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    <journal-meta>
      <journal-id journal-id-type="ojs">JDC</journal-id>
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        <journal-title xml:lang="en">Journal of Discovery Core</journal-title>
        <abbrev-journal-title xml:lang="en">JDC</abbrev-journal-title>
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      <publisher>
        <publisher-name>Digital Intelligence Press</publisher-name>
        <publisher-loc>Hong Kong<country>HK</country><uri>https://dipscie.com/index.php/JDC/index</uri></publisher-loc>
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      <issn pub-type="epub">3135-7687</issn>
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      <article-id pub-id-type="publisher-id">20</article-id>
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      <title-group>
        <article-title xml:lang="en">&lt;bold&gt;Research on Personalized Art Learning Path Recommendation Algorithm Based on Reinforcement Learning&lt;/bold&gt;</article-title>
      </title-group>
      <contrib-group content-type="author">
        <contrib>
          <name-alternatives>
            <name name-style="western" specific-use="primary">
              <surname>Sun</surname>
              <given-names>Sinuo</given-names>
            </name>
          </name-alternatives>
          <email>sinuosun1209@gmail.com</email>
          <xref ref-type="aff" rid="aff-1"/>
        </contrib>
        <contrib corresp="yes">
          <name-alternatives>
            <name name-style="western" specific-use="primary">
              <surname>Li</surname>
              <given-names>Yansong</given-names>
            </name>
          </name-alternatives>
          <email>liyansong813@163.com</email>
        </contrib>
      </contrib-group>
      <aff id="aff-1">
        <institution content-type="orgname">Department of Art Studies, Harbin Conservatory of Music, Harbin, Heilongjiang, China</institution>
      </aff>
      <pub-date date-type="pub" publication-format="epub">
        <day>16</day>
        <month>07</month>
        <year>2026</year>
      </pub-date>
      <fpage>55</fpage>
      <lpage>76</lpage>
      <pub-history>
        <event event-type="received">
          <event-desc>Received: <date date-type="received" iso-8601-date="2026-07-16T08:36:16+00:00"><day>16</day><month>7</month><year>2026</year></date></event-desc>
        </event>
      </pub-history>
      <permissions>
        <copyright-statement>Copyright (c) 2026 Sinuo Sun, Yansong Li (Author)</copyright-statement>
        <copyright-year>2026</copyright-year>
        <copyright-holder>Sinuo Sun, Yansong Li (Author)</copyright-holder>
        <license xlink:href="https://creativecommons.org/licenses/by/4.0">
          <license-p>&lt;a rel="license" href="https://creativecommons.org/licenses/by/4.0/"&gt;&lt;img alt="Creative Commons License" src="//i.creativecommons.org/l/by/4.0/88x31.png" /&gt;&lt;/a&gt;&lt;p&gt;This work is licensed under a &lt;a rel="license" href="https://creativecommons.org/licenses/by/4.0/"&gt;Creative Commons Attribution 4.0 International License&lt;/a&gt;.&lt;/p&gt;</license-p>
        </license>
      </permissions>
      <self-uri xlink:href="https://dipscie.com/index.php/JDC/article/view/10.67541_jdc2603"/>
      <kwd-group xml:lang="en">
        <kwd>Reinforcement learning; Personalized learning path recommendation; Graph attention network; Art education; Deep Q network</kwd>
      </kwd-group>
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        <page-count count="22"/>
