{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T04:00:32Z","timestamp":1769918432165,"version":"3.49.0"},"reference-count":73,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2023,7,17]],"date-time":"2023-07-17T00:00:00Z","timestamp":1689552000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,7,17]],"date-time":"2023-07-17T00:00:00Z","timestamp":1689552000000},"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":["Educ Inf Technol"],"published-print":{"date-parts":[[2024,4]]},"DOI":"10.1007\/s10639-023-12007-w","type":"journal-article","created":{"date-parts":[[2023,7,17]],"date-time":"2023-07-17T05:01:31Z","timestamp":1689570091000},"page":"5447-5483","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["An empirical assessment of smote variants techniques and interpretation methods in improving the accuracy and the interpretability of student performance models"],"prefix":"10.1007","volume":"29","author":[{"given":"Hayat","family":"Sahlaoui","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5387-9819","authenticated-orcid":false,"given":"El Arbi Abdellaoui","family":"Alaoui","sequence":"additional","affiliation":[]},{"given":"Said","family":"Agoujil","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9821-6146","authenticated-orcid":false,"given":"Anand","family":"Nayyar","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,7,17]]},"reference":[{"key":"12007_CR1","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1186\/s40561-019-0083-4","volume":"6","author":"GA Ak\u00e7ap\u0131nar","year":"2019","unstructured":"Ak\u00e7ap\u0131nar, G. A. (2019). Developing an early-warning system for spotting at-risk students by using eBook interaction logs. Smart Learning Environments, 6, 4.","journal-title":"Smart Learning Environments"},{"key":"12007_CR2","unstructured":"Ali, A. A. (2013). Classification with class imbalance problem. International Journal of Advances in Soft Computing and its Applications, 5 (3), 176\u2013204."},{"key":"12007_CR3","doi-asserted-by":"publisher","first-page":"185","DOI":"10.1016\/j.ijmedinf.2017.10.002","volume":"108","author":"AAED Awad","year":"2017","unstructured":"Awad, A. A. E. D. (2017). Early hospital mortality prediction of intensive care unit patients using an ensemble learning approach. International Journal of Medical Informatics, 108, 185\u2013195.","journal-title":"International Journal of Medical Informatics"},{"key":"12007_CR4","doi-asserted-by":"crossref","unstructured":"Barandela, R. A. (2004). The imbalanced training sample problem: Under or over sampling? Joint IAPR international workshops on statistical techniques in pattern recognition (SPR) and structural and syntactic pattern recognition (SSPR) (s. 806--814). Springer.","DOI":"10.1007\/978-3-540-27868-9_88"},{"key":"12007_CR5","doi-asserted-by":"publisher","first-page":"275","DOI":"10.3390\/educsci9040275","volume":"9","author":"TM Barros","year":"2019","unstructured":"Barros, T. M. (2019). Predictive models for imbalanced data: A school dropout perspective. Education Sciences, 9, 275.","journal-title":"Education Sciences"},{"key":"12007_CR6","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1145\/1007730.1007735","volume":"6","author":"GE Batista","year":"2004","unstructured":"Batista, G. E. (2004). A study of the behavior of several methods for balancing machine learning training data. ACM SIGKDD Explorations Newsletter, 6, 20\u201329.","journal-title":"ACM SIGKDD Explorations Newsletter"},{"key":"12007_CR7","first-page":"46","volume":"7","author":"EB Belachew","year":"2017","unstructured":"Belachew, E. B. (2017). Student performance prediction model using machine learning approach: The case of Wolkite university. International Journal If Advanced Research in Computer Science and Software Engineering, 7, 46\u201350.","journal-title":"International Journal If Advanced Research in Computer Science and Software Engineering"},{"key":"12007_CR8","first-page":"357","volume":"39","author":"J Berkson","year":"1944","unstructured":"Berkson, J. (1944). Application of the logistic function to bio-assay. Journal of the American Statistical Association, 39, 357\u2013365.","journal-title":"Journal of the American Statistical Association"},{"key":"12007_CR9","unstructured":"Brownlee, J. (2018).