{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,4]],"date-time":"2026-03-04T19:31:10Z","timestamp":1772652670719,"version":"3.50.1"},"reference-count":31,"publisher":"Elsevier BV","issue":"5","license":[{"start":{"date-parts":[[2025,7,15]],"date-time":"2025-07-15T00:00:00Z","timestamp":1752537600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,7,15]],"date-time":"2025-07-15T00:00:00Z","timestamp":1752537600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"Nature Science Foundation of Shanghai","award":["22ZR1416500"],"award-info":[{"award-number":["22ZR1416500"]}]},{"name":"Nature Science Foundation of Shanghai","award":["22ZR1416500"],"award-info":[{"award-number":["22ZR1416500"]}]},{"name":"Nature Science Foundation of Shanghai","award":["22ZR1416500"],"award-info":[{"award-number":["22ZR1416500"]}]},{"name":"Nature Science Foundation of Shanghai","award":["22ZR1416500"],"award-info":[{"award-number":["22ZR1416500"]}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2024YFC3307700"],"award-info":[{"award-number":["2024YFC3307700"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2024YFC3307700"],"award-info":[{"award-number":["2024YFC3307700"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J Artif Intell Educ"],"published-print":{"date-parts":[[2025,12]]},"DOI":"10.1007\/s40593-025-00500-x","type":"journal-article","created":{"date-parts":[[2025,7,15]],"date-time":"2025-07-15T18:53:20Z","timestamp":1752605600000},"page":"3270-3293","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A Novel Deep Knowledge Tracing Model with Problem Complexity and State Stability"],"prefix":"10.1016","volume":"35","author":[{"given":"Xinxin","family":"Li","sequence":"first","affiliation":[]},{"given":"Fei","family":"Luo","sequence":"additional","affiliation":[]},{"given":"Junhai","family":"Ouyang","sequence":"additional","affiliation":[]},{"given":"Luis Rojas","family":"Pino","sequence":"additional","affiliation":[]},{"given":"Wenhai","family":"Li","sequence":"additional","affiliation":[]},{"given":"Weichao","family":"Ding","sequence":"additional","affiliation":[]},{"given":"Chunhua","family":"Gu","sequence":"additional","affiliation":[]}],"member":"78","published-online":{"date-parts":[[2025,7,15]]},"reference":[{"key":"500_CR1","doi-asserted-by":"publisher","first-page":"30789","DOI":"10.1109\/ACCESS.2025.3541424","volume":"13","author":"J Ariza","year":"2025","unstructured":"Ariza, J., Benitez Restrepo, M., & Hern\u00e1ndez Hern\u00e1ndez, C. (2025). Generative ai in engineering and computing education: A scoping review of empirical studies and educational practices. IEEE Access., 13, 30789\u201330810. https:\/\/doi.org\/10.1109\/ACCESS.2025.3541424","journal-title":"IEEE Access."},{"issue":"3","key":"500_CR2","doi-asserted-by":"publisher","first-page":"732","DOI":"10.1007\/s40593-023-00357-y","volume":"34","author":"II Bittencourt","year":"2024","unstructured":"Bittencourt, I. I., Chalco, G., Santos, J., Fernandes, S., Silva, J., Batista, N., Hutz, C., & Isotani, S. (2024). Positive artificial intelligence in education (p-aied): A roadmap. International Journal of Artificial Intelligence in Education., 34(3), 732\u2013792.","journal-title":"International Journal of Artificial Intelligence in Education."},{"key":"500_CR3","doi-asserted-by":"crossref","unstructured":"Dai, H., Yun, Y., Zhang, Y., Zhang, W., & Shang, X. (2022). Contrastive deep knowledge tracing. In: International Conference on Artificial Intelligence in Education, pp. 289\u2013292. Springer.","DOI":"10.1007\/978-3-031-11647-6_54"},{"key":"500_CR4","doi-asserted-by":"publisher","unstructured":"Fu, W.