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    <ref-list>
      <ref id="R1">
        <mixed-citation>[1] Savage, T. (2024). Teaching to the line: how do visual arts technicians in higher education conceive of their pedagogies? (Doctoral dissertation, University for the Creative Arts). https://doi.org/10.13140/RG.2.2.18860.99203</mixed-citation>
      </ref>
      <ref id="R2">
        <mixed-citation>[2] Shao, J. (2024). Personalized Learning for Art Major Students Based on Learner Characteristics (Doctoral dissertation, Chapman University). https://doi.org/10.36837/chapman.000560</mixed-citation>
      </ref>
      <ref id="R3">
        <mixed-citation>[3] Ткач, М., Олексюк, О. М., Бобик, Л., Мимрик, М., &amp; Вей, Л. (2024). Non-linear thinking strategies in post-non-classical higher art education: A synergistic concept. Scientific Herald of Uzhhorod University, 55, 1994-2005. https://doi.org/10.54919/physics/55.2024.199kv4</mixed-citation>
      </ref>
      <ref id="R4">
        <mixed-citation>[4] Li, Y., &amp; Shi, J. (2025). Multimodal deep learning for art behavior analysis and personalized teaching path generation. Discover Artificial Intelligence, 5(1), 215. https://doi.org/10.1007/s44163-025-00480-w</mixed-citation>
      </ref>
      <ref id="R5">
        <mixed-citation>[5] Shokrzadeh, Z., Feizi-Derakhshi, M. R., Balafar, M. A., &amp; Mohasefi, J. B. (2024). Knowledge graph-based recommendation system enhanced by neural collaborative filtering and knowledge graph embedding. Ain Shams Engineering Journal, 15(1), 102263. https://doi.org/10.1016/j.asej.2023.102263</mixed-citation>
      </ref>
      <ref id="R6">
        <mixed-citation>[6] Troussas, C., &amp; Krouska, A. (2022). Path-based recommender system for learning activities using knowledge graphs. Information, 14(1), 9. https://doi.org/10.3390/info14010009</mixed-citation>
      </ref>
      <ref id="R7">
        <mixed-citation>[7] Qian, D., &amp; Luo, W. (2026). Revolutionizing arts education through 3D virtual reality: a mixed-method analysis of its impact on sculpting and carving skills among undergraduate art students. Educational technology research and development, 1-36. 1001-1036 https://doi.org/10.1007/s11423-025-10584-w</mixed-citation>
      </ref>
      <ref id="R8">
        <mixed-citation>[8] Du, W., Zhao, Y., Wang, Y., Wang, H., &amp; Yang, M. (2022). Novel machine learning approach for shape-finding design of tree-like structures. Computers &amp; Structures, 261, 106731. https://doi.org/10.1016/j.compstruc.2021.106731</mixed-citation>
      </ref>
      <ref id="R9">
        <mixed-citation>[9] Zhao, L. T., Wang, D. S., Liang, F. Y., &amp; Chen, J. (2023). A recommendation system for effective learning strategies: An integrated approach using context-dependent DEA. Expert Systems with Applications, 211, 118535. https://doi.org/10.1016/j.eswa.2022.118535</mixed-citation>
      </ref>
      <ref id="R10">
        <mixed-citation>[10] Garg, S., &amp; Roy, D. (2022). A birds eye view on knowledge graph embeddings, software libraries, applications and challenges. arXiv preprint arXiv:2205.09088. https://doi.org/10.48550/arXiv.2205.09088</mixed-citation>
      </ref>
      <ref id="R11">
        <mixed-citation>[11] Wani, A. A. (2025). Comprehensive review of dimensionality reduction algorithms: challenges, limitations, and innovative solutions. PeerJ Computer Science, 11, e3025. https://doi.org/10.7717/peerj-cs.3025</mixed-citation>
      </ref>
      <ref id="R12">