\u00a0A Gentle Introduction to Normality Tests in Python. https:\/\/machinelearningmastery.com\/a-gentle-introduction-to-normality-tests-in-python\/"},{"key":"12007_CR10","doi-asserted-by":"publisher","first-page":"2833","DOI":"10.3390\/su11102833","volume":"11","author":"DAM Buena\u00f1o-Fer\u0144andez","year":"2019","unstructured":"Buena\u00f1o-Fer\u0144andez, D. A. M. (2019). Application of machine learning in predicting performance for computer engineering students: A case study. Sustainability, 11, 2833.","journal-title":"Sustainability"},{"key":"12007_CR11","doi-asserted-by":"publisher","first-page":"832","DOI":"10.3390\/electronics8080832","volume":"8","author":"DV Carvalho","year":"2019","unstructured":"Carvalho, D. V. (2019). Machine learning interpretability: A survey on methods and metrics. Electronics, 8, 832.","journal-title":"Electronics"},{"key":"12007_CR12","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1613\/jair.953","volume":"16","author":"NV Chawla","year":"2002","unstructured":"Chawla, N. V. (2002). SMOTE: Synthetic minority over-sampling technique. Journal of Artificial Intelligence Research, 16, 321\u2013357.","journal-title":"Journal of Artificial Intelligence Research"},{"key":"12007_CR13","first-page":"24","volume":"110","author":"CA Chen","year":"2004","unstructured":"Chen, C. A. (2004). Using random forest to learn imbalanced data. University of California, Berkeley, 110, 24.","journal-title":"University of California, Berkeley"},{"key":"12007_CR14","doi-asserted-by":"crossref","unstructured":"Chitti, M. A. (2020). Need for interpretable student performance prediction. 2020 13th International Conference on Developments in eSystems Engineering (DeSE) (s. 269--272). IEEE.","DOI":"10.1109\/DeSE51703.2020.9450735"},{"key":"12007_CR15","doi-asserted-by":"publisher","first-page":"306","DOI":"10.1016\/j.procs.2018.10.313","volume":"140","author":"HR Darabi","year":"2018","unstructured":"Darabi, H. R. (2018). Forecasting mortality risk for patients admitted to intensive care units using machine learning. Procedia Computer Science, 140, 306\u2013313.","journal-title":"Procedia Computer Science"},{"key":"12007_CR16","first-page":"1","volume":"7","author":"J Dem\u0161ar","year":"2006","unstructured":"Dem\u0161ar, J. (2006). Statistical comparisons of classifiers over multiple data sets. The Journal of Machine Learning Research, 7, 1\u201330.","journal-title":"The Journal of Machine Learning Research"},{"key":"12007_CR17","doi-asserted-by":"publisher","first-page":"863","DOI":"10.1613\/jair.1.11192","volume":"61","author":"A Fer\u0144andez","year":"2018","unstructured":"Fer\u0144andez, A. (2018). SMOTE for learning from imbalanced data: Progress and challenges, marking the 15-year anniversary. Journal of Artificial Intelligence Research, 61, 863\u2013905.","journal-title":"Journal of Artificial Intelligence Research"},{"key":"12007_CR18","unstructured":"Fisher, R. (1956). Statistical methods and scientific inference Oxford. Hafner Publishing Co."},{"key":"12007_CR19","unstructured":"Freund, Y. A. (1996). Experiments with a new boosting algorithm. icml (Cilt 96, s. 148--156). i\u00e7inde Citeseer."},{"key":"12007_CR20","doi-asserted-by":"publisher","first-page":"675","DOI":"10.1080\/01621459.1937.10503522","volume":"32","author":"M Friedman","year":"1937","unstructured":"Friedman, M. (1937). The use of ranks to avoid the assumption of normality implicit in the analysis of variance. Journal of the American Statistical Association, 32, 675\u2013701.","journal-title":"Journal of the American Statistical Association"},{"key":"12007_CR21","doi-asserted-by":"publisher","first-page":"86","DOI":"10.1214\/aoms\/1177731944","volume":"11","author":"M Friedman","year":"1940","unstructured":"Friedman, M. (1940). A comparison of alternative tests of significance for the problem of m rankings. The Annals of Mathematical Statistics, 11, 86\u201392.","journal-title":"The Annals of Mathematical Statistics"},{"key":"12007_CR22","doi-asserted-by":"publisher","first-page":"463","DOI":"10.1109\/TSMCC.2011.2161285","volume":"42","author":"MA Galar","year":"2011","unstructured":"Galar, M. A. (2011). A review on ensembles for the class imbalance problem: Bagging-, boosting-, and hybrid-based approaches. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 42, 463\u2013484.","journal-title":"IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews)"},{"key":"12007_CR23","doi-asserted-by":"publisher","first-page":"47","DOI":"10.5267\/j.ijdns.2019.1.003","volume":"3","author":"RA Ghorbani","year":"2019","unstructured":"Ghorbani, R. A. (2019). Predictive data mining approaches in medical diagnosis: A review of some diseases prediction. International Journal of Data and Network Science, 3, 47\u201370.","journal-title":"International Journal of Data and Network Science"},{"key":"12007_CR24","doi-asserted-by":"publisher","first-page":"67899","DOI":"10.1109\/ACCESS.2020.2986809","volume":"8","author":"RA Ghorbani","year":"2020","unstructured":"Ghorbani, R. A. (2020). Comparing different resampling methods in predicting students\u2019 performance using machine learning techniques. IEEE Access, 8, 67899\u201367911.","journal-title":"IEEE Access"},{"key":"12007_CR25","first-page":"56","volume":"214","author":"SA Ghose","year":"2015","unstructured":"Ghose, S. A. (2015). An improved patient-specific mortality risk prediction in ICU in a random Forest classification framework. Studies in Health Technology and Informatics, 214, 56\u201361.","journal-title":"Studies in Health Technology and Informatics"},{"key":"12007_CR26","doi-asserted-by":"publisher","first-page":"2273","DOI":"10.1016\/j.ins.2009.02.011","volume":"179","author":"DAK Guan","year":"2009","unstructured":"Guan, D. A. K. (2009). Nearest neighbor editing aided by unlabeled data. Information Sciences, 179, 2273\u20132282.","journal-title":"Information Sciences"},{"key":"12007_CR27","doi-asserted-by":"crossref","unstructured":"Guo, B. A. (2015). Predicting students performance in educational data mining. 2015 international symposium on educational technology (ISET) (s. 125--128). IEEE.","DOI":"10.1109\/ISET.2015.33"},{"key":"12007_CR28","doi-asserted-by":"publisher","first-page":"220","DOI":"10.1016\/j.eswa.2016.12.035","volume":"73","author":"GA Haixiang","year":"2017","unstructured":"Haixiang, G. A. (2017). Learning from class imbalanced data: Review of methods and applications. Expert Systems with Applications, 73, 220\u2013239.","journal-title":"Expert Systems with Applications"},{"key":"12007_CR29","doi-asserted-by":"crossref","unstructured":"Han, H. A.-Y.-H. (2005). Borderline-SMOTE: a new over-sampling method in imbalanced data sets learning. International conference on intelligent computing (s. 878--887). Springer.","DOI":"10.1007\/11538059_91"},{"key":"12007_CR30","doi-asserted-by":"publisher","first-page":"469","DOI":"10.1016\/j.chb.2014.04.002","volume":"36","author":"Y-HALP Hu","year":"2014","unstructured":"Hu, Y.-H.A.L.P. (2014). Developing early warning systems to predict students\u2019 online learning performance. Computers in Human Behavior, 36, 469\u2013478.","journal-title":"Computers in Human Behavior"},{"key":"12007_CR31","doi-asserted-by":"crossref","unstructured":"Hussain, M. A. (2018). Student engagement predictions in an e-learning system and their impact on student course assessment scores. Computational Intelligence and Neuroscience, 2018, 21.","DOI":"10.1155\/2018\/6347186"},{"key":"12007_CR32","doi-asserted-by":"publisher","first-page":"88","DOI":"10.3390\/math6060088","volume":"6","author":"L J\u00e4ntschi","year":"2018","unstructured":"J\u00e4ntschi, L. (2018). Computation of probability associated with Anderson-Darling statistic. Mathematics, 6, 88.","journal-title":"Mathematics"},{"key":"12007_CR33","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40537-019-0192-5","volume":"6","author":"JM Johnson","year":"2019","unstructured":"Johnson, J. M. (2019). Survey on deep learning with