{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,5]],"date-time":"2026-06-05T12:27:20Z","timestamp":1780662440986,"version":"3.54.1"},"reference-count":28,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2019,12,20]],"date-time":"2019-12-20T00:00:00Z","timestamp":1576800000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2019,12,20]],"date-time":"2019-12-20T00:00:00Z","timestamp":1576800000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Educ Inf Technol"],"published-print":{"date-parts":[[2020,7]]},"DOI":"10.1007\/s10639-019-10049-7","type":"journal-article","created":{"date-parts":[[2019,12,20]],"date-time":"2019-12-20T21:02:38Z","timestamp":1576875758000},"page":"2733-2746","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":42,"title":["Adaptive recommendation system using machine learning algorithms for predicting student\u2019s best academic program"],"prefix":"10.1007","volume":"25","author":[{"given":"Mohamed","family":"Ezz","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1309-6449","authenticated-orcid":false,"given":"Ayman","family":"Elshenawy","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2019,12,20]]},"reference":[{"issue":"1","key":"10049_CR1","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1108\/JARHE-09-2017-0113","volume":"10","author":"OW Adejo","year":"2018","unstructured":"Adejo, O. W. (2018). Predicting student academic performance using multi-model heterogeneous ensemble approach. Journal of Applied Research in Higher Education, 10(1), 61\u201375.","journal-title":"Journal of Applied Research in Higher Education"},{"issue":"7","key":"10049_CR2","doi-asserted-by":"publisher","first-page":"528","DOI":"10.7763\/IJIET.2016.V6.745","volume":"6","author":"MA Al-Barrak","year":"2016","unstructured":"Al-Barrak, M. A., & Al-Razgan, M. (2016). Predicting Students final GPA using decision trees: A case study. International Journal of Information and Education Technology, 6(7), 528\u2013533.","journal-title":"International Journal of Information and Education Technology"},{"key":"10049_CR3","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1016\/j.procs.2016.04.010","volume":"82","author":"Y Altujjar","year":"2016","unstructured":"Altujjar, Y., Altamimi, W., & Al-Turaiki, I. (2016). Predicting critical courses affecting students performance: A case study. Procedia Computer Science, 82, 65\u201371.","journal-title":"Procedia Computer Science"},{"key":"10049_CR4","doi-asserted-by":"publisher","first-page":"177e194","DOI":"10.1016\/j.compedu.2017.05.007","volume":"113","author":"R Asif","year":"2017","unstructured":"Asif, R., Merceron, A., Abbas Ali, S., & Ghani Haider, N. (2017). Analyzing undergraduate students\u2019 performance using educational data mining. Computers & Education, 113, 177e194.","journal-title":"Computers & Education"},{"key":"10049_CR5","doi-asserted-by":"publisher","first-page":"2551","DOI":"10.3233\/IFS-141229","volume":"27","author":"J-F Chena","year":"2014","unstructured":"Chena, J.-F., & Hung Doa, Q. (2014). A cooperative Cuckoo Search \u2013 Hierarchical adaptive neuro-fuzzy inference system approach for predicting student academic performance. Journal of Intelligent & Fuzzy Systems, 27, 2551\u20132561.","journal-title":"Journal of Intelligent & Fuzzy Systems"},{"issue":"2","key":"10049_CR6","first-page":"34","volume":"4","author":"MM Ezz","year":"2015","unstructured":"Ezz, M. M. (2015). Advisory system for student enrollment in university based on variety of machine learning algorithms. International Journal of Computing Academic Research (IJCAR), 4(2), 34\u201345.","journal-title":"International Journal of Computing Academic Research (IJCAR)"},{"issue":"1","key":"10049_CR7","first-page":"1","volume":"1","author":"E Fernandes","year":"2018","unstructured":"Fernandes, E., Holanda, M., Victorino, M., Borges, V., Carvalho, R., & Van Erven, G. (2018). Educational data mining: Predictive analysis of academic performance of public school students in the capital of Brazil. Journal of Business Research, 1(1), 1\u20139.","journal-title":"Journal of Business Research"},{"key":"10049_CR8","doi-asserted-by":"publisher","first-page":"326","DOI":"10.1016\/j.protcy.2016.08.114","volume":"25","author":"H Hamsa","year":"2016","unstructured":"Hamsa, H., Indiradevi, S., & Kizhak, J. J. (2016). Student academic performance prediction model using decision tree and fuzzy genetic algorithm. Procedia Technology, 25, 326\u2013332.","journal-title":"Procedia Technology"},{"key":"10049_CR9","doi-asserted-by":"publisher","first-page":"134","DOI":"10.1016\/j.knosys.2018.07.042","volume":"161","author":"S Helal","year":"2018","unstructured":"Helal, S., Li, J., Liu, L., Ebrahimiea, E., Dawsonb, S., & Murrayc, D. J. (2018). Predicting