{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,12]],"date-time":"2026-06-12T12:55:03Z","timestamp":1781268903270,"version":"3.54.1"},"reference-count":35,"publisher":"Emerald","issue":"3","funder":[{"name":"Major Scientific and Technological Innovation Platform Project of Hunan Province","award":["2024JC1003"],"award-info":[{"award-number":["2024JC1003"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62077014"],"award-info":[{"award-number":["62077014"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62477009"],"award-info":[{"award-number":["62477009"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Key Project of Scientific Research Fund of Hunan Provincial Education Department","award":["23A0061"],"award-info":[{"award-number":["23A0061"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,9,10]]},"abstract":"<jats:sec>\n                  <jats:title>Purpose<\/jats:title>\n                  <jats:p>Recently, the number of online learners and learning resources has increased dramatically, and the knowledge network generated in the e-learning platform is getting vaster and more complex than ever. Analyzing learners' potential preferences by aggregating high-level semantic information from this network and accurately modeling their cognitive states is crucial for identifying similar learners. Combining similar learners\u2019 learning records helps recommend suitable exercises to improve the effectiveness of exercise recommendations. This article tackles the challenging problem of how to aggregate high-level semantic information in a huge graph and accurately model learners' cognitive states.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Design\/methodology\/approach<\/jats:title>\n                  <jats:p>Firstly, this approach constructs e-learning environments' knowledge graphs by integrating the difficulty of exercises and characteristics of answering behaviors, and the knowledge graph attention network (KGAT) is used to train the graph embedding model of the knowledge graph. Secondly, a score reevaluation method is designed based on the coefficient of completion quality to help accurately model learners' cognitive states. Then, the learners' actual cognitive states, obtained by the cognitive diagnosis model (CDM), are innovatively incorporated into graph matching for acquiring similar subgraphs. Finally, the personalized recommendation results are ranked according to learners' interaction probability on similar exercises.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Findings<\/jats:title>\n                  <jats:p>First, the proposed method has superior exercise recommendation performance. Experiments demonstrate that, compared to the existing approach, the proposed approach has an increase rate of 3.21%, 3.32%, 3.27% and 0.38% in precision, recall, F1 score and HR@10, respectively, in the large-scale graph data scenario. Second, aggregating high-level semantic information from the knowledge network helps explore learners' potential preferences. Finally, the fine-grained scoring mechanism based on learners' exercise completion quality can better reflect the actual mastery levels of learners, which improves the accuracy of modeling their cognitive states.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Originality\/value<\/jats:title>\n                  <jats:p>First, an approach to personalized exercise recommendation is proposed via knowledge enhancement and fuzzy cognitive fusion. The experiments demonstrate the effectiveness and feasibility of this approach in a scenario with large-scale graph data. Second, this approach provides a flexible and adaptable framework. In it, the CDM can be replaced to explore for better accuracy of cognitive evaluation. Third, KGAT is employed to embed the knowledge graph in e-learning environments for aggregating high-level semantic information from the graph. Finally, a score reevaluation method is designed to analyze learners' learning behavior for accurately modeling their cognitive states.