{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,9]],"date-time":"2026-03-09T04:50:28Z","timestamp":1773031828130,"version":"3.50.1"},"reference-count":45,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2025,2,7]],"date-time":"2025-02-07T00:00:00Z","timestamp":1738886400000},"content-version":"vor","delay-in-days":37,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100003621","name":"Ministry of Science, ICT and Future Planning","doi-asserted-by":"publisher","award":["2021R1C1C2004868"],"award-info":[{"award-number":["2021R1C1C2004868"]}],"id":[{"id":"10.13039\/501100003621","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100008530","name":"European Regional Development Fund","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100008530","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Complexity"],"published-print":{"date-parts":[[2025,1]]},"abstract":"<jats:p>While modeling students\u2019 learning behavior or preferences has been found to be a crucial indicator for their course achievement, very few studies have considered it in predicting the achievement of students in online courses. This study aims to model students\u2019 online learning behavior and accordingly predict their course achievement. First, feature vectors are developed using their aggregated action logs during a course. Second, some of these feature vectors are quantified into three numeric values that are used to model students\u2019 learning behavior, namely, accessing learning resources (content access), engaging with peers (engagement), and taking assessment tests (assessment). Both students\u2019 feature vectors and behavior models constitute a comprehensive student\u2019s learning behavioral pattern which is later used for the prediction of their course achievement. Lastly, using a multiple\u2010criteria decision\u2010making method (i.e., TOPSIS), the best classification methods were identified for courses with different sizes. Our findings revealed that the proposed generalizable approach could successfully predict students\u2019 achievement in courses with different numbers of students and features, showing the stability of the approach. Decision tree and AdaBoost classification methods appeared to outperform other existing methods on different datasets. Moreover, our results provide evidence that it is feasible to predict students\u2019 course achievement with high accuracy through modeling their learning behavior during online courses.<\/jats:p>","DOI":"10.1155\/cplx\/8851264","type":"journal-article","created":{"date-parts":[[2025,2,7]],"date-time":"2025-02-07T06:33:06Z","timestamp":1738909986000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Predicting Course Grades Through Comprehensive Modeling of Students\u2019 Learning Behavioral Patterns"],"prefix":"10.1155","volume":"2025","author":[{"given":"Danial","family":"Hooshyar","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3219-7250","authenticated-orcid":false,"given":"Yeongwook","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2025,2,7]]},"reference":[{"key":"e_1_2_12_1_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jad.2014.11.025"},{"key":"e_1_2_12_2_2","doi-asserted-by":"publisher","DOI":"10.1080\/01425692.2012.740807"},{"key":"e_1_2_12_3_2","volume-title":"E-Leadership: E-Skills for Competitiveness and Innovation Vision, Roadmap and Foresight Scenarios","author":"H\u00fcsing T.","year":"2013"},{"key":"e_1_2_12_4_2","doi-asserted-by":"crossref","unstructured":"KoriK. First-year Dropout in ICT Studies 2015 IEEE Global Engineering Education Conference (EDUCON) March 2015 Tallinn Estonia IEEE 