academic performance by considering student heterogeneity. Knowledge-Based Systems, 161, 134\u2013146.","journal-title":"Knowledge-Based Systems"},{"key":"10049_CR10","doi-asserted-by":"publisher","first-page":"497","DOI":"10.1007\/s13042-015-0341-x","volume":"8","author":"N Iam-On","year":"2017","unstructured":"Iam-On, N., & Boongoen, T. (2017). Improved student dropout prediction in Thai University using ensemble of mixed-type data clusterings. International Journal of Machine Learning and Cybernetics, 8, 497\u2013510.","journal-title":"International Journal of Machine Learning and Cybernetics"},{"key":"10049_CR11","doi-asserted-by":"publisher","first-page":"320","DOI":"10.1016\/j.sbspro.2013.10.240","volume":"97","author":"S Khadijah Mohamada","year":"2013","unstructured":"Khadijah Mohamada, S., & Tasira, Z. (2013). Educational data mining: A review. Procedia \u2013 Social and Behavioral Sciences, 97, 320\u2013324.","journal-title":"Procedia \u2013 Social and Behavioral Sciences"},{"key":"10049_CR12","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1088\/1757-899X\/215\/1\/012036","volume":"215","author":"AU Khasanah","year":"2017","unstructured":"Khasanah, A. U., & Harwati. (2017). A comparative study to predict student\u2019s performance using educational data mining techniques. IOP Conference Series: Materials Science and Engineering, 215, 1\u20137.","journal-title":"IOP Conference Series: Materials Science and Engineering"},{"issue":"5","key":"10049_CR13","first-page":"605","volume":"17","author":"S K\u0131l\u0131\u00e7 Depren","year":"2017","unstructured":"K\u0131l\u0131\u00e7 Depren, S., Esra A\u015fk\u0131n, \u00d6., & \u00d6z, E. (2017). Identifying the classification performances of educational data mining methods: A case study for TIMSS. Educational Sciences: Theory & Practice, 17(5), 605\u20131623.","journal-title":"Educational Sciences: Theory & Practice"},{"key":"10049_CR14","doi-asserted-by":"publisher","first-page":"529","DOI":"10.1016\/j.knosys.2010.03.010","volume":"23","author":"S Kotsiantis","year":"2010","unstructured":"Kotsiantis, S., Patriarcheas, K., & Xenos, M. (2010). A combinational incremental ensemble of classifiers as a technique for predicting students performance in distance education. Knowledge-Based Systems, 23, 529\u2013535.","journal-title":"Knowledge-Based Systems"},{"key":"10049_CR15","doi-asserted-by":"publisher","first-page":"36","DOI":"10.1016\/j.dss.2018.09.001","volume":"115","author":"V Migueis","year":"2018","unstructured":"Migueis, V., Freitas, A., Garciab, P. J., & Silva, A. (2018). Early segmentation of students according to their academic performance: A predictive modelling approach. Decision Support Systems, 115, 36\u201351.","journal-title":"Decision Support Systems"},{"key":"10049_CR16","first-page":"292","volume":"1","author":"G Mobasher","year":"2017","unstructured":"Mobasher, G., Shawish, A., & Ibrahim, O. (2017). Educational data mining rule based recommender systems. Educational Data Mining Rule based Recommender Systems, 1, 292\u2013299.","journal-title":"Educational Data Mining Rule based Recommender Systems"},{"key":"10049_CR17","doi-asserted-by":"publisher","first-page":"137","DOI":"10.1016\/j.procs.2016.09.380","volume":"102","author":"A Mohamed Ahmeda","year":"2016","unstructured":"Mohamed Ahmeda, A., Rizanerc, A., & Ulusoy, A. H. (2016). Using data mining to predict instructor performance. Procedia Computer Science, 102, 137\u2013142.","journal-title":"Procedia Computer Science"},{"key":"10049_CR18","doi-asserted-by":"publisher","first-page":"36","DOI":"10.5815\/ijmecs.2016.11.05","volume":"11","author":"A Mueen","year":"2016","unstructured":"Mueen, A., Zafar, B., & Manzoor, U. (2016). Modeling and predicting students\u2019 academic performance using data mining techniques. International Journal of Modern Education and Computer Science, 11, 36\u201342.","journal-title":"International Journal of Modern Education and Computer Science"},{"issue":"2","key":"10049_CR19","doi-asserted-by":"publisher","first-page":"221","DOI":"10.14257\/ijhit.2015.8.2.20","volume":"8","author":"SA Naser","year":"2015","unstructured":"Naser, S. A., Zaqout, I., Atallah, R., Alajrami, E., & Abu Ghosh, M. (2015). Predicting student performance using artificial neural network: In the faculty of engineering and information technology. International Journal of Hybrid Information Technology, 8(2), 221\u2013228.","journal-title":"International Journal of Hybrid Information Technology"},{"key":"10049_CR20","doi-asserted-by":"crossref","unstructured":"M. Pandey and. S. Taruna, \u201cTowards the integration of multiple classifier pertaining to the Student\u2019s performance