{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,17]],"date-time":"2025-10-17T22:40:53Z","timestamp":1760740853285,"version":"build-2065373602"},"publisher-location":"Singapore","reference-count":25,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819534555"},{"type":"electronic","value":"9789819534562"}],"license":[{"start":{"date-parts":[[2025,10,17]],"date-time":"2025-10-17T00:00:00Z","timestamp":1760659200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,10,17]],"date-time":"2025-10-17T00:00:00Z","timestamp":1760659200000},"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":[[2026]]},"DOI":"10.1007\/978-981-95-3456-2_19","type":"book-chapter","created":{"date-parts":[[2025,10,16]],"date-time":"2025-10-16T07:23:00Z","timestamp":1760599380000},"page":"269-284","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Diffusion-Driven Dual-Level Denoising: Identifying and\u00a0Mitigating Noisy Implicit Feedback for\u00a0Knowledge Tracing"],"prefix":"10.1007","author":[{"given":"Ping","family":"Zhang","sequence":"first","affiliation":[]},{"given":"Shun","family":"Mao","sequence":"additional","affiliation":[]},{"given":"Kaixian","family":"Huang","sequence":"additional","affiliation":[]},{"given":"Wanyun","family":"Cai","sequence":"additional","affiliation":[]},{"given":"Yuncheng","family":"Jiang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,10,17]]},"reference":[{"key":"19_CR1","doi-asserted-by":"crossref","unstructured":"Allen, S.E., Phillips, A.M., Kizilcec, R.F.: Student perceptions of pre-assessments: \u201cit\u2019s basically just guessing anyways\u201d. In: Proceedings of Physics Education Research Conference, pp. 27\u201332 (2021)","DOI":"10.1119\/perc.2021.pr.Allen"},{"key":"19_CR2","doi-asserted-by":"crossref","unstructured":"Baker, R., Corbett, A.T., Aleven, V.: More accurate student modeling through contextual estimation of slip and guess probabilities in Bayesian knowledge tracing. In: Proceedings of ITS, pp. 406\u2013415 (2008)","DOI":"10.1007\/978-3-540-69132-7_44"},{"key":"19_CR3","doi-asserted-by":"crossref","unstructured":"Baker, R.S., et al.: Contextual slip and prediction of student performance after use of an intelligent tutor. In: Proceedings of UMAP (2010)","DOI":"10.1007\/978-3-642-13470-8_7"},{"key":"19_CR4","doi-asserted-by":"crossref","unstructured":"Ghosh, A., Heffernan, N., Lan, A.S.: Context-aware attentive knowledge tracing. In: Proceedings of KDD, pp. 2330\u20132339 (2020)","DOI":"10.1145\/3394486.3403282"},{"key":"19_CR5","doi-asserted-by":"crossref","unstructured":"Guo, X., Huang, Z., Gao, J., Shang, M., Shu, M., Sun, J.: Enhancing knowledge tracing via adversarial training. In: Proceedings of ACM MM (2021)","DOI":"10.1145\/3474085.3475554"},{"key":"19_CR6","first-page":"6840","volume":"33","author":"J Ho","year":"2020","unstructured":"Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models. Adv. Neural. Inf. Process. Syst. 33, 6840\u20136851 (2020)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"issue":"2","key":"19_CR7","first-page":"131","volume":"11","author":"\u00d6K Kalkan","year":"2020","unstructured":"Kalkan, \u00d6.K., \u00c7uhadar, \u0130: An evaluation of 4PL IRT and Dina models for estimating pseudo-guessing and slipping parameters. J. Meas. Eval. Educ. Psychol. 11(2), 131\u2013146 (2020)","journal-title":"J. Meas. Eval. Educ. Psychol."},{"key":"19_CR8","unstructured":"Li, X., et al.: Enhancing length generalization for attention based knowledge tracing models with linear biases. In: Proceedings of IJCAI, pp. 5918\u20135926 (2024)"},{"key":"19_CR9","doi-asserted-by":"crossref","unstructured":"Li, X., et al.: Extending context window of attention based knowledge tracing models via length extrapolation. In: Proceedings of ECAI, pp. 1479\u20131486. IOS Press (2024)","DOI":"10.3233\/FAIA240651"},{"issue":"1","key":"19_CR10","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. 33(1), 100\u2013115 (2019)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"19_CR11","doi-asserted-by":"crossref","unstructured":"Liu, Q., et al.: Diffusion augmentation for sequential recommendation. In: Proceedings of CIKM, pp. 1576\u20131586. ACM (2023)","DOI":"10.1145\/3583780.3615134"},{"key":"19_CR12","doi-asserted-by":"crossref","unstructured":"Ma, H., et al.: HD-KT: advancing robust knowledge tracing via anomalous learning interaction detection. In: Proceedings of WWW (ACM Web Conference), pp. 4479\u20134488 (2024)","DOI":"10.1145\/3589334.3645718"},{"issue":"3","key":"19_CR13","doi-asserted-by":"publisher","first-page":"324","DOI":"10.1109\/TLT.2023.3259013","volume":"16","author":"S Mao","year":"2023","unstructured":"Mao, S., Zhan, J., Wang, Y., Jiang, Y.: Improving knowledge tracing via considering two types of actual differences from exercises and prior knowledge. IEEE Trans. Learn. Technol. 