{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,5]],"date-time":"2026-04-05T07:54:14Z","timestamp":1775375654091,"version":"3.50.1"},"reference-count":33,"publisher":"Springer Science and Business Media LLC","issue":"30","license":[{"start":{"date-parts":[[2024,2,15]],"date-time":"2024-02-15T00:00:00Z","timestamp":1707955200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,2,15]],"date-time":"2024-02-15T00:00:00Z","timestamp":1707955200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"DOI":"10.1007\/s11042-024-18426-2","type":"journal-article","created":{"date-parts":[[2024,2,15]],"date-time":"2024-02-15T08:02:47Z","timestamp":1707984167000},"page":"74349-74364","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":106,"title":["The accuracy of machine learning models relies on hyperparameter tuning: student result classification using random forest, randomized search, grid search, bayesian, genetic, and optuna algorithms"],"prefix":"10.1007","volume":"83","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1045-7728","authenticated-orcid":false,"given":"Yagyanath","family":"Rimal","sequence":"first","affiliation":[]},{"given":"Navneet","family":"Sharma","sequence":"additional","affiliation":[]},{"given":"Abeer","family":"Alsadoon","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,2,15]]},"reference":[{"issue":"3","key":"18426_CR1","doi-asserted-by":"publisher","first-page":"2009","DOI":"10.1007\/s00180-020-00999-9","volume":"36","author":"BG Marcot","year":"2021","unstructured":"Marcot BG, Hanea AM (2021) What is an optimal value of k in k-fold cross-validation in discrete Bayesian network analysis? Comput Stat 36(3):2009\u20132031. https:\/\/doi.org\/10.1007\/s00180-020-00999-9","journal-title":"Comput Stat"},{"key":"18426_CR2","doi-asserted-by":"publisher","unstructured":"Doyle T (2023) Helping students learn in a learner-centered environment: a guide to facilitating learning in higher education. Taylor & Francis,\u00a0New York. https:\/\/doi.org\/10.4324\/9781003445067","DOI":"10.4324\/9781003445067"},{"issue":"1","key":"18426_CR3","doi-asserted-by":"publisher","first-page":"299","DOI":"10.1007\/s11218-021-09612-3","volume":"24","author":"LM Daniels","year":"2021","unstructured":"Daniels LM, Goegan LD, Parker PC (2021) The impact of COVID-19 triggered changes to instruction and assessment on university students\u2019 self-reported motivation, engagement and perceptions. Soc Psychol Educ 24(1):299\u2013318. https:\/\/doi.org\/10.1007\/s11218-021-09612-3","journal-title":"Soc Psychol Educ"},{"issue":"3","key":"18426_CR4","doi-asserted-by":"publisher","first-page":"e3292","DOI":"10.1002\/rev3.3292","volume":"9","author":"R Morris","year":"2021","unstructured":"Morris R, Perry T, Wardle L (2021) Formative assessment and feedback for learning in higher education: A systematic review. Rev Educ 9(3):e3292. https:\/\/doi.org\/10.1002\/rev3.3292","journal-title":"Rev Educ"},{"key":"18426_CR5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2021\/6649524","volume":"2021","author":"GR El Said","year":"2021","unstructured":"El Said GR (2021) How did the COVID-19 pandemic affect higher education learning experience? An empirical investigation of learners\u2019 academic performance at a university in a developing country. Adv Hum-Comput Interact 2021:1\u201310","journal-title":"Adv Hum-Comput Interact"},{"issue":"4","key":"18426_CR6","doi-asserted-by":"publisher","first-page":"248","DOI":"10.1080\/08923647.2018.1509265","volume":"32","author":"AR Hurlbut","year":"2018","unstructured":"Hurlbut AR (2018) Online vs. traditional learning in teacher education: a comparison of student progress. Am J Distance Educ 32(4):248\u2013266. https:\/\/doi.org\/10.1080\/08923647.2018.1509265","journal-title":"Am J Distance Educ"},{"issue":"2","key":"18426_CR7","doi-asserted-by":"publisher","first-page":"86","DOI":"10.1152\/advan.00087.2009","volume":"34","author":"DJ Pepple","year":"2010","unstructured":"Pepple DJ, Young LE, Carroll