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1108\/ijicc-04-2025-0190","type":"journal-article","created":{"date-parts":[[2025,9,4]],"date-time":"2025-09-04T06:13:01Z","timestamp":1756966381000},"page":"563-585","source":"Crossref","is-referenced-by-count":5,"title":["Personalized exercise recommendation via knowledge enhancement and fuzzy cognitive fusion in large-scale e-learning environments"],"prefix":"10.1108","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1980-4709","authenticated-orcid":true,"given":"Hua","family":"Ma","sequence":"first","affiliation":[{"name":"College of Information Science and Engineering, Hunan Normal University , ,","place":["Changsha, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-7503-7664","authenticated-orcid":true,"given":"Xiangru","family":"Fu","sequence":"additional","affiliation":[{"name":"College of Information Science and Engineering, Hunan Normal University , ,","place":["Changsha, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-6000-0012","authenticated-orcid":true,"given":"Yuqi","family":"Tang","sequence":"additional","affiliation":[{"name":"College of Information Science and Engineering, Hunan Normal University , ,","place":["Changsha, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-2907-240X","authenticated-orcid":true,"given":"Xucan","family":"Yao","sequence":"additional","affiliation":[{"name":"College of Information Science and Engineering, Hunan Normal University , ,","place":["Changsha, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"140","published-online":{"date-parts":[[2025,9,5]]},"reference":[{"issue":"1","key":"2025091107264255100_ref001","doi-asserted-by":"publisher","first-page":"115","DOI":"10.3102\/1076998607309474","article-title":"DINA model and parameter estimation: a didactic","volume":"34","author":"De La Torre","year":"2009","journal-title":"Journal of Educational and Behavioral Statistics"},{"key":"2025091107264255100_ref002","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2024.111521","article-title":"Multi-knowledge enhanced graph convolution for learning resource recommendation","volume":"291","author":"Dong","year":"2024","journal-title":"Knowledge-Based Systems"},{"key":"2025091107264255100_ref003","doi-asserted-by":"publisher","first-page":"776","DOI":"10.1109\/TLT.2023.3333669","article-title":"Advanced mathematics exercise recommendation based on automatic knowledge extraction and multi-layer knowledge graph","volume":"17","author":"Dong","year":"2024","journal-title":"IEEE Transactions on Learning Technologies"},{"key":"2025091107264255100_ref004","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2022.118806","article-title":"Learning knowledge graph embedding with a dual-attention embedding network","volume":"212","author":"Fang","year":"2023","journal-title":"Expert Systems with Applications"},{"key":"2025091107264255100_ref005","doi-asserted-by":"publisher","first-page":"855","DOI":"10.1145\/2939672.2939754","article-title":"Node2vec: scalable feature learning for networks","author":"Grover","year":"2016"},{"key":"2025091107264255100_ref006","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2024.106954","article-title":"Explainable exercise recommendation with knowledge graph","volume":"183","author":"Guan","year":"2025","journal-title":"Neural Networks"},{"key":"2025091107264255100_ref007","doi-asserted-by":"publisher","first-page":"266","DOI":"10.1016\/j.ins.2020.03.014","article-title":"Knowledge modeling via contextualized representations for LSTM-based personalized exercise recommendation","volume":"523","author":"Huo","year":"2020","journal-title":"Information Sciences"},{"issue":"1","key":"2025091107264255100_ref008","doi-asserted-by":"publisher","first-page":"103","DOI":"10.11897\/SP.J.1016.2023.00103","article-title":"Personalized OJ exercise recommendation method with memory and cognition merging","volume":"46","author":"Jin","year":"2023","journal-title":"Chinese Journal of Computers"},{"key":"2025091107264255100_ref009","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2022.119122","article-title":"Knowledge graph embedding by relational rotation and complex convolution for link prediction","volume":"214","author":"Le","year":"2023","journal-title":"Expert Systems with Applications"},{"issue":"1","key":"2025091107264255100_ref010","doi-asserted-by":"publisher","DOI":"10.1155\/2023\/2578286","article-title":"Knowledge graph-enhanced intelligent tutoring system based on exercise representativeness and informativeness","volume":"2023","author":"Li","year":"2023","journal-title":"International Journal of Intelligent