        <mixed-citation>[12] Wang, Z., Li, S., Liu, Q., Pan, Z., &amp; Sun, X. (2026). MKAN-Refine: Fine-Grained Crisis Information Mining via Reliability-Aware Nonlinear Refinement and Kolmogorov–Arnold Networks. IEEE Access. https://doi.org/10.1109/access.2026.3688494</mixed-citation>
      </ref>
      <ref id="R13">
        <mixed-citation>[13] Li, P., &amp; Ding, Z. (2025). Application of deep learning-based personalized learning path prediction and resource recommendation for inheriting scientist spirit in graduate education. Computer Science and Information Systems, (00), 43-43. https://doi.org/10.2298/csis241125043l</mixed-citation>
      </ref>
      <ref id="R14">
        <mixed-citation>[14] Angelaki, S., Triantafyllidis, G. A., &amp; Besenecker, U. (2022). Lighting in kindergartens: Towards innovative design concepts for lighting design in kindergartens based on children’s perception of space. Sustainability, 14(4), 2302. https://doi.org/10.3390/su14042302</mixed-citation>
      </ref>
      <ref id="R15">
        <mixed-citation>[15] Newbury, R., Gu, M., Chumbley, L., Mousavian, A., Eppner, C., Leitner, J., ... &amp; Cosgun, A. (2023). Deep learning approaches to grasp synthesis: A review. IEEE Transactions on Robotics, 39(5), 3994-4015. https://doi.org/10.48550/arXiv.2207.02556</mixed-citation>
      </ref>
      <ref id="R16">
        <mixed-citation>[16] Bai, Y., Liu, Z., Guo, T., Hou, M., &amp; Xiao, K. (2025). Prerequisite relation learning: a survey and outlook. ACM Computing Surveys, 57(11), 1-28. https://doi.org/10.1145/3733593</mixed-citation>
      </ref>
      <ref id="R17">
        <mixed-citation>[17] Wang, Z., Yan, W., Zeng, C., Tian, Y., &amp; Dong, S. (2023). A unified interpretable intelligent learning diagnosis framework for learning performance prediction in intelligent tutoring systems. International Journal of Intelligent Systems, 2023(1), 4468025. https://doi.org/10.1155/2023/4468025</mixed-citation>
      </ref>
      <ref id="R18">
        <mixed-citation>[18] Winget, M., &amp; Persky, A. M. (2022). A practical review of mastery learning. American journal of pharmaceutical education, 86(10), ajpe8906. https://doi.org/10.5688/ajpe8906</mixed-citation>
      </ref>
      <ref id="R19">
        <mixed-citation>[19] Yu, S., Zeng, Y., Yang, F., &amp; Pan, Y. (2024, March). Causal-driven skill prerequisite structure discovery. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 38, No. 18, pp. 20604-20612). https://doi.org/10.1609/aaai.v38i18.30046</mixed-citation>
      </ref>
      <ref id="R20">
        <mixed-citation>[20] Vrahatis, A. G., Lazaros, K., &amp; Kotsiantis, S. (2024). Graph attention networks: a comprehensive review of methods and applications. Future Internet, 16(9), 318. https://doi.org/10.3390/fi16090318</mixed-citation>
      </ref>
      <ref id="R21">
        <mixed-citation>[21] Yu, X., Yang, S., Wang, Z., Song, S., Ma, H., Cao, Z., &amp; Zhang, X. (2025, July). LIGHT: Enhancing Learning Path Recommendation via Knowledge Topology-Aware Sequence Optimization. In Proceedings of the 48th International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 306-315). https://doi.org/10.1145/3726302.3730022</mixed-citation>
      </ref>
      <ref id="R22">
        <mixed-citation>[22] Amin, S., Uddin, M. I., Alarood, A. A., Mashwani, W. K., Alzahrani, A., &amp; Alzahrani, A. O. (2023). Smart E-learning framework for personalized adaptive learning and sequential path recommendations using reinforcement learning. IEEe Access, 11, 89769-89790. https://doi.org/10.1109/access.2023.3305584</mixed-citation>
      </ref>
      <ref id="R23">
