{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,20]],"date-time":"2026-04-20T10:35:21Z","timestamp":1776681321266,"version":"3.51.2"},"reference-count":29,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2024,8,2]],"date-time":"2024-08-02T00:00:00Z","timestamp":1722556800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,8,2]],"date-time":"2024-08-02T00:00:00Z","timestamp":1722556800000},"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":["SN COMPUT. SCI."],"DOI":"10.1007\/s42979-024-03118-3","type":"journal-article","created":{"date-parts":[[2024,8,2]],"date-time":"2024-08-02T13:30:46Z","timestamp":1722605446000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Enhancing Student Academic Performance Forecasting: A Comparative Analysis of Machine Learning Algorithms"],"prefix":"10.1007","volume":"5","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4217-0182","authenticated-orcid":false,"given":"Ishaan","family":"Dawar","sequence":"first","affiliation":[]},{"given":"Sakshi","family":"Negi","sequence":"additional","affiliation":[]},{"given":"Sumita","family":"Lamba","sequence":"additional","affiliation":[]},{"given":"Ashok","family":"Kumar","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,8,2]]},"reference":[{"key":"3118_CR1","doi-asserted-by":"publisher","unstructured":"Gamboa RA, Namasivayam S, Singh R. (2018) Correlation study between CGPA and PO attainments: a case study for taylor\u2019s university school of engineering. Redesig Learn Greater Soc Impact. https:\/\/doi.org\/10.1007\/978-981-10-4223-2_1","DOI":"10.1007\/978-981-10-4223-2_1"},{"key":"3118_CR2","doi-asserted-by":"publisher","unstructured":"Ji L, Zhang X, Zhang L, Research on the Algorithm of Education Data Mining Based on Big Data., in: 2020 IEEE 2nd International Conference on Computer Science and Educational Informatization (CSEI), 2020, pp. 344\u2013350, https:\/\/doi.org\/10.1109\/CSEI50228.2020.9142529","DOI":"10.1109\/CSEI50228.2020.9142529"},{"key":"3118_CR3","doi-asserted-by":"publisher","unstructured":"Waheed H, Hassan SU, Aljohani NR, Hardman J, Alelyani S, Nawaz R. (2020). Predicting academic performance of students from VLE big data using deep learning models. Computers in Human Behavior, 104(October 2019), 106189. https:\/\/doi.org\/10.1016\/j.chb.2019.106189","DOI":"10.1016\/j.chb.2019.106189"},{"issue":"1","key":"3118_CR4","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1080\/10494820.2013.817441","volume":"24","author":"O Casquero","year":"2016","unstructured":"Casquero O, Ovelar R, Romo J, Benito M, Alberdi M. Students\u2019 personal networks in virtual and personal learning environments: a case study in higher education using learning analytics approach. Interact Learn Environ. 2016;24(1):49\u201367. https:\/\/doi.org\/10.1080\/10494820.2013.817441.","journal-title":"Interact Learn Environ"},{"issue":"February 2017","key":"3118_CR5","doi-asserted-by":"publisher","first-page":"578","DOI":"10.1016\/j.chb.2018.07.002","volume":"92","author":"M Shorfuzzaman","year":"2019","unstructured":"Shorfuzzaman M, Hossain MS, Nazir A, Muhammad G, Alamri A. Harnessing the power of big data analytics in the cloud to support learning analytics in mobile learning environment. Comput Hum Behav. 2019;92(February 2017):578\u201388. https:\/\/doi.org\/10.1016\/j.chb.2018.07.002.","journal-title":"Comput Hum Behav"},{"issue":"1","key":"3118_CR6","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. Educational data mining: prediction of students\u2019 academic performance using machine learning algorithms. Smart Learn Environ. 