{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,12]],"date-time":"2026-06-12T15:45:26Z","timestamp":1781279126434,"version":"3.54.1"},"reference-count":93,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2024,4,22]],"date-time":"2024-04-22T00:00:00Z","timestamp":1713744000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Data"],"abstract":"<jats:p>College context and academic performance are important determinants of academic success; using students\u2019 prior experience with machine learning techniques to predict academic success before the end of the first year reinforces college self-efficacy. Dropout prediction is related to student retention and has been studied extensively in recent work; however, there is little literature on predicting academic success using educational machine learning. For this reason, CRISP-DM methodology was applied to extract relevant knowledge and features from the data. The dataset examined consists of 6690 records and 21 variables with academic and socioeconomic information. Preprocessing techniques and classification algorithms were analyzed. The area under the curve was used to measure the effectiveness of the algorithm; XGBoost had an AUC = 87.75% and correctly classified eight out of ten cases, while the decision tree improved interpretation with ten rules in seven out of ten cases. Recognizing the gaps in the study and that on-time completion of college consolidates college self-efficacy, creating intervention and support strategies to retain students is a priority for decision makers. Assessing the fairness and discrimination of the algorithms was the main limitation of this work. In the future, we intend to apply the extracted knowledge and learn about its influence of on university management.<\/jats:p>","DOI":"10.3390\/data9040060","type":"journal-article","created":{"date-parts":[[2024,4,22]],"date-time":"2024-04-22T12:38:41Z","timestamp":1713789521000},"page":"60","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":38,"title":["Predicting Academic Success of College Students Using Machine Learning Techniques"],"prefix":"10.3390","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9150-4009","authenticated-orcid":false,"given":"Jorge Humberto","family":"Guanin-Fajardo","sequence":"first","affiliation":[{"name":"Facultad de Ciencias de la Ingenier\u00eda, Universidad T\u00e9cnica Estatal de Quevedo, Quevedo 120508, Ecuador"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4296-0299","authenticated-orcid":false,"given":"Javier","family":"Gua\u00f1a-Moya","sequence":"additional","affiliation":[{"name":"Facultad de Ingenier\u00eda, Pontificia Universidad Cat\u00f3lica del Ecuador, Quito 170525, Ecuador"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5887-3977","authenticated-orcid":false,"given":"Jorge","family":"Casillas","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Artificial Intelligence, University of Granada, 18071 Granada, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,4,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Realinho, V., Machado, J., Baptista, L., and Martins, M.V. (2022). Predicting Student Dropout and Academic Success. Data, 7.","DOI":"10.3390\/data7110146"},{"key":"ref_2","first-page":"74","article-title":"University student retention: Best time and data to identify undergraduate students at risk of dropout","volume":"57","year":"2018","journal-title":"Innov. Educ. Teach. Int."},{"key":"ref_3","first-page":"e1507","article-title":"Patterns to Identify Dropout University Students with Educational Data Mining","volume":"23","author":"Barbosa","year":"2021","journal-title":"Rev. Electron. De Investig. Educ."},{"key":"ref_4","first-page":"480","article-title":"Early detection of students at dropout risk using administrative data and machine learning","volume":"40","author":"Silveira","year":"2021","journal-title":"RISTI\u2014Rev. Iber. De Sist. E Tecnol. De Inf."},{"key":"ref_5","first-page":"127","article-title":"Contexto universitario, profesores y estudiantes: V\u00ednculos y \u00e9xito acad\u00e9mico","volume":"88","author":"Barranquero","year":"2022","journal-title":"Rev. Iberoam. De Educ."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"106903","DOI":"10.1016\/j.compeleceng.2020.106903","article-title":"Enhancing prediction of student success: Automated machine learning approach","volume":"89","author":"Zeineddine","year":"2020","journal-title":"Comput. Electr. Eng."