{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,11]],"date-time":"2026-02-11T15:52:31Z","timestamp":1770825151529,"version":"3.50.1"},"reference-count":45,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2021,5,29]],"date-time":"2021-05-29T00:00:00Z","timestamp":1622246400000},"content-version":"vor","delay-in-days":148,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"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":[[2021,1]]},"abstract":"<jats:p>While modelling students\u2019 learning behavior or preferences has been found as a crucial indicator for their course achievement, very few studies have considered it in predicting 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 model constitute a comprehensive students\u2019 learning behavioral pattern which is later used for prediction of their course achievement. Lastly, using a multiple criteria 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 a high accuracy through modelling their learning behavior during online courses.<\/jats:p>","DOI":"10.1155\/2021\/7463631","type":"journal-article","created":{"date-parts":[[2021,5,29]],"date-time":"2021-05-29T16:22:39Z","timestamp":1622305359000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["[Retracted] Predicting Course Grade through Comprehensive Modelling of Students\u2019 Learning Behavioral Pattern"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9143-6648","authenticated-orcid":false,"given":"Danial","family":"Hooshyar","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3219-7250","authenticated-orcid":false,"given":"Yeongwook","family":"Yang","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2021,5,29]]},"reference":[{"key":"e_1_2_10_1_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jad.2014.11.025"},{"key":"e_1_2_10_2_2","doi-asserted-by":"publisher","DOI":"10.1080\/01425692.2012.740807"},{"key":"e_1_2_10_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_10_4_2","doi-asserted-by":"crossref","unstructured":"KoriK. PedasteM. TonissonE.et al. First-year dropout in ICT studies Proceedings of the IEEE Global Engineering Education Conference (EDUCON) March 2015 Tallinn Estonia 437\u2013445 https:\/\/doi.org\/10.1109\/EDUCON.2015.7096008 2-s2.0-84946066257.","DOI":"10.1109\/EDUCON.2015.7096008"},{"key":"e_1_2_10_5_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.iheduc.2020.100725"},{"key":"e_1_2_10_6_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.compedu.2020.103878"},{"key":"e_1_2_10_7_2","doi-asserted-by":"publisher","DOI":"10.1002\/widm.1075"},{"key":"e_1_2_10_8_2","doi-asserted-by":"publisher","DOI":"10.1109\/te.2016.2528889"},{"key":"e_1_2_10_9_2","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2015.2496278"},{"key":"e_1_2_10_10_2","doi-asserted-by":"publisher","DOI":"10.1109\/access.2019.2897979"},{"key":"e_1_2_10_11_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00500-018-3064-6"},{"key":"e_1_2_10_12_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00500-020-04841-8"},{"key":"e_1_2_10_13_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2013.02.007"},{"key":"e_1_2_10_14_2","doi-asserted-by":"publisher","DOI":"10.1108\/AAOUJ-01-2017-0016"},{"key":"e_1_2_10_15_2","doi-asserted-by":"publisher","DOI":"10.3390\/app10062145"},{"key":"e_1_2_10_16_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00500-020-05110-4"},{"key":"e_1_2_10_17_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.compedu.2016.09.005"},{"key":"e_1_2_10_18_2","doi-asserted-by":"publisher","DOI":"10.3390\/e22010012"},{"key":"e_1_2_10_19_2","doi-asserted-by":"crossref","unstructured":"HellasA. Nam LiaoS. IhantolaP.et al. Predicting academic performance: a systematic literature review Proceedings of the Companion of the 23rd Annual ACM Conference on Innovation and Technology in Computer Science Education July 2018 Larnaca Cyprus 175\u2013199 https:\/\/doi.org\/10.1145\/3293881.3295783 2-s2.0-85061139135.","DOI":"10.1145\/3293881.3295783"},{"key":"e_1_2_10_20_2","doi-asserted-by":"publisher","DOI":"10.1109\/access.2017.2654247"},{"key":"e_1_2_10_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_10_22_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10489-012-0374-8"},{"key":"e_1_2_10_23_2","doi-asserted-by":"publisher","DOI":"10.1177\/0735633118757015"},{"key":"e_1_2_10_24_2","doi-asserted-by":"publisher","DOI":"10.1109\/TLT.2018.2856808"},{"key":"e_1_2_10_25_2","doi-asserted-by":"crossref","unstructured":"ParackS. ZahidZ. andMerchantF. Application of data mining in educational databases for predicting academic trends and patterns Proceedings of the 2012 IEEE International Conference on Technology Enhanced Education (ICTEE) January 2012 Amritapuri India IEEE 1\u20134 https:\/\/doi.org\/10.1109\/ICTEE.2012.6208617 2-s2.0-84862892745.","DOI":"10.1109\/ICTEE.2012.6208617"},{"key":"e_1_2_10_26_2","doi-asserted-by":"crossref","unstructured":"ChristianT. M.andAyubM. Exploration