2022;9(1):11. https:\/\/doi.org\/10.1186\/s40561-022-00192-z.","journal-title":"Smart Learn Environ"},{"key":"3118_CR7","doi-asserted-by":"publisher","unstructured":"Onyema EM, Almuzaini KK, Onu FU, Verma D, Gregory US, Puttaramaiah M, Afriyie RK. (2022). Prospects and Challenges of Using Machine Learning for Academic Forecasting. In Z. Uddin, editor, Computational Intelligence and Neuroscience (Vol. 2022, pp. 1\u20137). Hindawi Limited. https:\/\/doi.org\/10.1155\/2022\/5624475","DOI":"10.1155\/2022\/5624475"},{"key":"3118_CR8","doi-asserted-by":"publisher","unstructured":"Pushpa SK, Manjunath TN, Mrunal TV, Singh A, Suhas C. (2017). Class result prediction usingmachine learning. In Proceedings of the 2017 International Conference On Smart Technologies For Smart Nation (SmartTechCon) (pp. 1208\u20131212). IEEE. https:\/\/doi.org\/10.1109\/SmartTechCon.2017.8358559","DOI":"10.1109\/SmartTechCon.2017.8358559"},{"key":"3118_CR9","doi-asserted-by":"publisher","unstructured":"Rifat MRI, Imran A, A., Badrudduza ASM. (2019, May). Edunet: A deep neural network approach for predicting CGPA of undergraduate students. In Proceedings of the 2019 1st International Conference on Advances in Science, Engineering and Robotics Technology (ICASERT) (pp. 1\u20136). IEEE. https:\/\/doi.org\/10.1109\/ICASERT.2019.8934616","DOI":"10.1109\/ICASERT.2019.8934616"},{"key":"3118_CR10","unstructured":"Yulianto LD, Triayudi A, Sholihati ID. Method and Decision Tree C4. 5. Jurnal Mantik. 2020;4(1):441\u201351. Implementation Educational Data Mining For Analysis of Student Performance Prediction with Comparison of K-Nearest Neighbor Data Mining."},{"key":"3118_CR11","doi-asserted-by":"publisher","first-page":"611","DOI":"10.1007\/978-981-15-5788-0_58\/COVER","volume":"1176","author":"S Rai","year":"2021","unstructured":"Rai S, Shastry KA, Pratap S, Kishore S, Mishra P, Sanjay HA. Machine learning approach for student academic performance prediction. Adv Intell Syst Comput. 2021;1176:611\u20138. https:\/\/doi.org\/10.1007\/978-981-15-5788-0_58\/COVER.","journal-title":"Adv Intell Syst Comput"},{"key":"3118_CR12","doi-asserted-by":"publisher","unstructured":"Shetu SF, Saifuzzaman M, Moon NN, Sultana S, Yousuf R. (2021). Student\u2019s performance prediction using data mining technique depending on overall academic status and environmental attributes. In Proceedings of the International Conference on Innovative Computing and Communications (pp. 757\u2013769). Springer, Singapore. https:\/\/doi.org\/10.1007\/978-981-15-5148-2_66","DOI":"10.1007\/978-981-15-5148-2_66"},{"key":"3118_CR13","doi-asserted-by":"publisher","first-page":"75","DOI":"10.1007\/978-981-15-5113-0_6\/COVER","volume":"1165","author":"D Sharma","year":"2021","unstructured":"Sharma D, Aggarwal D. A predictive approach to academic performance analysis of students based on parental infuence. Adv Intell Syst Comput. 2021;1165:75\u201384. https:\/\/doi.org\/10.1007\/978-981-15-5113-0_6\/COVER.","journal-title":"Adv Intell Syst Comput"},{"key":"3118_CR14","doi-asserted-by":"publisher","first-page":"347","DOI":"10.1007\/978-981-15-5113-0_25\/COVER","volume":"1165","author":"J Gajwani","year":"2021","unstructured":"Gajwani J, Chakraborty P. Students\u2019 performance prediction using feature selection and supervised machine learning algorithms. Adv Intell Syst Comput. 