Systems"},{"key":"2025091107264255100_ref011","doi-asserted-by":"publisher","DOI":"10.1016\/j.chb.2025.108607","article-title":"An optimal approach for predicting cognitive performance in education based on deep learning","volume":"167","author":"Li","year":"2025","journal-title":"Computers in Human Behavior"},{"key":"2025091107264255100_ref012","doi-asserted-by":"publisher","DOI":"10.27209\/d.cnki.glniu.2022.001937","article-title":"Research and implementation of personalized question recommendation system based on knowledge graph","author":"Lin","year":"2022"},{"issue":"4","key":"2025091107264255100_ref013","doi-asserted-by":"publisher","first-page":"1057","DOI":"10.13195\/j.kzyjc.2023.1400","article-title":"A review of emotion recognition of learners for online education","volume":"39","author":"Lin","year":"2024","journal-title":"Control and Decision"},{"key":"2025091107264255100_ref014","doi-asserted-by":"publisher","first-page":"296","DOI":"10.1145\/3168361","article-title":"Fuzzy cognitive diagnosis for modelling examinee performance","volume-title":"Proceedings of ACM Transactions on Intelligent Systems and Technology (TIST)","author":"Liu","year":"2018"},{"key":"2025091107264255100_ref015","doi-asserted-by":"publisher","first-page":"281","DOI":"10.1007\/978-981-16-6471-7_22","article-title":"MOOPer: a large-scale dataset of practice-oriented online learning","volume-title":"Proceedings of Knowledge Graph and Semantic Computing","author":"Liu","year":"2021"},{"issue":"2","key":"2025091107264255100_ref016","doi-asserted-by":"publisher","first-page":"259","DOI":"10.1108\/IJICC-08-2024-0362","article-title":"Fold embedding and attention-based collaborative filtering with masking strategy for consumer products rating prediction","volume":"18","author":"Liu","year":"2025","journal-title":"International Journal of Intelligent Computing and Cybernetics"},{"key":"2025091107264255100_ref017","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1145\/3210713.3210728","article-title":"Utilizing knowledge graph and student testing behavior data for personalized exercise recommendation","author":"Lv","year":"2018"},{"issue":"5","key":"2025091107264255100_ref018","doi-asserted-by":"publisher","first-page":"8053","DOI":"10.3233\/JIFS-222627","article-title":"Learning resource recommendation via knowledge graphs and learning style clustering","volume":"44","author":"Ma","year":"2023","journal-title":"Journal of Intelligent and Fuzzy Systems"},{"issue":"5","key":"2025091107264255100_ref019","doi-asserted-by":"publisher","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 Transactions on Learning Technologies"},{"issue":"3","key":"2025091107264255100_ref020","doi-asserted-by":"publisher","first-page":"1414","DOI":"10.1109\/TLT.2024.3382217","article-title":"Personalized early warning of learning performance for college students: a multi-level approach via cognitive ability and learning state modeling","volume":"17","author":"Ma","year":"2024","journal-title":"IEEE Transactions on Learning Technologies"},{"issue":"4","key":"2025091107264255100_ref021","doi-asserted-by":"publisher","first-page":"407","DOI":"10.1111\/j.1745-3984.2008.00072.x","article-title":"Cognitive diagnostic assessment for education: theory and applications","volume":"45","author":"Nichols","year":"2008","journal-title":"Journal of Educational Measurement"},{"key":"2025091107264255100_ref022","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2022.110117","article-title":"MulOER-SAN: 2-layer multi-objective framework for exercise recommendation with self-attention networks","volume":"260","author":"Ren","year":"2023","journal-title":"Knowledge-Based Systems"},{"issue":"2","key":"2025091107264255100_ref023","doi-asserted-by":"publisher","first-page":"2159","DOI":"10.1007\/s40747-022-00905-4","article-title":"Fully adaptive recommendation paradigm: top-enhanced recommender distillation for intelligent education systems","volume":"9","author":"Ren","year":"2023","journal-title":"Complex and Intelligent Systems"},{"issue":"1","key":"2025091107264255100_ref024","first-page":"3","article-title":"Trends, stages and changes in the digitisation of higher education - excerpt I from infinite possibilities: Report on the digital development of world higher education","volume":"29","author":"SGMOEA(Secretariat of the Global MOOC and Online