{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T14:44:10Z","timestamp":1773931450027,"version":"3.50.1"},"reference-count":48,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2023,12,29]],"date-time":"2023-12-29T00:00:00Z","timestamp":1703808000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["BDCC"],"abstract":"<jats:p>In the field of education, cognitive diagnosis is crucial for achieving personalized learning. The widely adopted DINA (Deterministic Inputs, Noisy And gate) model uncovers students\u2019 mastery of essential skills necessary to answer questions correctly. However, existing DINA-based approaches overlook the dependency between knowledge points, and their model training process is computationally inefficient for large datasets. In this paper, we propose a new cognitive diagnosis model called BNMI-DINA, which stands for Bayesian Network-based Multiprocess Incremental DINA. Our proposed model aims to enhance personalized learning by providing accurate and detailed assessments of students\u2019 cognitive abilities. By incorporating a Bayesian network, BNMI-DINA establishes the dependency relationship between knowledge points, enabling more accurate evaluations of students\u2019 mastery levels. To enhance model convergence speed, key steps of our proposed algorithm are parallelized. We also provide theoretical proof of the convergence of BNMI-DINA. Extensive experiments demonstrate that our approach effectively enhances model accuracy and reduces computational time compared to state-of-the-art cognitive diagnosis models.<\/jats:p>","DOI":"10.3390\/bdcc8010004","type":"journal-article","created":{"date-parts":[[2023,12,29]],"date-time":"2023-12-29T06:18:13Z","timestamp":1703830693000},"page":"4","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["BNMI-DINA: A Bayesian Cognitive Diagnosis Model for Enhanced Personalized Learning"],"prefix":"10.3390","volume":"8","author":[{"given":"Yiming","family":"Chen","sequence":"first","affiliation":[{"name":"Department of Computer Science and Technology, Anhui University, Hefei 230601, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-8849-4432","authenticated-orcid":false,"given":"Shuang","family":"Liang","sequence":"additional","affiliation":[{"name":"Department of Mathematics and Information Science, Guangzhou University, Guangzhou 510006, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,12,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"287","DOI":"10.1037\/1082-989X.11.3.287","article-title":"Measurement of psychological disorders using cognitive diagnosis models","volume":"11","author":"Templin","year":"2006","journal-title":"Psychol. Methods"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"306","DOI":"10.1109\/TLT.2023.3254544","article-title":"Dynamic Cognitive Diagnosis: An Educational Priors-Enhanced Deep Knowledge Tracing Perspective","volume":"16","author":"Wang","year":"2023","journal-title":"IEEE Trans. Learn. Technol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"171604","DOI":"10.1007\/s11704-022-1128-3","article-title":"New development of cognitive diagnosis models","volume":"17","author":"Liu","year":"2023","journal-title":"Front. Comput. Sci."},{"key":"ref_4","unstructured":"Luo, J., and Hubaux, J.P. (2006). Embedded Security in Cars: Securing Current and Future Automotive IT Applications, Springer."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"115","DOI":"10.3102\/1076998607309474","article-title":"DINA model and parameter estimation: A didactic","volume":"34","year":"2009","journal-title":"J. Educ. Behav. Stat."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"7","DOI":"10.24018\/ejmath.2023.4.4.230","article-title":"Consistency and Ability of Students Using DINA and DINO Models","volume":"4","author":"Wafa","year":"2023","journal-title":"Eur. J. Math. Stat."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Frederiksen, N., Mislevy, R.J., and Bejar, I.I. (2012). Test Theory for a New Generation of Tests, Routledge.","DOI":"10.4324\/9780203052358"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Nichols, P.D., Chipman, S.F., and Brennan, R.L. (2012). Cognitively Diagnostic Assessment, Routledge.","DOI":"10.4324\/9780203052969"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Leighton, J., and Gierl, M. (2007). Cognitive Diagnostic Assessment for Education: Theory and Applications, Cambridge University Press.","DOI":"10.1017\/CBO9780511611186"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"172","DOI":"10.1080\/15434300902985108","article-title":"Cognitive diagnosis approaches to language assessment: An overview","volume":"6","author":"Lee","year":"2009","journal-title":"Lang. Assess. Q."},{"key":"ref_11","unstructured":"Gu, Z. (2011). Maximizing the Potential of Multiple-Choice Items for Cognitive Diagnostic Assessment, University of Toronto Canada."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"391","DOI":"10.1177\/0265532215590848","article-title":"The selection of cognitive diagnostic models for a reading comprehension test","volume":"33","author":"Li","year":"2016","journal-title":"Lang. Test."},{"key":"ref_13","unstructured":"Yang, Y. (2023). Modeling Nonignorable Missingness with Response Times Using Tree-Based Framework in Cognitive Diagnostic Models, Columbia University."