437\u2013445.","DOI":"10.1109\/EDUCON.2015.7096008"},{"key":"e_1_2_12_5_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.iheduc.2020.100725"},{"key":"e_1_2_12_6_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.compedu.2020.103878"},{"key":"e_1_2_12_7_2","doi-asserted-by":"publisher","DOI":"10.1002\/widm.1075"},{"key":"e_1_2_12_8_2","doi-asserted-by":"publisher","DOI":"10.1109\/TE.2016.2528889"},{"key":"e_1_2_12_9_2","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2015.2496278"},{"key":"e_1_2_12_10_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2897979"},{"key":"e_1_2_12_11_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00500-018-3064-6"},{"key":"e_1_2_12_12_2","first-page":"1","article-title":"Proposed S-Algo+ Data Mining Algorithm for Web Platforms Course Content and Usage Evaluation","author":"Kazanidis I.","year":"2020","journal-title":"Soft Computing"},{"key":"e_1_2_12_13_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2013.02.007"},{"key":"e_1_2_12_14_2","doi-asserted-by":"publisher","DOI":"10.1108\/AAOUJ-01-2017-0016"},{"key":"e_1_2_12_15_2","article-title":"Implementing AutoML in Educational Data Mining for Prediction Tasks","volume":"10","author":"Tsiakmaki M.","year":"2020","journal-title":"Applied Sciences"},{"key":"e_1_2_12_16_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00500-020-05110-4"},{"key":"e_1_2_12_17_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.compedu.2016.09.005"},{"key":"e_1_2_12_18_2","article-title":"Mining Educational Data to Predict Students\u2019 Performance Through Procrastination Behavior","volume":"22","author":"Hooshyar D.","year":"2020","journal-title":"Entropy"},{"key":"e_1_2_12_19_2","doi-asserted-by":"crossref","unstructured":"HellasA. Predicting Academic Performance: A Systematic Literature Review Proceedings Companion of the 23rd Annual ACM Conference on Innovation and Technology in Computer Science Education July 2018 Larnaca Cyprus 175\u2013199.","DOI":"10.1145\/3293881.3295783"},{"key":"e_1_2_12_20_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2017.2654247"},{"key":"e_1_2_12_21_2","first-page":"57","article-title":"Classifiers for Educational Data Mining","author":"H\u00e4m\u00e4l\u00e4inen W.","year":"2010","journal-title":"Handbook of Educational Data Mining"},{"key":"e_1_2_12_22_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10489-012-0374-8"},{"key":"e_1_2_12_23_2","doi-asserted-by":"publisher","DOI":"10.1177\/0735633118757015"},{"key":"e_1_2_12_24_2","doi-asserted-by":"publisher","DOI":"10.1109\/TLT.2018.2856808"},{"key":"e_1_2_12_25_2","doi-asserted-by":"crossref","unstructured":"ParackS. ZahidZ. andMerchantF. Application of Data Mining in Educational Databases for Predicting Academic Trends and Patterns 2012 IEEE International Conference on Technology Enhanced Education (ICTEE) January 2012 Amritapuri India IEEE 1\u20134.","DOI":"10.1109\/ICTEE.2012.6208617"},{"key":"e_1_2_12_26_2","doi-asserted-by":"crossref","unstructured":"ChristianT. M.andAyubM. Exploration of Classification Using NBTree for Predicting Students\u2032 Performance 2014 International Conference on Data and Software Engineering (ICODSE) November 2014 Bandung Indonesia IEEE 1\u20136.","DOI":"10.1109\/ICODSE.2014.7062654"},{"key":"e_1_2_12_27_2","doi-asserted-by":"crossref","unstructured":"Minaei-BidgoliB. KashyD. A. KortemeyerG. andPunchW. F. Predicting Student Performance: an Application of Data Mining Methods With an Educational Web-Based System 1 33rd Annual Frontiers in Education November 2003 Westminster CO IEEE T2A\u2013T13.","DOI":"10.1109\/FIE.2003.1263284"},{"key":"e_1_2_12_28_2","doi-asserted-by":"crossref","unstructured":"LiK. F. RuskD. andSongF. Predicting Student Academic Performance 2013 Seventh International Conference on Complex Intelligent and Software Intensive Systems July 2013 Taichung Taiwan IEEE 27\u201333.","DOI":"10.1109\/CISIS.2013.15"},{"key":"e_1_2_12_29_2","first-page":"09","article-title":"Appraising the Significance of Self-Regulated Learning in Higher Education Using Neural Networks","volume":"1","author":"Kumar D.","year":"2012","journal-title":"International Journal of Engineering Research and