RG (2010) A comparison of student performance in multiple-choice and long essay questions in the MBBS stage I physiology examination at the University of the West Indies (Mona Campus). Adv Physiol Educ 34(2):86\u201389. https:\/\/doi.org\/10.1152\/advan.00087.2009","journal-title":"Adv Physiol Educ"},{"key":"18426_CR8","doi-asserted-by":"publisher","first-page":"95608","DOI":"10.1109\/ACCESS.2021.3093563","volume":"9","author":"SDA Bujang","year":"2021","unstructured":"Bujang SDA et al (2021) Multiclass Prediction Model for Student Grade Prediction Using Machine Learning. IEEE Access 9:95608\u201395621. https:\/\/doi.org\/10.1109\/ACCESS.2021.3093563","journal-title":"IEEE Access"},{"issue":"10","key":"18426_CR9","doi-asserted-by":"publisher","first-page":"6199","DOI":"10.3390\/su14106199","volume":"14","author":"A Alhothali","year":"2022","unstructured":"Alhothali A, Albsisi M, Assalahi H, Aldosemani T (2022) Predicting student outcomes in online courses using machine learning techniques: A review. Sustainability 14(10):6199","journal-title":"Sustainability"},{"key":"18426_CR10","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2022\/4151487","volume":"2022","author":"YA Alsariera","year":"2022","unstructured":"Alsariera YA, Baashar Y, Alkawsi G, Mustafa A, Alkahtani AA, Ali N (2022) Assessment and evaluation of different machine learning algorithms for predicting student performance. Comput Intell Neurosci 2022:1\u201311","journal-title":"Comput Intell Neurosci"},{"key":"18426_CR11","doi-asserted-by":"publisher","first-page":"106903","DOI":"10.1016\/j.compeleceng.2020.106903","volume":"89","author":"H Zeineddine","year":"2021","unstructured":"Zeineddine H, Braendle U, Farah A (2021) Enhancing prediction of student success: Automated machine learning approach. Comput Electr Eng 89:106903","journal-title":"Comput Electr Eng"},{"issue":"3","key":"18426_CR12","doi-asserted-by":"publisher","first-page":"637","DOI":"10.1007\/s40745-021-00341-0","volume":"10","author":"S Hussain","year":"2023","unstructured":"Hussain S, Khan MQ (2023) Student-Performulator: Predicting Students\u2019 Academic Performance at Secondary and Intermediate Level Using Machine Learning. Ann Data Sci 10(3):637\u2013655. https:\/\/doi.org\/10.1007\/s40745-021-00341-0","journal-title":"Ann Data Sci"},{"key":"18426_CR13","doi-asserted-by":"crossref","unstructured":"Saifuzzaman M, Parvin M, Jahan I, Moon NN, Nur FN, Shetu SF (2021) \u201cMachine learning approach to predict SGPA and CGPA\u201d. in 2021 International Conference on Artificial Intelligence and Computer Science Technology (ICAICST), IEEE, 211\u2013216.","DOI":"10.1109\/ICAICST53116.2021.9497847"},{"issue":"4","key":"18426_CR14","doi-asserted-by":"publisher","first-page":"136","DOI":"10.3991\/ijet.v16i04.18643","volume":"16","author":"T Hamim","year":"2021","unstructured":"Hamim T, Benabbou F, Sael N (2021) Survey of machine learning techniques for student profile modeling. Int J Emerg Technol Learn IJET 16(4):136\u2013151","journal-title":"Int J Emerg Technol Learn IJET"},{"key":"18426_CR15","doi-asserted-by":"publisher","first-page":"100081","DOI":"10.1016\/j.caeai.2022.100081","volume":"3","author":"JP Bernius","year":"2022","unstructured":"Bernius JP, Krusche S, Bruegge B (2022) Machine learning based feedback on textual student answers in large courses. Comput Educ Artif Intell 3:100081","journal-title":"Comput Educ Artif Intell"},{"key":"18426_CR16","doi-asserted-by":"publisher","unstructured":"Hussain AA, Dimililer K (2021) \u201cStudent Grade Prediction Using Machine Learning in Iot Era,\u201d in Forthcoming Networks and Sustainability in the IoT Era, vol. 353, E. Ever and F. Al-Turjman, Eds., in Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol. 353. , Cham: Springer International Publishing, 65\u201381. https:\/\/doi.org\/10.1007\/978-3-030-69431-9_6.","DOI":"10.1007\/978-3-030-69431-9_6"},{"key":"18426_CR17","doi-asserted-by":"publisher","first-page":"835","DOI":"10.1016\/j.procs.2021.03.104","volume":"184","author":"A