        <mixed-citation>[23] Malashin, I., Tynchenko, V., Gantimurov, A., Nelyub, V., &amp; Borodulin, A. (2024). Applications of long short-term memory (LSTM) networks in polymeric sciences: A review. Polymers, 16(18), 2607. https://doi.org/10.3390/polym16182607</mixed-citation>
      </ref>
      <ref id="R24">
        <mixed-citation>[24] Xu, W., He, J., Li, W., He, Y., Wan, H., Qin, W., &amp; Chen, Z. (2023). Long-short-term-memory-based deep stacked sequence-to-sequence autoencoder for health prediction of industrial workers in closed environments based on wearable devices. Sensors, 23(18), 7874. https://doi.org/10.3390/s23187874</mixed-citation>
      </ref>
      <ref id="R25">
        <mixed-citation>[25] Jaramillo-Martínez, R., Chavero-Navarrete, E., &amp; Ibarra-Pérez, T. (2024). Reinforcement-learning-based path planning: A reward function strategy. Applied Sciences, 14(17), 7654. https://doi.org/10.3390/app14177654</mixed-citation>
      </ref>
      <ref id="R26">
        <mixed-citation>[26] Gallici, M., Fellows, M., Ellis, B., Pou, B., Masmitja, I., Foerster, J., &amp; Martin, M. (2025, May). Simplifying deep temporal difference learning. In International Conference on Learning Representations (Vol. 2025, pp. 78148-78190). https://doi.org/10.48550/arXiv.2407.04811</mixed-citation>
      </ref>
      <ref id="R27">
        <mixed-citation>[27] Hong, Z. W., Kumar, A., Karnik, S., Bhandwaldar, A., Srivastava, A., Pajarinen, J., ... &amp; Agrawal, P. (2023). Beyond uniform sampling: Offline reinforcement learning with imbalanced datasets. Advances in Neural Information Processing Systems, 36, 4985-5009. https://doi.org/10.48550/arXiv.2310.04413</mixed-citation>
      </ref>
      <ref id="R28">
        <mixed-citation>[28] Lockwood, O., &amp; Si, M. (2022, October). A review of uncertainty for deep reinforcement learning. In Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment (Vol. 18, No. 1, pp. 155-162). https://doi.org/10.1609/aiide.v18i1.21959</mixed-citation>
      </ref>
      <ref id="R29">
        <mixed-citation>[29] Petravičius, T. (2023). Research and analysis of reinforcement learning methods in OpenAI Gym environment (Doctoral dissertation, Kauno technologijos universitetas.).</mixed-citation>
      </ref>
      <ref id="R30">
        <mixed-citation>[30] Yadav, R. K. (2025). Modeling Memory Retention with Ebbinghaus's Forgetting Curve and Interpretable Machine Learning on Behavioral Factors. Authorea Preprints. https://doi.org/10.36227/techrxiv.174495325.58680708/v1</mixed-citation>
      </ref>
      <ref id="R31">
        <mixed-citation>[31] De Melo, C. M., Torralba, A., Guibas, L., DiCarlo, J., Chellappa, R., &amp; Hodgins, J. (2022). Next-generation deep learning based on simulators and synthetic data. Trends in cognitive sciences, 26(2), 174-187. https://doi.org/10.1016/j.tics.2021.11.008</mixed-citation>
      </ref>
      <ref id="R32">
        <mixed-citation>[32] Ghorbani, Z., Mirebeigi-Jamasbi, S. S., Hassannia Dargah, M., Nahvi, M., Hosseinikhah Manshadi, S. A., &amp; Akbarzadeh Fathabadi, Z. (2025). A novel deep learning-based model for automated tooth detection and numbering in mixed and permanent dentition in occlusal photographs. BMC Oral Health, 25(1), 455. https://doi.org/10.1186/s12903-025-05803-y</mixed-citation>
      </ref>
      <ref id="R33">
        <mixed-citation>[33] Zhang, Z. (2026). Dynamic Pricing Strategy Optimization Based on a Reinforcement Learning PPO Algorithm: An Empirical Study on Ride-Hailing Platforms. Journal of Organizational and End User Computing (JOEUC), 38(1), 1-43. https://doi.org/10.4018/JOEUC.406688</mixed-citation>
      </ref>
    </ref-list>
  </back>
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