Development"},{"key":"e_1_2_12_30_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2014.04.024"},{"key":"e_1_2_12_31_2","doi-asserted-by":"crossref","unstructured":"GrayG. McGuinnessC. andOwendeP. An Application of Classification Models to Predict Learner Progression in Tertiary Education 2014 IEEE International Advance Computing Conference (IACC) February 2014 New Delhi India IEEE 549\u2013554.","DOI":"10.1109\/IAdCC.2014.6779384"},{"key":"e_1_2_12_32_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.iheduc.2018.02.002"},{"key":"e_1_2_12_33_2","doi-asserted-by":"publisher","DOI":"10.1109\/MC.2016.119"},{"key":"e_1_2_12_34_2","doi-asserted-by":"publisher","DOI":"10.1109\/JSTSP.2017.2692560"},{"key":"e_1_2_12_35_2","article-title":"A Comparative Study of Classification and Regression Algorithms for Modelling Students\u2019 Academic Performance","author":"Strecht P.","year":"2015","journal-title":"International Educational Data Mining Society"},{"key":"e_1_2_12_36_2","article-title":"Next-Term Student Performance Prediction: A Recommender Systems Approach","author":"Sweeney M.","year":"2016","journal-title":"arXiv preprint arXiv:1604.01840"},{"key":"e_1_2_12_37_2","doi-asserted-by":"publisher","DOI":"10.1142\/S0218213019400013"},{"key":"e_1_2_12_38_2","doi-asserted-by":"publisher","DOI":"10.1002\/9781119485001"},{"key":"e_1_2_12_39_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-48318-9_3"},{"key":"e_1_2_12_40_2","doi-asserted-by":"crossref","unstructured":"SandersonM.andZobelJ. Information Retrieval System Evaluation: Effort Sensitivity and Reliability Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval August 2005 Salvador Brazil 162\u2013169.","DOI":"10.1145\/1076034.1076064"},{"key":"e_1_2_12_41_2","doi-asserted-by":"crossref","unstructured":"HullD. Using Statistical Testing in the Evaluation of Retrieval Experiments Proceedings of the 16th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval June 1993 Pittsburgh PA USA 329\u2013338.","DOI":"10.1145\/160688.160758"},{"key":"e_1_2_12_42_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01659-3_7"},{"key":"e_1_2_12_43_2","doi-asserted-by":"publisher","DOI":"10.1080\/01587919.2019.1632170"},{"key":"e_1_2_12_44_2","article-title":"Learning Style Classification Based on Student\u2019s Behavior in Moodle Learning Management System","volume":"3","author":"Abdullah M. A.","year":"2015","journal-title":"Transactions on Machine Learning and Artificial Intelligence"},{"key":"e_1_2_12_45_2","first-page":"1","article-title":"Prediction of Students\u2019 Procrastination Behaviour Through Their Submission Behavioural Pattern in Online Learning","author":"Yang Y.","year":"2020","journal-title":"Journal of Ambient Intelligence and Humanized Computing"}],"container-title":["Complexity"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1155\/cplx\/8851264","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/full-xml\/10.1155\/cplx\/8851264","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1155\/cplx\/8851264","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,9]],"date-time":"2026-03-09T03:50:35Z","timestamp":1773028235000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1155\/cplx\/8851264"}},"subtitle":[],"editor":[{"given":"Hiroki","family":"Sayama","sequence":"additional","affiliation":[],"role":[{"role":"editor","vocabulary":"crossref"}]}],"short-title":[],"issued":{"date-parts":[[2025,1]]},"references-count":45,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2025,1]]}},"alternative-id":["10.1155\/cplx\/8851264"],"URL":"https:\/\/doi.org\/10.1155\/cplx\/8851264","archive":["Portico"],"relation":{},"ISSN":["1076-2787","1099-0526"],"issn-type":[{"value":"1076-2787","type":"print"},{"value":"1099-0526","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,1]]},"assertion":[{"value":"2024-04-05","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-12-09","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-02-07","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"8851264"}}