-T., Bothell, D., Douglass, S., Haimson, C., Sohn, M.-H., & Anderson, J. (2006). Toward a real-time model-based training system \n\n                  \n                \n. Interacting with Computers., 18(6), 1215\u20131241. https:\/\/doi.org\/10.1016\/j.intcom.2006.07.011","DOI":"10.1016\/j.intcom.2006.07.011"},{"key":"500_CR5","doi-asserted-by":"publisher","unstructured":"Guo, L., Zhang, J., Ma, G., & Dai, J. (2025). A deep knowledge tracing model based on cognitive assimilation and item response theory with better interpretability. Applied Soft Computing., 180, Article 113331. https:\/\/doi.org\/10.1016\/j.asoc.2025.113331","DOI":"10.1016\/j.asoc.2025.113331"},{"key":"500_CR6","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1016\/j.patrec.2022.11.016","volume":"165","author":"H Jiang","year":"2023","unstructured":"Jiang, H., Xiao, B., Luo, Y., & Ma, J. (2023). A self-attentive model for tracing knowledge and engagement in parallel. Pattern Recognition Letters., 165, 25\u201332. https:\/\/doi.org\/10.1016\/j.patrec.2022.11.016","journal-title":"Pattern Recognition Letters."},{"key":"500_CR7","first-page":"2330","volume":"2022","author":"W Lee","year":"2022","unstructured":"Lee, W., Chun, J., Lee, Y., Park, K., & Park, S. (2022). Contrastive learning for knowledge tracing. Proceedings of the ACM Web Conference, 2022, 2330\u20132338.","journal-title":"Proceedings of the ACM Web Conference"},{"key":"500_CR8","doi-asserted-by":"crossref","unstructured":"Liu, Q., Huang, Z., Yin, Y., Chen, E., Xiong, H., Su, Y., & Hu, G. (2021). Ekt: Exercise-aware knowledge tracing for student performance prediction, vol. 33, pp. 100\u2013115.","DOI":"10.1109\/TKDE.2019.2924374"},{"key":"500_CR9","doi-asserted-by":"crossref","unstructured":"Liu, Y., Yang, Y., Chen, X., Shen, J., Zhang, H., & Yu, Y. (2020). Improving knowledge tracing via pre-training question embeddings. arXiv:2012.05031, 1577.","DOI":"10.24963\/ijcai.2020\/219"},{"key":"500_CR10","doi-asserted-by":"publisher","unstructured":"Li, Q., Yuan, X., Yue, J., Shen, X., Liang, R., Liu, S., & Yan, Z. (2025). Dual-view multi-scale cognitive representation for deep knowledge tracing. Knowledge-Based Systems., 310, Article 113010. https:\/\/doi.org\/10.1016\/j.knosys.2025.113010","DOI":"10.1016\/j.knosys.2025.113010"},{"key":"500_CR11","doi-asserted-by":"publisher","unstructured":"Lu, G., Niu, K., Peng, X., Zhou, Y., Zhang, K., & Tai, W. (2024). Self-kt: Self-attentive knowledge tracing with feature fusion pre-training in online education. In: 2024 International Joint Conference on Neural Networks (IJCNN), pp. 1\u20138. https:\/\/doi.org\/10.1109\/IJCNN60899.2024.10651418","DOI":"10.1109\/IJCNN60899.2024.10651418"},{"key":"500_CR12","doi-asserted-by":"crossref","unstructured":"Minn, S., Yu, Y., Desmarais, M.C., Zhu, F., & Vie, J.-J. (2018). Deep knowledge tracing and dynamic student classification for knowledge tracing, 1182\u20131187. IEEE.","DOI":"10.1109\/ICDM.2018.00156"},{"issue":"10","key":"500_CR13","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3561970","volume":"55","author":"N Rethmeier","year":"2023","unstructured":"Rethmeier, N., & Augenstein, I. (2023). A primer on contrastive pretraining in language processing: Methods, lessons learned, and perspectives. ACM Computing Surveys., 55(10), 1\u201317.","journal-title":"ACM Computing Surveys."