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Guerrero-Higueras, M., Llamas, C.F., Gonz\u00e1lez, L.S., Fern\u00e1ndez, A.G., Costales, G.E., and Gonz\u00e1lez, M.C. (2020). Academic Success Assessment through Version Control Systems. Appl. Sci., 10.","DOI":"10.3390\/app10041492"},{"key":"ref_8","unstructured":"Rafik, M. (2023). Artificial Intelligence in Higher Education and Scientific Research. Bridging Human and Machine: Future Education with Intelligence, Springer."},{"key":"ref_9","unstructured":"BOE (2024, March 23). BOE-A-2023-7500 Ley Org\u00e1nica 2\/2023, de 22 de marzo, del Sistema Universitario. Available online: https:\/\/www.boe.es\/buscar\/act.php?id=BOE-A-2023-7500."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1080\/09639280701740142","article-title":"Exogenous and endogenous factors influencing students\u2019 performance in undergraduate accounting modules","volume":"18","author":"Guney","year":"2009","journal-title":"Account. Educ."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Tamada, M.M., Giusti, R., and Netto, J.F.d.M. (2022). Predicting Students at Risk of Dropout in Technical Course Using LMS Logs. Electronics, 11.","DOI":"10.3390\/electronics11030468"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"785","DOI":"10.1007\/s10734-017-0170-9","article-title":"Social selection in higher education. Enrolment, dropout and timely degree attainment in Italy","volume":"75","author":"Contini","year":"2017","journal-title":"High. Educ."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1016\/j.chb.2017.01.047","article-title":"Evaluating the effectiveness of educational data mining techniques for early prediction of students\u2019 academic failure in introductory programming courses","volume":"73","author":"Costa","year":"2017","journal-title":"Comput. Hum. Behav."},{"key":"ref_14","first-page":"107","article-title":"Early dropout prediction using data mining: A case study with high school students","volume":"33","author":"Cano","year":"2015","journal-title":"Expert Syst."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1007\/s40747-017-0037-9","article-title":"An insight into imbalanced Big Data classification: Outcomes and challenges","volume":"3","author":"Chawla","year":"2017","journal-title":"Complex Intell. Syst."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"100018","DOI":"10.1016\/j.caeai.2021.100018","article-title":"Artificial neural networks in academic performance prediction: Systematic implementation and predictor evaluation","volume":"2","author":"Musso","year":"2021","journal-title":"Comput. Educ. Artif. Intell."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"233","DOI":"10.4067\/S0718-50062020000500233","article-title":"Academic performance prediction by machine learning as a success\/failure indicator for engineering students","volume":"13","author":"Contreras","year":"2020","journal-title":"Form. Univ."},{"key":"ref_18","first-page":"217","article-title":"Improve student performance prediction using ensemble model for higher education","volume":"Volume 318","author":"Hassan","year":"2019","journal-title":"Frontiers in Artificial Intelligence and Applications"},{"key":"ref_19","first-page":"1","article-title":"Ensembles for feature selection: A review and future trends","volume":"52","year":"2018","journal-title":"Inf. Fusion"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"310","DOI":"10.22581\/muet1982.2002.08","article-title":"The role of knowledge management and data mining in improving educational practices and the learning infrastructure","volume":"39","author":"Meghji","year":"2020","journal-title":"Mehran Univ. Res. J. Eng. Technol."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Crivei, L., Czibula, G., Ciubotariu, G., and Dindelegan, M. (2020, January 21\u201323). Unsupervised learning based mining of academic data sets for students\u2019 performance analysis. Proceedings of the SACI 2020\u2014IEEE 14th International Symposium on Applied Computational In-telligence and Informatics, Proceedings, Timisoara, Romania.","DOI":"10.1109\/SACI49304.2020.9118835"},{"key":"ref_22","first-page":"291","article-title":"Semisupervised learning to discover the average scale of graduation of university students","volume":"15","author":"Casillas","year":"2019","journal-title":"Rev. Conrado"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Alyahyan, E., and