{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T12:17:18Z","timestamp":1773317838122,"version":"3.50.1"},"publisher-location":"Cham","reference-count":25,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031992636","type":"print"},{"value":"9783031992643","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025]]},"DOI":"10.1007\/978-3-031-99264-3_41","type":"book-chapter","created":{"date-parts":[[2025,7,23]],"date-time":"2025-07-23T06:42:59Z","timestamp":1753252979000},"page":"328-335","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["SPAR-GNN: Knowledge Tracing with\u00a0Behavioural Patterns and\u00a0Selective LLM Feedback"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0489-5203","authenticated-orcid":false,"given":"Zhongtian","family":"Sun","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9325-1789","authenticated-orcid":false,"given":"Jingyun","family":"Wang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9258-3503","authenticated-orcid":false,"given":"Ahmed","family":"Alamri","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1454-8822","authenticated-orcid":false,"given":"Alexandra","family":"Cristea","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,7,21]]},"reference":[{"issue":"11","key":"41_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3569576","volume":"55","author":"G Abdelrahman","year":"2023","unstructured":"Abdelrahman, G., Wang, Q., Nunes, B.: Knowledge tracing: a survey. ACM Comput. Surv. 55(11), 1\u201337 (2023)","journal-title":"ACM Comput. Surv."},{"key":"41_CR2","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"353","DOI":"10.1007\/978-3-030-49663-0_42","volume-title":"Intelligent Tutoring Systems","author":"A Alamri","year":"2020","unstructured":"Alamri, A., Sun, Z., Cristea, A.I., Senthilnathan, G., Shi, L., Stewart, C.: Is MOOC learning different for dropouts? A visually-driven, multi-granularity explanatory ML approach. In: Kumar, V., Troussas, C. (eds.) ITS 2020. LNCS, vol. 12149, pp. 353\u2013363. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-49663-0_42"},{"key":"41_CR3","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1007\/978-3-030-80421-3_15","volume-title":"Intelligent Tutoring Systems","author":"A Alamri","year":"2021","unstructured":"Alamri, A., Sun, Z., Cristea, A.I., Stewart, C., Pereira, F.D.: MOOC next week dropout prediction: weekly assessing time and learning patterns. In: Cristea, A.I., Troussas, C. (eds.) ITS 2021. LNCS, vol. 12677, pp. 119\u2013130. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-80421-3_15"},{"issue":"1","key":"41_CR4","doi-asserted-by":"publisher","first-page":"206","DOI":"10.1515\/edu-2022-0013","volume":"4","author":"M Carlon","year":"2022","unstructured":"Carlon, M., Cross, J.S.: Knowledge tracing for adaptive learning in a metacognitive tutor. Open Educ. Stud. 4(1), 206\u2013224 (2022)","journal-title":"Open Educ. Stud."},{"key":"41_CR5","doi-asserted-by":"publisher","first-page":"243","DOI":"10.1007\/s11257-009-9063-7","volume":"19","author":"M Feng","year":"2009","unstructured":"Feng, M., Heffernan, N., Koedinger, K.: Addressing the assessment challenge with an online system that tutors as it assesses. User Model. User-Adap. Inter. 19, 243\u2013266 (2009)","journal-title":"User Model. User-Adap. Inter."},{"issue":"3","key":"41_CR6","first-page":"31","volume":"12","author":"T Gervet","year":"2020","unstructured":"Gervet, T., Koedinger, K., Schneider, J., Mitchell, T., et al.: When is deep learning the best approach to knowledge tracing? J. Educ. Data Mining 12(3), 31\u201354 (2020)","journal-title":"J. Educ. Data Mining"},{"key":"41_CR7","doi-asserted-by":"crossref","unstructured":"Ghosh, A., Heffernan, N., Lan, A.S.: Context-aware attentive knowledge tracing. In: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2330\u20132339 (2020)","DOI":"10.1145\/3394486.3403282"},{"key":"41_CR8","unstructured":"Harit, A., Sun, Z., Yu, J., Al\u00a0Moubayed, N.: Monitoring behavioral changes using spatiotemporal graphs: a case study on the studentlife dataset. In: NeurIPS 2024 Workshop on Behavioral Machine Learning (2024)"},{"key":"41_CR9","unstructured":"Harit, A., Sun, Z., Yu, J., Moubayed, N.A.: Breaking down financial news impact: a novel AI approach with geometric hypergraphs. arXiv preprint arXiv:2409.00438 (2024)"},{"key":"41_CR10","unstructured":"Liu, Z., Liu, Q., Chen, J., Huang, S., Tang, J., Luo, W.: pyKT: a python library to benchmark deep learning based knowledge tracing models. In: Thirty-Sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (2022)"},{"key":"41_CR11","unstructured":"Neshaei, S.P., Davis, R.L., Hazimeh, A., Lazarevski, B., Dillenbourg, P., K\u00e4ser, T.: Towards modeling learner performance with large language models. arXiv preprint arXiv:2403.14661 (2024)"},{"key":"41_CR12","unstructured":"Pandey, S., Karypis, G.: A self-attentive model for knowledge tracing. arXiv preprint arXiv:1907.06837 (2019)"},{"key":"41_CR13","unstructured":"Piech, C., et al.: Deep knowledge tracing. In: Advances in Neural Information Processing Systems, vol. 28 (2015)"},{"key":"41_CR14","doi-asserted-by":"crossref","unstructured":"Pu, S., Becker, L.: Self-attention in knowledge tracing: why it works. In: International Conference on Artificial Intelligence in Education, pp. 731\u2013736. Springer (2022)","DOI":"10.1007\/978-3-031-11644-5_75"},{"key":"41_CR15","unstructured":"Sarsa, S., Leinonen, J., Hellas, A.: Empirical evaluation of deep learning models for knowledge tracing: of hyperparameters and metrics on performance and replicability. arXiv preprint arXiv:2112.15072 (2021)"},{"key":"41_CR16","doi-asserted-by":"crossref","unstructured":"Shin, D., Shim, Y., Yu, H., Lee, S., Kim, B., Choi, Y.: Saint+: integrating temporal features for ednet correctness prediction. In: LAK21: 11th International Learning Analytics and Knowledge Conference, pp. 490\u2013496 (2021)","DOI":"10.1145\/3448139.3448188"},{"key":"41_CR17","doi-asserted-by":"publisher","first-page":"165","DOI":"10.1016\/j.aiopen.2023.10.002","volume":"4","author":"Z Sun","year":"2023","unstructured":"Sun, Z., Harit, A., Cristea, A.I., Wang, J., Lio, P.: Money: ensemble learning for stock price movement prediction via a convolutional network with adversarial hypergraph model. AI Open 4, 165\u2013174 (2023)","journal-title":"AI Open"},{"key":"41_CR18","doi-asserted-by":"crossref","unstructured":"Sun, Z., Harit, A., Cristea, A.I., Wang, J., Lio, P.: A rewiring contrastive patch performermixer framework for graph representation learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5930\u20135939. IEEE (2023)","DOI":"10.1109\/BigData59044.2023.10386951"},{"key":"41_CR19","doi-asserted-by":"crossref","unstructured":"Sun, Z., Harit, A., Cristea, A.I., Yu, J., Shi, L., Al\u00a0Moubayed, N.: Contrastive learning with heterogeneous graph attention networks on short text classification. In: 2022 International Joint Conference on Neural Networks (IJCNN), pp.\u00a01\u20136. IEEE (2022)","DOI":"10.1109\/IJCNN55064.2022.9892257"},{"key":"41_CR20","doi-asserted-by":"crossref","unstructured":"Sun, Z., Harit, A., Yu, J., Wang, J., Li\u00f2, P.: Advanced hypergraph mining for web applications using sphere neural networks (2025)","DOI":"10.1145\/3701716.3715577"},{"key":"41_CR21","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2022.117681","volume":"206","author":"Z Wu","year":"2022","unstructured":"Wu, Z., Huang, L., Huang, Q., Huang, C., Tang, Y.: SGKT: session graph-based knowledge tracing for student performance prediction. Expert Syst. Appl. 206, 117681 (2022)","journal-title":"Expert Syst. Appl."},{"key":"41_CR22","unstructured":"Wynn, A., Wang, J., Sun, Z., Shimada, A.: Analysing learner behaviour in an ontology-based e-learning system: a graph neural network approach (2024)"},{"key":"41_CR23","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"299","DOI":"10.1007\/978-3-030-67658-2_18","volume-title":"Machine Learning and Knowledge Discovery in Databases","author":"Y Yang","year":"2021","unstructured":"Yang, Y., et al.: GIKT: a graph-based interaction model for knowledge tracing. In: Hutter, F., Kersting, K., Lijffijt, J., Valera, I. (eds.) ECML PKDD 2020. LNCS (LNAI), vol. 12457, pp. 299\u2013315. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-67658-2_18"},{"key":"41_CR24","doi-asserted-by":"crossref","unstructured":"Yu, J., Sun, Z., Luo, S.: Adversarial diffusion model for unsupervised domain-adaptive semantic segmentation. arXiv preprint arXiv:2412.16859 (2024)","DOI":"10.2139\/ssrn.4923258"},{"key":"41_CR25","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"171","DOI":"10.1007\/978-3-642-39112-5_18","volume-title":"Artificial Intelligence in Education","author":"MV Yudelson","year":"2013","unstructured":"Yudelson, M.V., Koedinger, K.R., Gordon, G.J.: Individualized Bayesian knowledge tracing models. In: Lane, H.C., Yacef, K., Mostow, J., Pavlik, P. (eds.) AIED 2013. LNCS (LNAI), vol. 7926, pp. 171\u2013180. Springer, Heidelberg (2013). https:\/\/doi.org\/10.1007\/978-3-642-39112-5_18"}],"container-title":["Communications in Computer and Information Science","Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners, Doctoral Consortium, Blue Sky, and WideAIED"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-99264-3_41","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,7]],"date-time":"2025-09-07T21:02:53Z","timestamp":1757278973000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-99264-3_41"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9783031992636","9783031992643"],"references-count":25,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-99264-3_41","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"value":"1865-0929","type":"print"},{"value":"1865-0937","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"21 July 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"AIED","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Artificial Intelligence in Education","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Palermo","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 July 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 July 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"aied2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/aied2025.itd.cnr.it\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}