Education Alliance)","year":"2023","journal-title":"Chinese Journal of ICT in Education"},{"issue":"2","key":"2025091107264255100_ref025","doi-asserted-by":"publisher","first-page":"641","DOI":"10.1016\/j.ipm.2023.103411","article-title":"KRL_Match: Knowledge graph objects matching for knowledge representation learning","volume":"65","author":"Suo","year":"2023","journal-title":"Knowledge and Information Systems"},{"issue":"4","key":"2025091107264255100_ref026","doi-asserted-by":"publisher","first-page":"345","DOI":"10.1111\/j.1745-3984.1983.tb00212.x","article-title":"Rule space: An approach for dealing with misconceptions based on item response theory","volume":"20","author":"Tatsuoka","year":"1983","journal-title":"Journal of Educational Measurement"},{"key":"2025091107264255100_ref027","doi-asserted-by":"publisher","first-page":"950","DOI":"10.1145\/3292500.333098","article-title":"KGAT: knowledge graph attention network for recommendation","volume-title":"Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","author":"Wang","year":"2019"},{"key":"2025091107264255100_ref028","doi-asserted-by":"publisher","first-page":"6153","DOI":"10.1609\/aaai.v34i04.6080","article-title":"Neural cognitive diagnosis for intelligent education systems","author":"Wang","year":"2020"},{"issue":"10","key":"2025091107264255100_ref029","doi-asserted-by":"publisher","first-page":"691","DOI":"10.1109\/TLT.2023.3326449","article-title":"Contrastive personalized exercise recommendation with reinforcement learning","volume":"17","author":"Wu","year":"2024","journal-title":"IEEE Transactions on Learning Technologies"},{"key":"2025091107264255100_ref030","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2024.129283","article-title":"EPCTS: Enhanced prompt-aware cross-prompt essay trait scoring","volume":"621","author":"Xu","year":"2025","journal-title":"Neurocomputing"},{"issue":"3","key":"2025091107264255100_ref031","doi-asserted-by":"publisher","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 Transactions on Emerging Topics in Computational Intelligence"},{"issue":"11","key":"2025091107264255100_ref032","doi-asserted-by":"publisher","first-page":"2558","DOI":"10.20009\/j.cnki.21-1106\/TP.2022-0231","article-title":"Research on exercise recommendation algorithm for online judge system enhanced by knowledge graph","volume":"44","author":"Ye","year":"2023","journal-title":"Journal of Chinese Computer Systems"},{"key":"2025091107264255100_ref033","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2022.118344","article-title":"KGAN: Knowledge grouping aggregation network for course recommendation in MOOCs","volume":"211","author":"Zhang","year":"2023","journal-title":"Expert Systems with Applications"},{"issue":"6","key":"2025091107264255100_ref034","doi-asserted-by":"publisher","first-page":"1620","DOI":"10.3778\/j.issn.1673-9418.2407092","article-title":"Research on exercise recommendation algorithm based on student knowledge state perception","volume":"19","author":"Zhou","year":"2025","journal-title":"Journal of Frontiers of Computer Science and Technology"},{"key":"2025091107264255100_ref035","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1109\/NaNA51271.2020.00080","article-title":"A study on exercise recommendation method using knowledge graph for computer network course","author":"Zhu","year":"2020"}],"container-title":["International Journal of Intelligent Computing and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.emerald.com\/ijicc\/article-pdf\/18\/3\/563\/10174364\/ijicc-04-2025-0190en.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/www.emerald.com\/ijicc\/article-pdf\/18\/3\/563\/10174364\/ijicc-04-2025-0190en.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,11]],"date-time":"2025-09-11T11:26:52Z","timestamp":1757590012000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.emerald.com\/ijicc\/article\/18\/3\/563\/1277282\/Personalized-exercise-recommendation-via-knowledge"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,5]]},"references-count":35,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2025,9,10]]}},"URL":"https:\/\/doi.org\/10.1108\/ijicc-04-2025-0190","relation":{},"ISSN":["1756-378X","1756-3798"],"issn-type":[{"value":"1756-378X","type":"print"},{"value":"1756-3798","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,5]]}}}