{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T05:16:41Z","timestamp":1742966201353,"version":"3.40.3"},"publisher-location":"Cham","reference-count":27,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031208614"},{"type":"electronic","value":"9783031208621"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-20862-1_26","type":"book-chapter","created":{"date-parts":[[2022,11,3]],"date-time":"2022-11-03T23:29:12Z","timestamp":1667518152000},"page":"353-365","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["APGKT: Exploiting Associative Path on\u00a0Skills Graph for\u00a0Knowledge Tracing"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0133-9762","authenticated-orcid":false,"given":"Haotian","family":"Zhang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8203-0956","authenticated-orcid":false,"given":"Chenyang","family":"Bu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0022-4103","authenticated-orcid":false,"given":"Fei","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4724-8989","authenticated-orcid":false,"given":"Shuochen","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7031-0889","authenticated-orcid":false,"given":"Yuhong","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5421-6171","authenticated-orcid":false,"given":"Xuegang","family":"Hu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,11,4]]},"reference":[{"key":"26_CR1","unstructured":"Liu, Q., Shen, S., Huang, Z., Chen, E., Zheng, Y.: A survey of knowledge tracing, arXiv preprint arXiv:2105.15106 (2021)"},{"issue":"12","key":"26_CR2","first-page":"2523","volume":"57","author":"X Hu","year":"2020","unstructured":"Hu, X., Liu, F., Bu, C.: Research advances on knowledge tracing models in educational big data. J. Comput. Res. Develop. 57(12), 2523\u20132546 (2020)","journal-title":"J. Comput. Res. Develop."},{"key":"26_CR3","doi-asserted-by":"crossref","unstructured":"Bu, C., et al.: Cognitive diagnostic model made more practical by genetic algorithm, IEEE Transactions on Emerging Topics in Computational Intelligence (TETCI) (2022)","DOI":"10.1109\/TETCI.2022.3182692"},{"key":"26_CR4","unstructured":"Tong, H., Wang, Z., Liu, Q., Zhou, Y., Han, W., HGKT: Introducing hierarchical exercise graph for knowledge tracing, arXiv preprint arXiv:2006.16915 (2020)"},{"issue":"7","key":"26_CR5","doi-asserted-by":"publisher","first-page":"2412","DOI":"10.1109\/TFUZZ.2021.3083177","volume":"30","author":"F Liu","year":"2022","unstructured":"Liu, F., Hu, X., Bu, C., Yu, K.: Fuzzy Bayesian knowledge tracing. IEEE Trans. Fuzzy Syst. (TFS) 30(7), 2412\u20132425 (2022)","journal-title":"IEEE Trans. Fuzzy Syst. (TFS)"},{"issue":"4","key":"26_CR6","doi-asserted-by":"publisher","first-page":"253","DOI":"10.1007\/BF01099821","volume":"4","author":"AT Corbett","year":"1994","unstructured":"Corbett, A.T., Anderson, J.R.: Knowledge tracing: Modeling the acquisition of procedural knowledge. User Model. User-Adap. Inter. 4(4), 253\u2013278 (1994)","journal-title":"User Model. User-Adap. Inter."},{"issue":"3","key":"26_CR7","doi-asserted-by":"publisher","first-page":"313","DOI":"10.1007\/s11257-017-9193-2","volume":"27","author":"R Pel\u00e1nek","year":"2017","unstructured":"Pel\u00e1nek, R.: Bayesian knowledge tracing, logistic models, and beyond: An overview of learner modeling techniques. User Model. User-Adap. Inter. 27(3), 313\u2013350 (2017)","journal-title":"User Model. User-Adap. Inter."},{"key":"26_CR8","unstructured":"Piech, C., et al.: Deep knowledge tracing, in: Proceedings of International Conference on Neural Information Processing Systems (NeurIPS), pp. 505\u2013513 (2015)"},{"key":"26_CR9","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"513","DOI":"10.1007\/978-3-030-89188-6_38","volume-title":"PRICAI 2021: Trends in Artificial Intelligence","author":"C Bu","year":"2021","unstructured":"Bu, C., Lu, Y., Liu, F.: Automatic graph learning with evolutionary algorithms: an experimental study. In: Pham, D.N., Theeramunkong, T., Governatori, G., Liu, F. (eds.) PRICAI 2021. LNCS (LNAI), vol. 13031, pp. 513\u2013526. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-89188-6_38"},{"key":"26_CR10","doi-asserted-by":"crossref","unstructured":"Nakagawa, H., Iwasawa, Y., Matsuo, Y.: Graph-based knowledge tracing: Modeling student proficiency using graph neural network, In: Proceedings of IEEE\/WIC\/ACM International Conference on Web Intelligence (WI), IEEE, pp. 156\u2013163 (2019)","DOI":"10.1145\/3350546.3352513"},{"key":"26_CR11","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"},{"issue":"4","key":"26_CR12","doi-asserted-by":"publisher","first-page":"450","DOI":"10.1109\/TLT.2017.2689017","volume":"10","author":"T K\u00e4ser","year":"2017","unstructured":"K\u00e4ser, T., Klingler, S., Schwing, A.G., Gross, M.: Dynamic bayesian networks for student modeling. IEEE Trans. Learn. Technol. 10(4), 450\u2013462 (2017)","journal-title":"IEEE Trans. Learn. Technol."},{"key":"26_CR13","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"164","DOI":"10.1007\/11774303_17","volume-title":"Intelligent Tutoring Systems","author":"H Cen","year":"2006","unstructured":"Cen, H., Koedinger, K., Junker, B.: Learning factors analysis \u2013 a general method for cognitive model evaluation and improvement. In: Ikeda, M., Ashley, K.D., Chan, T.-W. (eds.) ITS 2006. LNCS, vol. 4053, pp. 164\u2013175. Springer, Heidelberg (2006). https:\/\/doi.org\/10.1007\/11774303_17"},{"key":"26_CR14","unstructured":"Pavlik, P.I., Cen, H., Koedinger, K.R.: Performance factors analysis-a new alternative to knowledge tracing, In: Proceedings of Conference on Artificial Intelligence in Education: Building Learning Systems That Care: From Knowledge Representation to Affective Modelling, IOS Press, NLD, pp. 531\u2013538 (2009)"},{"key":"26_CR15","doi-asserted-by":"crossref","unstructured":"Vie, J.J., Kashima, H.: Knowledge tracing machines: Factorization machines for knowledge tracing, In: Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), vol. 33, pp. 750\u2013757 (2019)","DOI":"10.1609\/aaai.v33i01.3301750"},{"key":"26_CR16","doi-asserted-by":"crossref","unstructured":"Zhang, J., Shi, X., King, I., Yeung, D.Y.: Dynamic key-value memory networks for knowledge tracing, In: Proceedings of International Conference on World Wide Web (WWW), ACM, pp. 765\u2013774 (2017)","DOI":"10.1145\/3038912.3052580"},{"key":"26_CR17","doi-asserted-by":"crossref","unstructured":"Liu, Q., et al.: Finding fimilar exercises in online education systems, In: Proceedings of ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD \u201918, Association for Computing Machinery, New York, NY, USA, pp. 1821\u20131830 (2018)","DOI":"10.1145\/3219819.3219960"},{"key":"26_CR18","doi-asserted-by":"crossref","unstructured":"Liu, Q., et al.: Finding fimilar exercises in online education systems, In: Proceedings of ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD \u201918, Association for Computing Machinery, New York, NY, USA, pp. 1821\u20131830 (2018)","DOI":"10.1145\/3219819.3219960"},{"issue":"1","key":"26_CR19","doi-asserted-by":"publisher","first-page":"100","DOI":"10.1109\/TKDE.2019.2924374","volume":"33","author":"Q Liu","year":"2019","unstructured":"Liu, Q., et al.: EKT: exercise-aware knowledge tracing for student performance prediction. IEEE Trans. Knowl. Data Eng. (TKDE) 33(1), 100\u2013115 (2019)","journal-title":"IEEE Trans. Knowl. Data Eng. (TKDE)"},{"key":"26_CR20","unstructured":"Pandey, S., Karypis, G.: A self-attentive model for knowledge tracing, CoRR abs\/1907.06837 (2019). http:\/\/arxiv.org\/abs\/1907.06837"},{"key":"26_CR21","doi-asserted-by":"publisher","first-page":"223","DOI":"10.1016\/j.ins.2020.08.017","volume":"545","author":"X Wang","year":"2021","unstructured":"Wang, X., Mei, X., Huang, Q., Han, Z., Huang, C.: Fine-grained learning performance prediction via adaptive sparse self-attention networks. Inf. Sci. 545, 223\u2013240 (2021)","journal-title":"Inf. Sci."