D\u00fc\u015ftearg\u00f6r, D. (2020, January 3\u201315). Decision trees for very early prediction of student\u2019s achievement. Proceedings of the 2020 2nd International Conference on Computer and Information Sciences (ICCIS), Sakaka, Saudi Arabia.","DOI":"10.1109\/ICCIS49240.2020.9257646"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"107649","DOI":"10.1109\/ACCESS.2022.3211070","article-title":"An Explainable Model for Identifying At-Risk Student at Higher Education","volume":"10","author":"Alwarthan","year":"2022","journal-title":"IEEE Access"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1527","DOI":"10.1007\/s10639-018-9839-7","article-title":"Data mining approach to predicting the performance of first year student in a university using the admission requirements","volume":"24","author":"Adekitan","year":"2018","journal-title":"Educ. Inf. Technol."},{"key":"ref_26","unstructured":"Fayyad, U., Piatetsky-Shapiro, G., and Smyth, P. (1996, January 2\u20134). Knowledge Discovery and Data Mining: Towards a Unifying Framework. Proceedings of the Second International Conference on Knowledge Discovery and Data Mining, Portland, Oregon."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"220","DOI":"10.1016\/j.eswa.2016.12.035","article-title":"Learning from class-imbalanced data: Review of methods and applications","volume":"73","author":"Haixiang","year":"2017","journal-title":"Expert Syst. Appl."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1613\/jair.953","article-title":"SMOTE: Synthetic Minority Over-sampling Technique Nitesh","volume":"16","author":"Chawla","year":"2002","journal-title":"J. Artif. Intell. Res."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1186\/s41239-021-00279-6","article-title":"Enhancing data pipelines for forecasting student performance: Integrating feature selection with crossvalidation","volume":"18","author":"Bertolini","year":"2021","journal-title":"Int. J. Educ. Technol. High. Educ."},{"key":"ref_30","first-page":"269","article-title":"Utilizing Feature Selection in Identifying Predicting Factors of Student Retention","volume":"10","author":"Febro","year":"2019","journal-title":"Int. J. Adv. Comput. Sci. Appl."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1016\/j.patcog.2016.05.012","article-title":"Feature selection using Forest Optimization Algorithm","volume":"60","author":"Ghaemi","year":"2016","journal-title":"Pattern Recognit."},{"key":"ref_32","unstructured":"R Development Core Team (2020). R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing."},{"key":"ref_33","first-page":"2825","article-title":"Scikit-learn: Machine learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref_34","first-page":"121","article-title":"Using Educational Data Mining to Predict Students\u2019 Academic Performance for Applying Early Interventions","volume":"20","author":"Alturki","year":"2021","journal-title":"J. Inf. Technol. Educ. JITE. Innov. Pract. IIP"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"189069","DOI":"10.1109\/ACCESS.2020.3031572","article-title":"Creating a recommender system to support higher education students in the subject enrollment decisi\u00f3n","volume":"8","author":"Preciado","year":"2020","journal-title":"IEEE Access"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.knosys.2018.07.042","article-title":"Predicting academic performance by considering student heterogeneity","volume":"161","author":"Helal","year":"2018","journal-title":"Knowl.-Based Syst."},{"key":"ref_37","first-page":"1","article-title":"Educational data mining: Prediction of students\u2019 academic performance using machine learning algorithms","volume":"9","year":"2022","journal-title":"Smart Learn. Environ."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"2165","DOI":"10.1007\/s10639-020-10346-6","article-title":"A data-driven approach to predict first-year students\u2019 academic success in higher education institutions","volume":"26","author":"Gil","year":"2020","journal-title":"Educ. Inf. Technol."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1048","DOI":"10.1007\/s11162-019-09546-y","article-title":"Predicting University Students\u2019 Academic Success and Major Using Random Forests","volume":"60","author":"Beaulac","year":"2019","journal-title":"Res. High. Educ."