Tarik","year":"2021","unstructured":"Tarik A, Aissa H, Yousef F (2021) Artificial intelligence and machine learning to predict student performance during the COVID-19. Procedia Comput Sci 184:835\u2013840","journal-title":"Procedia Comput Sci"},{"issue":"4","key":"18426_CR18","doi-asserted-by":"publisher","first-page":"294","DOI":"10.26599\/BDMA.2021.9020030","volume":"5","author":"T Ahajjam","year":"2022","unstructured":"Ahajjam T, Moutaib M, Aissa H, Azrour M, Farhaoui Y, Fattah M (2022) Predicting students\u2019 final performance using artificial neural networks. Big Data Min Anal 5(4):294\u2013301","journal-title":"Big Data Min Anal"},{"issue":"4","key":"18426_CR19","doi-asserted-by":"publisher","first-page":"66","DOI":"10.3991\/ijet.v18i04.32583","volume":"18","author":"S Issaro","year":"2023","unstructured":"Issaro S, Wannapiroon P (2023) Intelligent Student Relationship Management Platform with Machine Learning for Student Empowerment. Int J Emerg Technol Learn Online 18(4):66","journal-title":"Int J Emerg Technol Learn Online"},{"key":"18426_CR20","doi-asserted-by":"crossref","unstructured":"Priya PM (2023) Prediction system for student\u2019s academic performance to increase university admission system and cumulative grade point average credits. Available: \nhttps:\/\/jst.org.in\/admin\/uploads\/JST070502.pdf.\u00a0Accessed 7 Oct 2023","DOI":"10.46243\/jst.2022.v7.i05.pp20-31"},{"key":"18426_CR21","doi-asserted-by":"crossref","unstructured":"Hoti AH, Zenuni X, Hamiti M, Ajdari J (2023) Student performance prediction using AI and ML: state of the art. In: 2023 12th Mediterranean Conference on Embedded Computing (MECO). IEEE, pp 1\u20136. Available: https:\/\/ieeexplore.ieee.org\/abstract\/document\/10154933\/. Accessed 7 Oct 2023","DOI":"10.1109\/MECO58584.2023.10154933"},{"key":"18426_CR22","unstructured":"Bujang\u00a0SDA et al (2022) Imbalanced classification methods for student grade prediction: a systematic literature review. IEEE Access. Available: \nhttps:\/\/ieeexplore.ieee.org\/abstract\/document\/9965398\/.\u00a0Accessed 7 Oct 2023"},{"issue":"3","key":"18426_CR23","doi-asserted-by":"publisher","first-page":"57","DOI":"10.5815\/ijmecs.2021.03.05","volume":"13","author":"JG Perez","year":"2021","unstructured":"Perez JG, Perez ES (2021) Predicting student program completion using Na\u00efve Bayes classification algorithm. Int J Mod Educ Comput Sci 13(3):57\u201367","journal-title":"Int J Mod Educ Comput Sci"},{"key":"18426_CR24","doi-asserted-by":"crossref","unstructured":"Haque A (2021) EC-GAN: Low-sample classification using semi-supervised algorithms and GANs (Student Abstract). In: Proceedings of the AAAI conference on artificial intelligence. AAAI, pp 15797\u201315798. Available: https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/view\/17895. Accessed 7 Oct 2023","DOI":"10.1609\/aaai.v35i18.17895"},{"issue":"6","key":"18426_CR25","doi-asserted-by":"publisher","first-page":"3360","DOI":"10.1080\/10494820.2021.1928235","volume":"31","author":"A Asselman","year":"2023","unstructured":"Asselman A, Khaldi M, Aammou S (2023) Enhancing the prediction of student performance based on the machine learning XGBoost algorithm. Interact Learn Environ 31(6):3360\u20133379. https:\/\/doi.org\/10.1080\/10494820.2021.1928235","journal-title":"Interact Learn Environ"},{"issue":"1","key":"18426_CR26","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1186\/s40561-022-00192-z","volume":"9","author":"M Ya\u011fc\u0131","year":"2022","unstructured":"Ya\u011fc\u0131 M (2022) Educational data mining: prediction of students\u2019 academic performance using machine learning algorithms. Smart Learn Environ 9(1):11. https:\/\/doi.org\/10.1186\/s40561-022-00192-z","journal-title":"Smart Learn Environ"},{"issue":"6","key":"18426_CR27","first-page":"48","volume":"41","author":"S-SM Ajibade","year":"2022","unstructured":"Ajibade S-SM, Dayupay J, Ngo-Hoang D-L, Oyebode OJ, Sasan JM (2022) Utilization