling Student Proficiency Using Graph Neural Network[C]//Proc of the Int Conf on Web Intelligence (WI). Piscataway, NJ: IEEE, 2019: 156-163.Google ScholarGoogle Scholar
  18. Tong H., Zhou Y., Wang Z.: HGKT: introducing problem schema with hierarchical exercise graph for knowledge tracing[J]. arXiv e-prints, 2020: arXiv: 2006.16915.Google ScholarGoogle Scholar

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      • Published in

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        AAIA '23: Proceedings of the 2023 International Conference on Advances in Artificial Intelligence and Applications
        November 2023
        406 pages
        ISBN:9798400708268
        DOI:10.1145/3603273

        Copyright © 2023 ACM

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        • Published: 9 January 2024

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