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1016\/j.ins.2019.04.052","article-title":"Evolutionary inversion of class distribution in overlapping areas for multiclass imbalanced learning","volume":"494","author":"Fernandes","year":"2019","journal-title":"Inf. Sci."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"219","DOI":"10.32614\/RJ-2017-027","article-title":"Noisefiltersr the noise-filtersr package","volume":"9","author":"Morales","year":"2017","journal-title":"R J."},{"key":"ref_42","unstructured":"Zeng, X., and Martinez, T. (2003, January 17). A noise filtering method using neural networks. Proceedings of the IEEE International Workshop on Soft Computing Techniques in Instrumentation and Measurement and Related Applications (SCIMA2003), Provo, UT, USA."},{"key":"ref_43","unstructured":"Verbaeten, S., and Assche, A. (2003). Multiple Classifier Systems. MCS 2003, Springer. Lecture Notes in Computer Science."},{"key":"ref_44","first-page":"111268","article-title":"A comparative analysis of machine learning and statistical methods for evaluating building performance: A systematic review and future benchmarking framework","volume":"252","author":"Ali","year":"2024","journal-title":"J. Affect. Disord."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Rajula, H.S.R., Verlato, G., Manchia, M., Antonucci, N., and Fanos, V. (2020). Comparison of Conventional Statistical Methods with Machine Learning in Medicine: Diagnosis, Drug Development, and Treatment. Medicina, 56.","DOI":"10.3390\/medicina56090455"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.knosys.2015.12.006","article-title":"Tutorial on practical tips of the most influential data preprocessing algo-rithms in data mining","volume":"98","author":"Luengo","year":"2016","journal-title":"Knowl.-Based Syst."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1016\/j.inffus.2017.09.010","article-title":"Dynamic classifier selection: Recent advances and perspectives","volume":"41","author":"Sabourin","year":"2018","journal-title":"Inf. Fusion"},{"key":"ref_48","unstructured":"Yadav, S.K., and Pal, S. (2012). Data Mining: A Prediction for Performance Improvement of Engineering Students using Classification. arXiv."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"1145","DOI":"10.1016\/S0031-3203(96)00142-2","article-title":"The use of the area under the ROC curve in the evaluation of machine learning algorithms","volume":"30","author":"Bradley","year":"1997","journal-title":"Pattern Recognit."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"823","DOI":"10.1002\/cae.21839","article-title":"Associating students and teachers for tutoring in higher education using clustering and data mining","volume":"25","author":"Medina","year":"2017","journal-title":"Comput. Appl. Eng. Educ."},{"key":"ref_51","unstructured":"Kononenko, I. (1994). European Conference on Machine Learning, Springer."},{"key":"ref_52","unstructured":"Liu, H., and Setiono, R. (1996, January 4\u20137). Feature selection and classification: A probabilistic wrapper approach. Proceedings of the 9th International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems (IEAAIE\u00b496), Fukuoka, Japan."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1109\/TSMCB.2006.883267","article-title":"Wrapper\u2013Filter Feature Selection Algorithm Using a Memetic Framework","volume":"37","author":"Zhu","year":"2007","journal-title":"IEEE Trans. Syst. Man Cybern. Part B"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"491","DOI":"10.1109\/TKDE.2005.66","article-title":"Toward integrating feature selection algorithms for classification and clustering","volume":"17","author":"Liu","year":"2005","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"519","DOI":"10.1080\/713827181","article-title":"An analysis of four missing data treatment methods for supervised learning","volume":"17","author":"Batista","year":"2003","journal-title":"Appl. Artif. Intell."},{"key":"ref_56","unstructured":"Kira, K., and Rendell, L. (1992, January 12\u201316). The feature selection problem: Traditional methods and a new algorithm. Proceedings of the AAAI\u201992: Proceedings of the Tenth National Conference on Artificial Intelligence, San Jose, CA, USA."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"210","DOI":"10.1016\/j.neucom.2015.05.105","article-title":"Mutual information criterion for feature selection from incomplete data","volume":"168","author":"Qian","year":"2015","journal-title":"Neurocomputing"},{"key":"ref_58","unstructured":"Sheinvald, J., Dom, B., and Niblack, W. (1990, January 16\u201321). A modeling approach to feature selection. Proceedings of the 10th International Conference on Pattern Recognition, Atlantic City, NJ, USA."