prediction,\u201d Perspectives in Science, vol. 8, pp. 364\u2013366, 2016.","DOI":"10.1016\/j.pisc.2016.04.076"},{"key":"10049_CR21","doi-asserted-by":"publisher","first-page":"1701","DOI":"10.1016\/j.tele.2018.04.015","volume":"35","author":"MW Rodrigues","year":"2018","unstructured":"Rodrigues, M. W., Isotanib, S., & Z\u00e1ratea, L. E. (2018). Educational data mining: A review of evaluation process in the e-learning. Telematics and Informatics, 35, 1701\u20131717.","journal-title":"Telematics and Informatics"},{"issue":"6","key":"10049_CR22","doi-asserted-by":"publisher","first-page":"601","DOI":"10.1109\/TSMCC.2010.2053532","volume":"40","author":"C Romero","year":"2010","unstructured":"Romero, C., & Ventura, S. (2010). Educational data mining: A review of the state of the art. IEEE Transactions on Systems, Man, and Cybernetics Part C: Applications and Reviews, 40(6), 601\u2013618.","journal-title":"IEEE Transactions on Systems, Man, and Cybernetics Part C: Applications and Reviews"},{"key":"10049_CR23","doi-asserted-by":"publisher","first-page":"458","DOI":"10.1016\/j.compedu.2013.06.009","volume":"68","author":"C Romero","year":"2013","unstructured":"Romero, C., L\u00f3pez, M.-I., Luna, J.-M., & Ventura, S. (2013). Predicting students\u2019 final performance from participation in on-line discussion forums. Computers & Education, 68, 458\u2013472.","journal-title":"Computers & Education"},{"key":"10049_CR24","doi-asserted-by":"publisher","first-page":"2811","DOI":"10.1016\/j.procs.2010.08.006","volume":"1","author":"N Thai-Nghe","year":"2010","unstructured":"Thai-Nghe, N., Drumond, L., Krohn-Grimberghe, A., & Schmidt-Thieme, L. (2010). Recommender system for predicting student performance. Procedia Computer Science, 1, 2811\u20132819.","journal-title":"Procedia Computer Science"},{"issue":"2","key":"10049_CR25","doi-asserted-by":"publisher","first-page":"164","DOI":"10.1515\/cait-2017-0024","volume":"17","author":"T-O Tran","year":"2017","unstructured":"Tran, T.-O., Dang, H.-T., Dinh, V.-T., Truong, T.-M.-N., Vuong, T.-P.-T., & Phan, X.-H. (2017). Performance prediction for students: A multi-strategy approach. Cybernetics and Information Technologies, 17(2), 164\u2013182.","journal-title":"Cybernetics and Information Technologies"},{"issue":"117","key":"10049_CR26","first-page":"715","volume":"392","author":"G-J Wang","year":"2013","unstructured":"Wang, G.-J., Chi, X., Shou, C., Yang, J.-J., & Yang, M.-Y. (2013). Random matrix theory analysis of cross-correlations in the US stock market: Evidence from Pearson\u2019s correlation coefficient and detrended cross-correlation coefficient. Physica A: Statistical Mechanics and its Applications, 392(117), 715\u20133730.","journal-title":"Physica A: Statistical Mechanics and its Applications"},{"key":"10049_CR27","doi-asserted-by":"publisher","first-page":"302","DOI":"10.1016\/j.procs.2018.08.178","volume":"135","author":"F Widyahastutia","year":"2018","unstructured":"Widyahastutia, F., & Utami Tjhinb, V. (2018). Performance prediction in online discussion forum: State-of-the-art and comparative analysis. Procedia Computer Science, 135, 302\u2013314.","journal-title":"Procedia Computer Science"},{"key":"10049_CR28","doi-asserted-by":"publisher","first-page":"1659","DOI":"10.1007\/s12652-018-0864-6","volume":"9","author":"X Zhang","year":"2018","unstructured":"Zhang, X., Sun, G., Pan, Y., Sun, H., & He, Y. (2018). Students performance modelling based on behavior pattern. Journal of Ambient Intelligence and Humanized Computing, 9, 1659\u20131670.","journal-title":"Journal of Ambient Intelligence and Humanized Computing"}],"container-title":["Education and Information Technologies"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10639-019-10049-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s10639-019-10049-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10639-019-10049-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,12,19]],"date-time":"2020-12-19T00:45:16Z","timestamp":1608338716000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s10639-019-10049-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,12,20]]},"references-count":28,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2020,7]]}},"alternative-id":["10049"],"URL":"https:\/\/doi.org\/10.1007\/s10639-019-10049-7","relation":{},"ISSN":["1360-2357","1573-7608"],"issn-type":[{"value":"1360-2357","type":"print"},{"value":"1573-7608","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,12,20]]},"assertion":[{"value":"9 July 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 October 2019","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 December 2019","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}