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"829","DOI":"10.1109\/TETCI.2022.3220812","article-title":"Cognitive diagnosis-based personalized exercise group assembly via a multi-objective evolutionary algorithm","volume":"7","author":"Yang","year":"2023","journal-title":"IEEE Trans. Emerg. Top. Comput. Intell."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"119309","DOI":"10.1016\/j.eswa.2022.119309","article-title":"ICD: A new interpretable cognitive diagnosis model for intelligent tutor systems","volume":"215","author":"Qi","year":"2023","journal-title":"Expert Syst. Appl."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"680","DOI":"10.1109\/TLT.2023.3240931","article-title":"Predicting Student Performance in Future Exams via Neutrosophic Cognitive Diagnosis in Personalized E-learning Environment","volume":"16","author":"Ma","year":"2023","journal-title":"IEEE Trans. Learn. Technol."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Gao, W., Wang, H., Liu, Q., Wang, F., Lin, X., Yue, L., Zhang, Z., Lv, R., and Wang, S. (2023, January 23\u201327). Leveraging Transferable Knowledge Concept Graph Embedding for Cold-Start Cognitive Diagnosis. Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, Taipei, Taiwan.","DOI":"10.1145\/3539618.3591774"},{"key":"ref_18","unstructured":"Wang, S., Zeng, Z., Yang, X., and Zhang, X. (2023, January 7\u201314). Self-supervised graph learning for long-tailed cognitive diagnosis. Proceedings of the AAAI Conference on Artificial Intelligence, Washington, DC, USA."},{"key":"ref_19","unstructured":"Wu, R., Liu, Q., Liu, Y., Chen, E., Su, Y., Chen, Z., and Hu, G. (2015, January 25\u201331). Cognitive modelling for predicting examinee performance. Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, Buenos Aires, Argentina."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"333","DOI":"10.1007\/BF02295640","article-title":"Higher-order latent trait models for cognitive diagnosis","volume":"69","author":"Douglas","year":"2004","journal-title":"Psychometrika"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1011","DOI":"10.3724\/SP.J.1041.2010.01011","article-title":"A polytomous cognitive diagnosis model: P-DINA model","volume":"42","author":"Tu","year":"2010","journal-title":"Acta Psychol. Sin."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1080\/10904018.2018.1500915","article-title":"A cognitive diagnostic assessment study of the listening test of the Singapore\u2013Cambridge general certificate of education O-level: Application of DINA, DINO, G-DINA, HO-DINA, and RRUM","volume":"35","author":"Aryadoust","year":"2021","journal-title":"Int. J. List."},{"key":"ref_23","first-page":"1047","article-title":"The rapid calculation method of DINA model for large scale cognitive diagnosis","volume":"46","author":"Wang","year":"2018","journal-title":"Acta Electonica Sin."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Pearl, J. (1988). Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference, Morgan Kaufmann.","DOI":"10.1016\/B978-0-08-051489-5.50008-4"},{"key":"ref_25","unstructured":"Murphy, K.P. (1998). Inference and Learning in Hybrid Bayesian Networks, Citeseer."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"118885","DOI":"10.1016\/j.eswa.2022.118885","article-title":"COVID-19 medical waste transportation risk evaluation integrating type-2 fuzzy total interpretive structural modeling and Bayesian network","volume":"213","author":"Tang","year":"2023","journal-title":"Expert Syst. Appl."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Chan, L.S., Chu, A.M., and So, M.K. (2023). A moving-window bayesian network model for assessing systemic risk in financial markets. PLoS ONE, 18.","DOI":"10.1371\/journal.pone.0279888"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"2033","DOI":"10.1111\/risa.14100","article-title":"Textual data transformations using natural language processing for risk assessment","volume":"43","author":"Kamil","year":"2023","journal-title":"Risk Anal."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"109156","DOI":"10.1016\/j.knosys.2022.109156","article-title":"A novel quantitative relationship neural network for explainable cognitive diagnosis model","volume":"250","author":"Yang","year":"2022","journal-title":"Knowl. Based Syst."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"371","DOI":"10.1023\/A:1021258506583","article-title":"Using Bayesian networks to manage uncertainty in student modeling","volume":"12","author":"Conati","year":"2002","journal-title":"User Model. User Adapt. Interact."},{"key":"ref_31","first-page":"147","article-title":"The Andes physics tutoring system: Lessons learned","volume":"15","author":"VanLehn","year":"2005","journal-title":"Int. J. Artif. Intell. Educ."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"K\u00e4ser, T., Klingler, S., Schwing, A.G., and Gross, M. (2014, January 5\u20139). Beyond knowledge tracing: Modeling skill topologies with bayesian networks. Proceedings of the Intelligent Tutoring Systems: 12th International Conference, ITS 2014, Honolulu, HI, USA.","DOI":"10.1007\/978-3-319-07221-0_23"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"313","DOI":"10.1007\/s11257-017-9193-2","article-title":"Bayesian knowledge tracing, logistic models, and beyond: An overview of learner modeling techniques","volume":"27","year":"2017","journal-title":"User Model. User Adapt. Interact."