},{"key":"ref_59","unstructured":"(2008). The Concise Encyclopedia of Statistics, Springer."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1007\/BF00116251","article-title":"Induction of decision trees","volume":"1","author":"Quinlan","year":"1986","journal-title":"Mach. Learn."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"421","DOI":"10.1007\/s10888-011-9188-x","article-title":"The origins of the Gini index: Extracts from Variabilit\u00e0 e Mutabilit\u00e0 (1912) by Corrado Gini","volume":"10","author":"Ceriani","year":"2011","journal-title":"J. Econ. Inequal."},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Pawlak, Z. (1991). Imprecise Categories, Approximations and Rough Sets, Springer.","DOI":"10.1007\/978-94-011-3534-4"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"307","DOI":"10.1016\/j.cam.2017.04.036","article-title":"A hybrid system with filter approach and multiple population genetic algorithm for feature selection in credit scoring","volume":"329","author":"Wang","year":"2018","journal-title":"J. Comput. Appl. Math."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.ins.2018.06.056","article-title":"Improving imbalanced learning through a heuristic oversampling method based on k-means and SMOTE","volume":"465","author":"Douzas","year":"2018","journal-title":"Inf. Sci."},{"key":"ref_65","first-page":"10","article-title":"Balancing training data for automated annotation of keywords: A case study","volume":"3","author":"Batista","year":"2003","journal-title":"WOB"},{"key":"ref_66","first-page":"769","article-title":"Two modifications of cnn","volume":"6","author":"Ivan","year":"1976","journal-title":"IEEE Trans. Syst. Man Commun. SMC"},{"key":"ref_67","first-page":"539","article-title":"Exploratory Undersampling for Class-Imbalance Learning","volume":"39","author":"Liu","year":"2008","journal-title":"IEEE Trans. Syst. Man Cybern. Part B"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1109\/5254.708428","article-title":"Support vector machines","volume":"13","author":"Hearst","year":"1998","journal-title":"IEEE Intell. Syst."},{"key":"ref_69","unstructured":"Almeida, L.B. (1997). Handbook of Neural Computation, Oxford University Press."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Ensemble Mach. Learn."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"1189","DOI":"10.1214\/aos\/1013203451","article-title":"Greedy function approximation: A gradient boosting machine","volume":"29","author":"Friedman","year":"2001","journal-title":"Ann. Stat."},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Chen, T., and Guestrin, C. (2016, January 13\u201317). Xgboost: A scalable tree boosting system. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA.","DOI":"10.1145\/2939672.2939785"},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1007\/BF00058655","article-title":"Bagging predictors","volume":"24","author":"Breiman","year":"1996","journal-title":"Mach. Learn."},{"key":"ref_74","first-page":"713","article-title":"Na\u00efve Bayes","volume":"15","author":"Webb","year":"2010","journal-title":"Encycl. Mach. Learn."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"757","DOI":"10.1007\/978-981-15-5148-2_66","article-title":"Student\u2019s performance prediction using data mining technique depending on overall academic status and environmental attributes","volume":"Volume 1166","author":"Shetu","year":"2021","journal-title":"Advances in Intelligent Systems and Computing"},{"key":"ref_76","unstructured":"Fisher, R.A. (1935). The Design of Experiments, Oliver & Boyd."},{"key":"ref_77","first-page":"1","article-title":"Statistical Comparisons of Classifiers over Multiple Data Sets","volume":"7","year":"2006","journal-title":"J. Mach. Learn. Res."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"997","DOI":"10.1037\/0003-066X.49.12.997","article-title":"The eart is round (p < 0.05)","volume":"49","author":"Cohen","year":"1994","journal-title":"Am. Psychol."