of Ensemble Techniques for Prediction of the Academic Performance of Students. J Optoelectron Laser 41(6):48\u201354","journal-title":"J Optoelectron Laser"},{"key":"18426_CR28","doi-asserted-by":"crossref","unstructured":"Dianah S, Selamat A, Krejcar O (2022) Improve imbalanced multiclass classification based on modified SMOTE and feature selection for student grade prediction. In: Handbook of Research on New Investigations in Artificial Life, AI, and Machine Learning. IGI Global, pp 371\u2013389. Available: https:\/\/www.igi-global.com\/chapter\/improve-imbalanced-multiclass-classification-based-on-modified-smote-and-feature-selection-for-student-grade-prediction\/296811. Accessed 7 Oct 2023","DOI":"10.4018\/978-1-7998-8686-0.ch014"},{"issue":"4","key":"18426_CR29","doi-asserted-by":"publisher","first-page":"290","DOI":"10.1080\/08839514.2021.1877481","volume":"35","author":"Y Sun","year":"2021","unstructured":"Sun Y, Li Z, Li X, Zhang J (2021) Classifier Selection and Ensemble Model for Multi-class Imbalance Learning in Education Grants Prediction. Appl Artif Intell 35(4):290\u2013303. https:\/\/doi.org\/10.1080\/08839514.2021.1877481","journal-title":"Appl Artif Intell"},{"key":"18426_CR30","doi-asserted-by":"crossref","unstructured":"Jayasundara S, Indika A, Herath D (2022) Interpretable student performance prediction using explainable boosting machine for multi-class classification. In: 2022 2nd International Conference on Advanced Research in Computing (ICARC). IEEE, pp 391\u2013396. Available: https:\/\/ieeexplore.ieee.org\/abstract\/document\/9753867\/. Accessed 7 Oct 2023","DOI":"10.1109\/ICARC54489.2022.9753867"},{"issue":"3","key":"18426_CR31","doi-asserted-by":"publisher","first-page":"e1301","DOI":"10.1002\/widm.1301","volume":"9","author":"P Probst","year":"2019","unstructured":"Probst P, Wright MN, Boulesteix A (2019) Hyperparameters and tuning strategies for random forest. WIREs Data Min Knowl Discov 9(3):e1301. https:\/\/doi.org\/10.1002\/widm.1301","journal-title":"WIREs Data Min Knowl Discov"},{"key":"18426_CR32","doi-asserted-by":"publisher","first-page":"107329","DOI":"10.1016\/j.compbiolchem.2020.107329","volume":"88","author":"M Rahman","year":"2020","unstructured":"Rahman M, Islam D, Mukti RJ, Saha I (2020) A deep learning approach based on convolutional LSTM for detecting diabetes. Comput Biol Chem 88:107329","journal-title":"Comput Biol Chem"},{"key":"18426_CR33","doi-asserted-by":"crossref","unstructured":"Ansarullah SI, Mohsin Saif S, Abdul Basit Andrabi S, Kumhar SH, Kirmani MM, Kumar DP (2022) An intelligent and reliable hyperparameter optimization machine learning model for early heart disease assessment using imperative risk attributes. J Healthc Eng 2022. Available: https:\/\/www.hindawi.com\/journals\/jhe\/2022\/9882288\/. Accessed 15 Oct 2023","DOI":"10.1155\/2022\/9882288"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-024-18426-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-024-18426-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-024-18426-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,3]],"date-time":"2024-09-03T02:19:39Z","timestamp":1725329979000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-024-18426-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,2,15]]},"references-count":33,"journal-issue":{"issue":"30","published-online":{"date-parts":[[2024,9]]}},"alternative-id":["18426"],"URL":"https:\/\/doi.org\/10.1007\/s11042-024-18426-2","relation":{},"ISSN":["1573-7721"],"issn-type":[{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,2,15]]},"assertion":[{"value":"23 October 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 December 2023","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 January 2024","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 February 2024","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no conflicts of interest in publishing this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflicts of interest"}}]}}