16(3), 324\u2013338 (2023)","journal-title":"IEEE Trans. Learn. Technol."},{"key":"19_CR14","doi-asserted-by":"crossref","unstructured":"Nagatani, K., Zhang, Q., Sato, M., Chen, Y.Y., Chen, F., Ohkuma, T.: Augmenting knowledge tracing by considering forgetting behavior. In: Proceedings of WWW, pp. 3101\u20133107 (2019)","DOI":"10.1145\/3308558.3313565"},{"key":"19_CR15","unstructured":"Pandey, S., Karypis, G.: A self-attentive model for knowledge tracing. In: Proceedings of EDM., pp. 384\u2013389. International Educational Data Mining Society (2019)"},{"key":"19_CR16","doi-asserted-by":"publisher","unstructured":"Papamitsiou, Z., Economides, A.A.: Process mining of interactions during computer-based testing for detecting and modelling guessing behavior. In: Proceedings of LCT, as part of HCI International, pp. 437\u2013449. Springer (2016). https:\/\/doi.org\/10.1007\/978-3-319-39483-1_40","DOI":"10.1007\/978-3-319-39483-1_40"},{"key":"19_CR17","unstructured":"Piech, C., et al.: Deep knowledge tracing. In: Advances in Neural Information Processing Systems, vol. 28 (2015)"},{"key":"19_CR18","doi-asserted-by":"crossref","unstructured":"Rombach, R., Blattmann, A., Lorenz, D., Esser, P., Ommer, B.: High-resolution image synthesis with latent diffusion models. In: Proceedings of CVPR, pp. 10684\u201310695 (2022)","DOI":"10.1109\/CVPR52688.2022.01042"},{"key":"19_CR19","doi-asserted-by":"crossref","unstructured":"Shen, X., et al.: Revisiting knowledge tracing: a simple and powerful model. In: Proceedings of ACM MM, pp. 263\u2013272 (2024)","DOI":"10.1145\/3664647.3681205"},{"issue":"1","key":"19_CR20","doi-asserted-by":"publisher","first-page":"105","DOI":"10.1146\/annurev-orgpsych-012420-055324","volume":"8","author":"AJ Winfred","year":"2021","unstructured":"Winfred, A.J., Hagen, E., Felix, G.J.: The lazy or dishonest respondent: detection and prevention. Annu. Rev. Organ. Psych. Organ. Behav. 8(1), 105\u2013137 (2021)","journal-title":"Annu. Rev. Organ. Psych. Organ. Behav."},{"key":"19_CR21","doi-asserted-by":"crossref","unstructured":"Yao, F., et al.: ADARD: an adaptive response denoising framework for robust learner modeling. In: Proceedings of KDD, pp. 3886\u20133895 (2024)","DOI":"10.1145\/3637528.3671684"},{"key":"19_CR22","unstructured":"Yeung, C.: Deep-IRT: make deep learning based knowledge tracing explainable using item response theory. In: Proceedings of EDM (2019)"},{"key":"19_CR23","doi-asserted-by":"crossref","unstructured":"Yeung, C.K., Yeung, D.Y.: Addressing two problems in deep knowledge tracing via prediction-consistent regularization. In: Proceedings of the Fifth Annual ACM Conference on Learning at Scale, pp. 1\u201310 (2018)","DOI":"10.1145\/3231644.3231647"},{"key":"19_CR24","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 WWW, pp. 765\u2013774 (2017)","DOI":"10.1145\/3038912.3052580"},{"key":"19_CR25","doi-asserted-by":"crossref","unstructured":"Zhao, J., Wang, W., Xu, Y., Sun, T., Feng, F., Chua, T.: Denoising diffusion recommender model. In: Proceedings of SIGIR, pp. 1370\u20131379 (2024)","DOI":"10.1145\/3626772.3657825"}],"container-title":["Lecture Notes in Computer Science","Advanced Data Mining and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-95-3456-2_19","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,17]],"date-time":"2025-10-17T22:04:53Z","timestamp":1760738693000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-95-3456-2_19"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,17]]},"ISBN":["9789819534555","9789819534562"],"references-count":25,"URL":"https:\/\/doi.org\/10.1007\/978-981-95-3456-2_19","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2025,10,17]]},"assertion":[{"value":"17 October 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"value":"ADMA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Advanced Data Mining and Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Kyoto","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Japan","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 October 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24 October 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"adma2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/adma2025.github.io\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}