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1037\/1082-989X.1.2.115","article-title":"Statistical significance testing and cumulative knowledge in psychology: Implications for training of researchers","volume":"1","author":"Schmidt","year":"1996","journal-title":"Psychol. Methods"},{"key":"ref_80","unstructured":"Harlow, L.L., Mulaik, S.A., and Steiger, J.H. (1997). What If There Were No Significance Tests?, Lawrence Erlbaum Associates Publishers."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"2895","DOI":"10.37624\/IJERT\/13.10.2020.2895-2908","article-title":"Students Performance: From Detection of Failures and Anomaly Cases to the Solutions-Based Mining Algorithms","volume":"13","year":"2020","journal-title":"Int. J. Eng. Res. Technol."},{"key":"ref_82","first-page":"1677","article-title":"A comparative study of machine learning algorithms for virtual learning environment performance prediction","volume":"12","author":"Ismanto","year":"2023","journal-title":"IAES Int. J. Artif. Intell."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"288","DOI":"10.1007\/978-981-16-1480-4_25","article-title":"Feature Selection Using Ensemble Techniques","volume":"Volume 1395","author":"Kaushik","year":"2021","journal-title":"Futuristic Trends in Network and Communication Technologies"},{"key":"ref_84","first-page":"598","article-title":"Information literacy as a key to academic success: Results from a longitudinal study","volume":"676","author":"Mayer","year":"2016","journal-title":"Commun. Comput. Inf. Sci."},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"562","DOI":"10.1037\/0022-0663.94.3.562","article-title":"Predicting success in college: A longitudinal study of achievement goals and ability measures as predictors of interest and performance from freshman year through graduation","volume":"94","author":"Harackiewicz","year":"2002","journal-title":"J. Educ. Psychol."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"959","DOI":"10.1109\/TSP.2015.2496278","article-title":"Predicting Grades","volume":"64","author":"Meier","year":"2015","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1109\/TE.2021.3137086","article-title":"MIDFIELD: A Resource for Longitudinal Student Record Research","volume":"65","author":"Lord","year":"2022","journal-title":"IEEE Trans. Educ."},{"key":"ref_88","doi-asserted-by":"crossref","unstructured":"Tompsett, J., and Knoester, C. (2023). Family socioeconomic status and college attendance: A consideration of individual-level and school-level pathways. PLoS ONE, 18.","DOI":"10.1371\/journal.pone.0284188"},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"29101","DOI":"10.1007\/s11432-017-9371-y","article-title":"Pre-course student performance prediction with multi-instance multi-label learning","volume":"62","author":"Ma","year":"2018","journal-title":"Sci. China Inf. Sci."},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"911","DOI":"10.1007\/s10994-016-5612-6","article-title":"Confidence curves: An alternative to null hypothesis significance testing for the comparison of classifiers","volume":"106","author":"Berrar","year":"2017","journal-title":"Mach. Learn."},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1080\/0952813X.2012.680252","article-title":"Significance tests or confidence intervals: Which are preferable for the comparison of classifiers?","volume":"25","author":"Berrar","year":"2013","journal-title":"J. Exp. Theor. Artif. Intell."},{"key":"ref_92","first-page":"2677","article-title":"An Extension on \u201cStatistical Comparisons of Classifiers over Multiple Data Sets\u201d for all Pairwise Comparisons","volume":"9","author":"Herrera","year":"2008","journal-title":"J. Mach. Learn. Res."},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1515\/jacsm-2017-0002","article-title":"Friedman and Wilcoxon Evaluations Comparing SVM, Bagging, Boosting, K-NN and Decision Tree Classifiers","volume":"9","author":"Biju","year":"2017","journal-title":"J. Appl. Comput. Sci. Methods"}],"container-title":["Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2306-5729\/9\/4\/60\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:32:19Z","timestamp":1760106739000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2306-5729\/9\/4\/60"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4,22]]},"references-count":93,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2024,4]]}},"alternative-id":["data9040060"],"URL":"https:\/\/doi.org\/10.3390\/data9040060","relation":{},"ISSN":["2306-5729"],"issn-type":[{"value":"2306-5729","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,4,22]]}}}