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Liu, S., Qu, H., Chen, Q., Jian, W., Liu, R., and You, L. (2022, January 15\u201318). AFMeta: Asynchronous Federated Meta-learning with Temporally Weighted Aggregation. Proceedings of the 2022 IEEE Smartworld, Ubiquitous Intelligence & Computing, Scalable Computing & Communications, Digital Twin, Privacy Computing, Metaverse, Autonomous & Trusted Vehicles (SmartWorld\/UIC\/ScalCom\/DigitalTwin\/PriComp\/Meta), Haikou, China.","DOI":"10.1109\/SmartWorld-UIC-ATC-ScalCom-DigitalTwin-PriComp-Metaverse56740.2022.00100"},{"key":"ref_35","first-page":"1","article-title":"An effective learning evaluation method based on text data with real-time attribution-a case study for mathematical class with students of junior middle school in China","volume":"22","author":"Liu","year":"2023","journal-title":"ACM Trans. Asian Low Resour. Lang. Inf. Process."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Liu, S., Yu, X., Ma, H., Wang, Z., Qin, C., and Zhang, X. (2023, January 21\u201325). Homogeneous Cohort-Aware Group Cognitive Diagnosis: A Multi-grained Modeling Perspective. Proceedings of the 32nd ACM International Conference on Information and Knowledge Management, Birmingham, UK.","DOI":"10.1145\/3583780.3615287"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"585","DOI":"10.1007\/s11280-021-00990-4","article-title":"A generalized multi-skill aggregation method for cognitive diagnosis","volume":"26","author":"Zhang","year":"2023","journal-title":"World Wide Web"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"384","DOI":"10.3389\/fpsyg.2020.00384","article-title":"Bayesian estimation of the dina model with P\u00f3lya-gamma Gibbs sampling","volume":"11","author":"Zhang","year":"2020","journal-title":"Front. Psychol."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Bi, H., Chen, E., He, W., Wu, H., Zhao, W., Wang, S., and Wu, J. (2023, January 7\u201314). BETA-CD: A Bayesian meta-learned cognitive diagnosis framework for personalized learning. Proceedings of the AAAI Conference on Artificial Intelligence, Washington, DC, USA.","DOI":"10.1609\/aaai.v37i4.25629"},{"key":"ref_40","unstructured":"McLachlan, G. (September, January 28). On Aitken\u2019s method and other approaches for accelerating convergence of the EM algorithm. Proceedings of the AC Aitken Centenary Conference, Dunedin, New Zealand."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1111\/j.2517-6161.1977.tb01600.x","article-title":"Maximum likelihood from incomplete data via the EM algorithm","volume":"39","author":"Dempster","year":"1977","journal-title":"J. R. Stat. Soc. Ser. B (Methodol.)"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1007\/BF01531004","article-title":"Logical and algorithmic properties of independence and their application to Bayesian networks","volume":"2","author":"Geiger","year":"1990","journal-title":"Ann. Math. Artif. Intell."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"279","DOI":"10.1023\/A:1017986506241","article-title":"Accelerating EM for large databases","volume":"45","author":"Thiesson","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_44","unstructured":"Chang, H.S., Hsu, H.J., and Chen, K.T. (2015, January 26\u201329). Modeling Exercise Relationships in E-Learning: A Unified Approach. Proceedings of the EDM, Madrid, Spain."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1007\/s10664-023-10314-x","article-title":"Evaluating pre-trained models for user feedback analysis in software engineering: A study on classification of app-reviews","volume":"28","author":"Hadi","year":"2023","journal-title":"Empir. Softw. Eng."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1109\/MC.2009.263","article-title":"Matrix factorization techniques for recommender systems","volume":"42","author":"Koren","year":"2009","journal-title":"Computer"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"8312","DOI":"10.1109\/TKDE.2022.3201037","article-title":"NeuralCD: A general framework for cognitive diagnosis","volume":"35","author":"Wang","year":"2022","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Li, J., Wang, F., Liu, Q., Zhu, M., Huang, W., Huang, Z., Chen, E., Su, Y., and Wang, S. (2022, January 14\u201318). HierCDF: A Bayesian Network-based Hierarchical Cognitive Diagnosis Framework. Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, DC, USA.","DOI":"10.1145\/3534678.3539486"}],"container-title":["Big Data and Cognitive Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2504-2289\/8\/1\/4\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T21:44:09Z","timestamp":1760132649000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2504-2289\/8\/1\/4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,29]]},"references-count":48,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,1]]}},"alternative-id":["bdcc8010004"],"URL":"https:\/\/doi.org\/10.3390\/bdcc8010004","relation":{},"ISSN":["2504-2289"],"issn-type":[{"value":"2504-2289","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,12,29]]}}}