2021;1165:347\u201354. https:\/\/doi.org\/10.1007\/978-981-15-5113-0_25\/COVER.","journal-title":"Adv Intell Syst Comput"},{"key":"3118_CR15","doi-asserted-by":"publisher","first-page":"9867","DOI":"10.1016\/j.aej.2022.03.021","volume":"61","author":"Y Baashar","year":"2022","unstructured":"Baashar Y, Hamed Y, Alkawsi G, Fernando Capretz L, Alhussian H, Alwadain A, Al-amri R. Evaluation of postgraduate academic performance using artifcial intelligence models. Alex Eng J. 2022;61:9867\u201378. https:\/\/doi.org\/10.1016\/j.aej.2022.03.021.","journal-title":"Alex Eng J"},{"key":"3118_CR16","doi-asserted-by":"publisher","first-page":"916","DOI":"10.1108\/K-12-2020-0865","volume":"51","author":"MN Yakubu","year":"2022","unstructured":"Yakubu MN, Abubakar AM. Applying machine learning approach to predict students\u2019 performance in higher educational institutions. Kybernetes. 2022;51:916\u201334. https:\/\/doi.org\/10.1108\/K-12-2020-0865.","journal-title":"Kybernetes"},{"key":"3118_CR17","doi-asserted-by":"publisher","unstructured":"Bhushan M, Verma U, Garg C, Negi A. (2023). Machine Learning-Based Academic Result Prediction System. In International Journal of Software Innovation (Vol. 12, Issue 1, pp. 1\u201314). IGI Global. https:\/\/doi.org\/10.4018\/ijsi.334715","DOI":"10.4018\/ijsi.334715"},{"key":"3118_CR18","doi-asserted-by":"publisher","unstructured":"Pallathadka H, Wenda A, Ramirez-As\u00eds E, As\u00eds-L\u00f3pez M, Flores-Albornoz J, Phasinam K. (2023). Classification and prediction of student performance data using various machine learning algorithms. In Materials Today: Proceedings (Vol. 80, pp. 3782\u20133785). Elsevier BV. https:\/\/doi.org\/10.1016\/j.matpr.2021.07.382","DOI":"10.1016\/j.matpr.2021.07.382"},{"issue":"1","key":"3118_CR19","doi-asserted-by":"publisher","first-page":"46","DOI":"10.5815\/ijmecs.2023.01.04","volume":"15","author":"N Sharma","year":"2023","unstructured":"Sharma N, Appukutti S, Garg U, Mukherjee J, Mishra S. Analysis of student\u2019s academic performance based on their time spent on extra-curricular activities using machine learning techniques. Int J Mod Educ Comput Sci. 2023;15(1):46\u201357. https:\/\/doi.org\/10.5815\/ijmecs.2023.01.04.","journal-title":"Int J Mod Educ Comput Sci"},{"key":"3118_CR20","doi-asserted-by":"publisher","first-page":"9655","DOI":"10.1007\/s10639-022-11573-9","volume":"28","author":"A Kukkar","year":"2023","unstructured":"Kukkar A, Mohana R, Sharma A, et al. Prediction of student academic performance based on their emotional wellbeing and interaction on various e-learning platforms. Educ Inf Technol. 2023;28:9655\u201384. https:\/\/doi.org\/10.1007\/s10639-022-11573-9.","journal-title":"Educ Inf Technol"},{"key":"3118_CR21","doi-asserted-by":"publisher","unstructured":"Chavez H, Chavez-Arias B, Contreras-Rosas S, Alvarez-Rodr\u00edguez JM, Raymundo C. (2023). Artificial neural network model to predict student performance using nonpersonal information. In Frontiers in Education (Vol. 8). Frontiers Media SA. https:\/\/doi.org\/10.3389\/feduc.2023.1106679","DOI":"10.3389\/feduc.2023.1106679"},{"key":"3118_CR22","doi-asserted-by":"publisher","DOI":"10.22054\/ims.2023.75523.2375","author":"M Salari","year":"2024","unstructured":"Salari M, Radfar R, Faghihi M. Bus Intell Manage Stud. 2024;12(47):315\u201366. https:\/\/doi.org\/10.22054\/ims.2023.75523.2375. Predicting students\u2019 performance using machine learning algorithms and educational data mining (a case study of Shahed University)."