},{"key":"500_CR14","doi-asserted-by":"crossref","unstructured":"Shen, S., Huang, Z., Liu, Q., Su, Y., Wang, S., & Chen, E. (2022). Assessing student\u2019s dynamic knowledge state by exploring the question difficulty effect, 427\u2013437.","DOI":"10.1145\/3477495.3531939"},{"issue":"5","key":"500_CR15","doi-asserted-by":"publisher","first-page":"634","DOI":"10.1109\/TLT.2022.3190055","volume":"15","author":"SB Shum","year":"2022","unstructured":"Shum, S. B., Littlejohn, A., Kitto, K., & Crick, R. (2022). Framing professional learning analytics as reframing oneself. IEEE Transactions on Learning Technologies., 15(5), 634\u2013649. https:\/\/doi.org\/10.1109\/TLT.2022.3190055","journal-title":"IEEE Transactions on Learning Technologies."},{"key":"500_CR16","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. (2021). Jkt: A joint graph convolutional network based deep knowledge tracing. Information Sciences., 580, 510\u2013523.","journal-title":"Information Sciences."},{"key":"500_CR17","doi-asserted-by":"crossref","unstructured":"Su, Y., Liu, Q., Liu, Q., Huang, Z., Yin, Y., Chen, E., Ding, C., Wei, S., & Hu, G. (2018). Exercise-enhanced sequential modeling for student performance prediction. In: Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence.","DOI":"10.1609\/aaai.v32i1.11864"},{"key":"500_CR18","doi-asserted-by":"crossref","unstructured":"Sun, J., Yu, F., Liu, S., Luo, Y., Liang, R., & Shen, X. (2023). Adversarial bootstrapped question representation learning for knowledge tracing. In: Proceedings of the 31st ACM International Conference on Multimedia, pp. 8016\u20138025.","DOI":"10.1145\/3581783.3612044"},{"issue":"1","key":"500_CR19","doi-asserted-by":"publisher","first-page":"2838","DOI":"10.1109\/TCE.2023.3293953","volume":"70","author":"J Sun","year":"2023","unstructured":"Sun, J., Du, S., Liu, Z., Yu, F., Liu, S., & Shen, X. (2023). Weighted heterogeneous graph-based three-view contrastive learning for knowledge tracing in personalized e-learning systems. IEEE Transactions on Consumer Electronics., 70(1), 2838\u20132847.","journal-title":"IEEE Transactions on Consumer Electronics."},{"key":"500_CR20","doi-asserted-by":"crossref","unstructured":"Tsutsumi, E., Nishio, T., & Ueno, M. (2024). Deep-irt with a temporal convolutional network for reflecting students\u2019 long-term history of ability data. In: International Conference on Artificial Intelligence in Education, pp. 250\u2013264. Springer.","DOI":"10.1007\/978-3-031-64302-6_18"},{"key":"500_CR21","doi-asserted-by":"crossref","unstructured":"Wang, Z., Feng, X., Tang, J., Huang, G.Y., & Liu, Z. (2019). Deep knowledge tracing with side information. In: Proceedings of the 28th International Joint Conference on Artificial Intelligence, pp. 3859\u20133865.","DOI":"10.1007\/978-3-030-23207-8_56"},{"issue":"1","key":"500_CR22","doi-asserted-by":"publisher","first-page":"97","DOI":"10.1007\/s40593-023-00342-5","volume":"34","author":"B Williamson","year":"2024","unstructured":"Williamson, B. (2024). The social life of ai in education. International Journal of Artificial Intelligence in Education., 34(1), 97\u2013104.","journal-title":"International Journal of Artificial Intelligence in Education."},{"key":"500_CR23","doi-asserted-by":"publisher","unstructured":"Wong, C.S.Y., Yang, G., Chen, N.F., & Savitha, R. (2022). Incremental context aware attentive knowledge tracing. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 3993\u20133997. https:\/\/doi.org\/10.1109\/ICASSP43922.2022.9746810","DOI":"10.1109\/ICASSP43922.2022.9746810"},{"key":"500_CR24","doi-asserted-by":"publisher","first-page":"200","DOI":"10.1016\/j.ins.2022.12.075","volume":"624","author":"T Wu","year":"2023","unstructured":"Wu, T., & Ling, Q. (2023). Self-supervised heterogeneous hypergraph network for knowledge tracing. Information Sciences., 624, 200\u2013216.","journal-title":"Information Sciences."