of classification using NBTree for predicting students\u2019 performance Proceedings of the 2014 International Conference on Data and Software Engineering (ICODSE) November 2014 Bandung Indonesia IEEE 1\u20136 https:\/\/doi.org\/10.1109\/ICODSE.2014.7062654 2-s2.0-84946685455.","DOI":"10.1109\/ICODSE.2014.7062654"},{"key":"e_1_2_10_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 Proceedings of the 33rd Annual Frontiers in Education 2003 FIE 2003 November 2003 Westminster CO USA IEEE https:\/\/doi.org\/10.1109\/FIE.2003.1263284 2-s2.0-84945969815.","DOI":"10.1109\/FIE.2003.1263284"},{"key":"e_1_2_10_28_2","doi-asserted-by":"crossref","unstructured":"LiK. F. RuskD. andSongF. Predicting student academic performance Proceedings of the 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_10_29_2","first-page":"9","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_10_30_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2014.04.024"},{"key":"e_1_2_10_31_2","doi-asserted-by":"crossref","unstructured":"GrayG. McGuinnessC. andOwendeP. An application of classification models to predict learner progression in tertiary education Proceedings of the 2014 IEEE International Advance Computing Conference (IACC) February 2014 Gurgaon India IEEE 549\u2013554 https:\/\/doi.org\/10.1109\/IAdCC.2014.6779384 2-s2.0-84899078811.","DOI":"10.1109\/IAdCC.2014.6779384"},{"key":"e_1_2_10_32_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.iheduc.2018.02.002"},{"key":"e_1_2_10_33_2","doi-asserted-by":"publisher","DOI":"10.1109\/mc.2016.119"},{"key":"e_1_2_10_34_2","doi-asserted-by":"publisher","DOI":"10.1109\/jstsp.2017.2692560"},{"key":"e_1_2_10_35_2","unstructured":"StrechtP. CruzL. SoaresC. andMendes-MoreiraJ. A comparative study of classification and regression algorithms for modelling students\u2019 academic performance Proceedings of the 8th International Conference on Educational Data Mining 2015 Madrid Spain."},{"key":"e_1_2_10_36_2","unstructured":"SweeneyM. RangwalaH. LesterJ. andJohriA. Next-term student performance prediction: a recommender systems approach 2016 http:\/\/arxiv.org\/abs\/1604.01840."},{"key":"e_1_2_10_37_2","doi-asserted-by":"publisher","DOI":"10.1142\/s0218213019400013"},{"key":"e_1_2_10_38_2","doi-asserted-by":"publisher","DOI":"10.1002\/9781119485001"},{"key":"e_1_2_10_39_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-48318-9_3"},{"key":"e_1_2_10_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 https:\/\/doi.org\/10.1145\/1076034.1076064 2-s2.0-84885608872.","DOI":"10.1145\/1076034.1076064"},{"key":"e_1_2_10_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 July 1993 Pittsburgh PA USA 329\u2013338 https:\/\/doi.org\/10.1145\/160688.160758.","DOI":"10.1145\/160688.160758"},{"key":"e_1_2_10_42_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01659-3_7"},{"key":"e_1_2_10_43_2","doi-asserted-by":"publisher","DOI":"10.1080\/01587919.2019.1632170"},{"key":"e_1_2_10_44_2","article-title":"Learning style classification based on student\u2032s 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_10_45_2","doi-asserted-by":"publisher","DOI":"10.1007\/s12652-020-02041-8"}],"updated-by":[{"DOI":"10.1155\/2024\/9896106","type":"retraction","label":"Retraction","source":"retraction-watch","updated":{"date-parts":[[2024,1,24]],"date-time":"2024-01-24T00:00:00Z","timestamp":1706054400000},"record-id":"52391"},{"DOI":"10.1155\/2024\/9896106","type":"retraction","label":"Retraction","source":"publisher","updated":{"date-parts":[[2024,1,24]],"date-time":"2024-01-24T00:00:00Z","timestamp":1706054400000}}],"container-title":["Complexity"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/downloads.hindawi.com\/journals\/complexity\/2021\/7463631.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/complexity\/2021\/7463631.xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1155\/2021\/7463631","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,9]],"date-time":"2024-08-09T22:20:45Z","timestamp":1723242045000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1155\/2021\/7463631"}},"subtitle":[],"editor":[{"given":"Zhihan","family":"Lv","sequence":"additional","affiliation":[]}],"short-title":[],"issued":{"date-parts":[[2021,1]]},"references-count":45,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2021,1]]}},"alternative-id":["10.1155\/2021\/7463631"],"URL":"https:\/\/doi.org\/10.1155\/2021\/7463631","archive":["Portico"],"relation":{"retraction":[{"id-type":"doi","id":"10.1155\/2024\/9896106","asserted-by":"object"}]},"ISSN":["1076-2787","1099-0526"],"issn-type":[{"value":"1076-2787","type":"print"},{"value":"1099-0526","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,1]]},"assertion":[{"value":"2021-04-08","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-05-14","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-05-29","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"7463631"}}