},{"key":"26_CR22","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"364","DOI":"10.1007\/978-3-030-52240-7_66","volume-title":"Artificial Intelligence in Education","author":"J Zhu","year":"2020","unstructured":"Zhu, J., Yu, W., Zheng, Z., Huang, C., Tang, Y., Fung, G.P.C.: Learning from interpretable analysis: attention-based knowledge tracing. In: Bittencourt, I.I., Cukurova, M., Muldner, K., Luckin, R., Mill\u00e1n, E. (eds.) AIED 2020. LNCS (LNAI), vol. 12164, pp. 364\u2013368. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-52240-7_66"},{"key":"26_CR23","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: Proceedings of LAK21: International Learning Analytics and Knowledge Conference, LAK21, Association for Computing Machinery, New York, NY, USA, pp. 490\u2013496 (2021)","DOI":"10.1145\/3448139.3448188"},{"issue":"1","key":"26_CR24","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1080\/00461527909529207","volume":"14","author":"JM Royer","year":"1979","unstructured":"Royer, J.M.: Theories of the transfer of learning. Educ. Psychol. 14(1), 53\u201369 (1979)","journal-title":"Educ. Psychol."},{"key":"26_CR25","doi-asserted-by":"crossref","unstructured":"Tong, S., et al.: Structure-based knowledge tracing: An influence propagation view, In: Proceedings of IEEE International Conference on Data Mining (ICDM), IEEE, pp. 541\u2013550 (2020)","DOI":"10.1109\/ICDM50108.2020.00063"},{"key":"26_CR26","doi-asserted-by":"publisher","first-page":"510","DOI":"10.1016\/j.ins.2021.08.100","volume":"580","author":"X Song","year":"2021","unstructured":"Song, X., Li, J., Tang, Y., Zhao, T., Chen, Y., Guan, Z.: JKT: a joint graph convolutional network based deep knowledge tracing. Inf. Sci. 580, 510\u2013523 (2021)","journal-title":"Inf. Sci."},{"key":"26_CR27","first-page":"1","volume":"7","author":"J Dem\u0161ar","year":"2006","unstructured":"Dem\u0161ar, J.: Statistical comparisons of classifiers over multiple data sets. J. Mach. Learn. Res. 7, 1\u201330 (2006)","journal-title":"J. Mach. Learn. Res."}],"container-title":["Lecture Notes in Computer Science","PRICAI 2022: Trends in Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-20862-1_26","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,11,3]],"date-time":"2022-11-03T23:34:03Z","timestamp":1667518443000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-20862-1_26"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031208614","9783031208621"],"references-count":27,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-20862-1_26","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"4 November 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PRICAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Pacific Rim International Conference on Artificial Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Shangai","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10 November 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13 November 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"pricai2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/pricai.org\/2022\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Easychair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"432","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"91","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"39","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"21% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"7-8","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"n\/a","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}