},{"key":"500_CR25","doi-asserted-by":"crossref","unstructured":"Yang, J., Duan, J., Tran, S., Xu, Y., Chanda, S., Chen, L., Zeng, B., Chilimbi, T., & Huang, J. (2022). Vision-language pre-training with triple contrastive learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 15671\u201315680. IEEE.","DOI":"10.1109\/CVPR52688.2022.01522"},{"key":"500_CR26","unstructured":"Yeung, C.-K. (2019). Deep-irt: Make deep learning based knowledge tracing explainable using item response theory. arXiv preprint arXiv:1904.11738."},{"key":"500_CR27","first-page":"855","volume":"2023","author":"Y Yin","year":"2023","unstructured":"Yin, Y., Dai, L., Huang, Z., Shen, S., Wang, F., Liu, Q., Chen, E., & Li, X. (2023). Tracing knowledge instead of patterns: Stable knowledge tracing with diagnostic transformer. Proceedings of the ACM Web Conference, 2023, 855\u2013864.","journal-title":"Proceedings of the ACM Web Conference"},{"issue":"1","key":"500_CR28","doi-asserted-by":"publisher","first-page":"335","DOI":"10.1109\/TKDE.2023.3282907","volume":"36","author":"J Yu","year":"2023","unstructured":"Yu, J., Yin, H., Xia, X., Chen, T., Li, J., & Huang, Z. (2023). Self-supervised learning for recommender systems: A survey. IEEE Transactions on Knowledge and Data Engineering., 36(1), 335\u2013355.","journal-title":"IEEE Transactions on Knowledge and Data Engineering."},{"key":"500_CR29","doi-asserted-by":"crossref","unstructured":"Zhang, J., Shi, X., King, I., Yeung, & D.-Y. (2017). Dynamic key-value memory networks for knowledge tracing. In: Proceedings of the 26th International Conference on World Wide Web, pp. 765\u2013774.","DOI":"10.1145\/3038912.3052580"},{"key":"500_CR30","doi-asserted-by":"crossref","unstructured":"Zhang, M., Zhu, X., Zhang, C., Ji, Y., Pan, F., & Yin, C. (2021). Multi-factors aware dual-attentional knowledge tracing. In: Proceedings of the 30th ACM International Conference on Information & Knowledge Management, pp. 2588\u20132597. ACM.","DOI":"10.1145\/3459637.3482372"},{"key":"500_CR31","doi-asserted-by":"crossref","unstructured":"Zhao, Y., Ma, H., Wang, J., He, X., & Chang, L. (2024). Question-response representation with dual-level contrastive learning for improving knowledge tracing. Information Sciences., 658, Article 120032.","DOI":"10.1016\/j.ins.2023.120032"}],"container-title":["International Journal of Artificial Intelligence in Education"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40593-025-00500-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s40593-025-00500-x","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40593-025-00500-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,4]],"date-time":"2026-03-04T18:12:45Z","timestamp":1772647965000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s40593-025-00500-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,15]]},"references-count":31,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2025,12]]}},"alternative-id":["500"],"URL":"https:\/\/doi.org\/10.1007\/s40593-025-00500-x","relation":{},"ISSN":["1560-4292","1560-4306"],"issn-type":[{"value":"1560-4292","type":"print"},{"value":"1560-4306","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,7,15]]},"assertion":[{"value":"2 July 2025","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 July 2025","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}