},{"key":"3118_CR23","doi-asserted-by":"publisher","first-page":"541","DOI":"10.46743\/2160-3715\/2021.4492","volume":"26","author":"N Jain","year":"2021","unstructured":"Jain N. Survey versus interviews: comparing data collection tools for exploratory research. Qualitative Rep. 2021;26:541\u201354. https:\/\/doi.org\/10.46743\/2160-3715\/2021.4492.","journal-title":"Qualitative Rep"},{"key":"3118_CR24","doi-asserted-by":"publisher","first-page":"110114","DOI":"10.1016\/j.asoc.2023.110114","volume":"136","author":"L Zheng","year":"2023","unstructured":"Zheng L, Wang C, Chen X, Song Y, Meng Z, Zhang R. Evolutionary machine learning builds smart education big data platform: data-driven higher education. Appl Soft Comput. 2023;136:110114. https:\/\/doi.org\/10.1016\/j.asoc.2023.110114.","journal-title":"Appl Soft Comput"},{"key":"3118_CR25","doi-asserted-by":"crossref","unstructured":"Saxena M, Gupta S. (2022, July). Prediction of Academic Performance of Students Using Multiple Regression. In 4th International Conference on Communication & Information Processing (ICCIP).","DOI":"10.2139\/ssrn.4289258"},{"issue":"9","key":"3118_CR26","doi-asserted-by":"publisher","first-page":"982","DOI":"10.1007\/s42452-019-0884-7","volume":"1","author":"ET Lau","year":"2019","unstructured":"Lau ET, Sun L, Yang Q. Modelling, prediction and classification of student academic performance using artificial neural networks. SN Appl Sci. 2019;1(9):982. https:\/\/doi.org\/10.1007\/s42452-019-0884-7.","journal-title":"SN Appl Sci"},{"key":"3118_CR27","doi-asserted-by":"publisher","unstructured":"Dawar I, Kumar N, Negi S, Pathan S, Layek S. (2023, March). Text Categorization using Supervised Machine Learning Techniques. In 2023 Sixth International Conference of Women in Data Science at Prince Sultan University (WiDS PSU) (pp. 185\u2013190). IEEE. https:\/\/doi.org\/10.1109\/WiDS-PSU57071.2023.00046","DOI":"10.1109\/WiDS-PSU57071.2023.00046"},{"issue":"August","key":"3118_CR28","doi-asserted-by":"publisher","first-page":"103999","DOI":"10.1016\/j.compedu.2020.103999","volume":"158","author":"ML Bernacki","year":"2020","unstructured":"Bernacki ML, Chavez MM, Uesbeck PM. Predicting achievement and providing support before STEM majors begin to fail. Comput Educ. 2020;158(August):103999. https:\/\/doi.org\/10.1016\/j.compedu.2020.103999.","journal-title":"Comput Educ"},{"key":"3118_CR29","doi-asserted-by":"publisher","DOI":"10.1016\/j.heliyon.2020.e04081","author":"F Cruz-Jesus","year":"2020","unstructured":"Cruz-Jesus F, Castelli M, Oliveira T, Mendes R, Nunes C, Sa-Velho M, Rosa-Louro A. Using artifcial intelligence methods to assess academic achievement in public high schools of a European Union country. Heliyon. 2020. https:\/\/doi.org\/10.1016\/j.heliyon.2020.e04081.","journal-title":"Heliyon"}],"container-title":["SN Computer Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42979-024-03118-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s42979-024-03118-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42979-024-03118-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,2]],"date-time":"2024-08-02T13:31:26Z","timestamp":1722605486000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s42979-024-03118-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,2]]},"references-count":29,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2024,8]]}},"alternative-id":["3118"],"URL":"https:\/\/doi.org\/10.1007\/s42979-024-03118-3","relation":{"references":[{"id-type":"uri","id":"","asserted-by":"subject"}]},"ISSN":["2661-8907"],"issn-type":[{"value":"2661-8907","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,8,2]]},"assertion":[{"value":"7 May 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 July 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 August 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of Interest"}}],"article-number":"758"}}