class imbalance. Journal of Big Data, 6, 1\u201354.","journal-title":"Journal of Big Data"},{"key":"12007_CR34","doi-asserted-by":"publisher","first-page":"8413","DOI":"10.3390\/app10238413","volume":"10","author":"SA Karlos","year":"2020","unstructured":"Karlos, S. A. (2020). Predicting and interpreting students\u2019 grades in distance higher education through a semi-regression method. Applied Sciences, 10, 8413.","journal-title":"Applied Sciences"},{"key":"12007_CR35","doi-asserted-by":"publisher","first-page":"2","DOI":"10.1016\/j.jksuci.2016.04.002","volume":"30","author":"AA Kaur","year":"2018","unstructured":"Kaur, A. A. (2018). An empirical evaluation of classification algorithms for fault prediction in open source projects. Journal of King Saud University-Computer and Information Sciences, 30, 2\u201317.","journal-title":"Journal of King Saud University-Computer and Information Sciences"},{"key":"12007_CR36","unstructured":"Keshtkar, F. A. (2018). Predicting risk of failure in online learning platforms using machine learning algorithms for modeling students\u2019 academic performance. Southeast Missouri State University."},{"key":"12007_CR37","doi-asserted-by":"crossref","unstructured":"Khosravi, H. A. (2017). Using learning analytics to investigate patterns of performance and engagement in large classes. Proceedings of the 2017 acm sigcse technical symposium on computer science education (s. 309--314). i\u00e7inde","DOI":"10.1145\/3017680.3017711"},{"key":"12007_CR38","first-page":"25","volume":"30","author":"SA Kotsiantis","year":"2006","unstructured":"Kotsiantis, S. A. (2006). Handling imbalanced datasets: A review. GESTS International Transactions on Computer Science and Engineering, 30, 25\u201336.","journal-title":"GESTS International Transactions on Computer Science and Engineering"},{"key":"12007_CR39","doi-asserted-by":"crossref","unstructured":"Koutina, M. A. (2011). Predicting postgraduate students\u2019 performance using machine learning techniques. Artificial intelligence applications and innovations (s. 159--168). i\u00e7inde Springer.","DOI":"10.1007\/978-3-642-23960-1_20"},{"key":"12007_CR40","doi-asserted-by":"publisher","first-page":"105662","DOI":"10.1016\/j.asoc.2019.105662","volume":"83","author":"G Kov\u00e1cs","year":"2019","unstructured":"Kov\u00e1cs, G. (2019). An empirical comparison and evaluation of minority oversampling techniques on a large number of imbalanced datasets. Applied Soft Computing, 83, 105662.","journal-title":"Applied Soft Computing"},{"key":"12007_CR41","doi-asserted-by":"publisher","first-page":"215","DOI":"10.1007\/s13748-019-00172-4","volume":"8","author":"LI-G-P-F Kuncheva","year":"2019","unstructured":"Kuncheva, L.I.-G.-P.-F. (2019). Instance selection improves geometric mean accuracy: A study on imbalanced data classification. Progress in Artificial Intelligence, 8, 215\u2013228.","journal-title":"Progress in Artificial Intelligence"},{"key":"12007_CR42","doi-asserted-by":"publisher","first-page":"141","DOI":"10.1016\/j.ijhm.2013.06.006","volume":"35","author":"HAC Li","year":"2013","unstructured":"Li, H. A. C. (2013). Parametric prediction on default risk of Chinese listed tourism companies by using random oversampling, isomap, and locally linear embeddings on imbalanced samples. International Journal of Hospitality Management, 35, 141\u2013151.","journal-title":"International Journal of Hospitality Management"},{"key":"12007_CR43","doi-asserted-by":"publisher","first-page":"218","DOI":"10.1016\/j.compind.2018.01.017","volume":"98","author":"JA Liu","year":"2018","unstructured":"Liu, J. A. (2018). Mortality prediction based on imbalanced high-dimensional ICU big data. Computers in Industry, 98, 218\u2013225.","journal-title":"Computers in Industry"},{"key":"12007_CR44","first-page":"539","volume":"39","author":"X-YAH Liu","year":"2008","unstructured":"Liu, X.-Y.A.H. (2008). Exploratory undersampling for class-imbalance learning. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 39, 539\u2013550.","journal-title":"IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics)"},{"key":"12007_CR45","unstructured":"Longadge, R. A. (2013). Class imbalance problem in data mining review. arXiv preprint arXiv:1305.1707."},{"key":"12007_CR46","doi-asserted-by":"publisher","first-page":"113","DOI":"10.1016\/j.ins.2013.07.007","volume":"250","author":"VA Lopez","year":"2013","unstructured":"Lopez, V. A. (2013). An insight into classification with imbalanced data: Empirical results and current trends on using data intrinsic characteristics. Information Sciences, 250, 113\u2013141.","journal-title":"Information Sciences"},{"key":"12007_CR47","unstructured":"Lundberg, S. M.-I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 30."},{"key":"12007_CR48","unstructured":"Ma, Y. A. (2013). Imbalanced learning: foundations, algorithms, and applications. John Wiley & Sons."},{"key":"12007_CR49","doi-asserted-by":"publisher","first-page":"315","DOI":"10.1007\/s10489-012-0374-8","volume":"38","author":"CA M\u00e1rquez-Vera","year":"2013","unstructured":"M\u00e1rquez-Vera, C. A. (2013). Predicting student failure at school using genetic programming and different data mining approaches with high dimensional and imbalanced data. Applied Intelligence, 38, 315\u2013330.","journal-title":"Applied Intelligence"},{"key":"12007_CR50","doi-asserted-by":"crossref","unstructured":"Mathew, J. A. (2015). Kernel-based SMOTE for SVM classification of imbalanced datasets. IECON 2015\u201341st Annual Conference of the IEEE Industrial Electronics Society (s. 001127--001132). i\u00e7inde IEEE.","DOI":"10.1109\/IECON.2015.7392251"},{"key":"12007_CR51","doi-asserted-by":"crossref","unstructured":"Moreno Garc\u00eda, M. N. (2014). Machine learning methods for mortality prediction of polytraumatized patients in intensive care units--dealing with imbalanced and high-dimensional data. International Conference on Intelligent Data Engineering and Automated Learning (s. 309--317). Springer.","DOI":"10.1007\/978-3-319-10840-7_38"},{"key":"12007_CR52","doi-asserted-by":"crossref","unstructured":"Mueen, A. A. (2016). Modeling and predicting students\u2019\u00a0academic performance using data mining techniques. International Journal of Modern Education & Computer Science, 8 (11), 36.","DOI":"10.5815\/ijmecs.2016.11.05"},{"key":"12007_CR53","doi-asserted-by":"publisher","first-page":"563","DOI":"10.1007\/s10844-015-0368-1","volume":"46","author":"KA Napierala","year":"2016","unstructured":"Napierala, K. A. (2016). Types of minority class examples and their influence on learning classifiers from imbalanced data. Journal of Intelligent Information Systems, 46, 563\u2013597.","journal-title":"Journal of Intelligent Information Systems"},{"key":"12007_CR54","unstructured":"Poduska, J. (2018). SHAP and LIME Python Libraries. Part 1 - Great Explainers, with Pros and Cons to Both. https:\/\/www.dominodatalab.com\/blog\/shap-lime-python-libraries-part-1-great-explainers-pros-cons"},{"key":"12007_CR55","unstructured":"Pojon, M. (2017). Using machine learning to predict student performance. Luonnontieteiden tiedekunta, Faculty of Natural Sciences."},{"key":"12007_CR56","unstructured":"Rade\u010di\u0107i, D. (2020, Nov 27). LIME: How to Interpret Machine Learning Models With Python. https:\/\/towardsdatascience.com\/lime-how-to-interpret-machine-learning-models-with-python-94b0e7e4432eadresindenalindi"},{"key":"12007_CR57","doi-asserted-by":"crossref","unstructured":"Rashu, R. I. (2014). Data mining approaches to predict final grade by overcoming class imbalance problem. 2014 17th International conference on computer and information technology (ICCIT) (s. 14--19). IEEE.","DOI":"10.1109\/ICCITechn.2014.7073095"},{"key":"12007_CR58","doi-asserted-by":"crossref","unstructured":"Ribeiro, M. T. (2016). Why should i trust you?\u201d\u00a0Explaining the predictions of any classifier. Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, (s. 1135\u20131144). San Francisco: ACM.","DOI":"10.1145\/2939672.2939778"},{"key":"12007_CR59","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1007\/s10729-012-9216-9","volume":"16","author":"YF Roumani","year":"2013","unstructured":"Roumani, Y. F. (2013). Classifying highly imbalanced ICU data. Health Care Management Science, 16, 119\u2013128.","journal-title":"Health Care Management Science"},{"key":"12007_CR60","doi-asserted-by":"publisher","first-page":"152688","DOI":"10.1109\/ACCESS.2021.3124270","volume":"9","author":"HA Sahlaoui","year":"2021","unstructured":"Sahlaoui, H. A. (2021). Predicting and Interpreting Student Performance Using Ensemble Models and Shapley Additive Explanations. IEEE Access, 9, 152688\u2013152703.","journal-title":"IEEE Access"},{"key":"12007_CR61","doi-asserted-by":"crossref","unstructured":"Sahlaoui, H. A. (2023). A Game Theoretic Framework for Interpretable Student Performance Model. International Conference on Networking, Intelligent Systems and Security (s. 21--34). Springer.","DOI":"10.1007\/978-3-031-15191-0_3"},{"key":"12007_CR62","doi-asserted-by":"publisher","first-page":"185","DOI":"10.1109\/TSMCA.2009.2029559","volume":"40","author":"CA Seiffert","year":"2009","unstructured":"Seiffert, C. A. (2009). RUSBoost: A hybrid approach to alleviating class imbalance. IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans, 40, 185\u2013197.","journal-title":"IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans"},{"key":"12007_CR63","doi-asserted-by":"publisher","first-page":"185","DOI":"10.1109\/TSMCA.2009.2029559","volume":"40","author":"K Seiffert","year":"2010","unstructured":"Seiffert, K., Seiffert, C., Khoshgoftaar, T. M., Van Hulse, J., & Napolitano, A. (2010). Rusboost: A hybrid approach to alleviating class imbalance. IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans, 40, 185\u2013197.","journal-title":"IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans"},{"key":"12007_CR64","unstructured":"Solanki, S. (2022). How to use LIME to interpret predictions of ML models? https:\/\/coderzcolumn.com\/tutorials\/machine-learning\/how-to-use-lime-to-understand-sklearn-models-predictions"},{"key":"12007_CR65","unstructured":"Straw, J. (2017). Building trust in machine learning models (using LIME in Python. https:\/\/www.analyticsvidhya.com\/blog\/2017\/06\/building-trust-in-machine-learning-models\/"},{"key":"12007_CR66","doi-asserted-by":"publisher","first-page":"290","DOI":"10.1080\/08839514.2021.1877481","volume":"35","author":"YA Sun","year":"2021","unstructured":"Sun, Y. A. (2021). Classifier selection and ensemble model for multi-class imbalance learning in education grants prediction. Applied Artificial Intelligence, 35, 290\u2013303.","journal-title":"Applied Artificial Intelligence"},{"key":"12007_CR67","doi-asserted-by":"publisher","first-page":"281","DOI":"10.1109\/TSMCB.2008.2002909","volume":"39","author":"YAQ Tang","year":"2008","unstructured":"Tang, Y. A. Q. (2008). SVMs modeling for highly imbalanced classification. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 39, 281\u2013288.","journal-title":"IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics)"},{"key":"12007_CR68","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1016\/j.eswa.2013.07.046","volume":"41","author":"DA Thammasiri","year":"2014","unstructured":"Thammasiri, D. A. (2014). A critical assessment of imbalanced class distribution problem: The case of predicting freshmen student attrition. Expert Systems with Applications, 41, 321\u2013330.","journal-title":"Expert Systems with Applications"},{"key":"12007_CR69","doi-asserted-by":"crossref","unstructured":"Van Hulse, J. A. (2007). Experimental perspectives on learning from imbalanced data. Proceedings of the 24th international conference on Machine learning, (s. 935\u2013942). New York: ACM.","DOI":"10.1145\/1273496.1273614"},{"key":"12007_CR70","doi-asserted-by":"crossref","unstructured":"Vultureanu-Albi\u015fi, A. A. (2021). Improving students\u2019 performance by interpretable explanations using ensemble tree-based approaches. 2021 IEEE 15th International Symposium on Applied Computational Intelligence and Informatics (SACI) (s. 215--220). IEEE.","DOI":"10.1109\/SACI51354.2021.9465558"},{"key":"12007_CR71","unstructured":"Wandera, H. A. (2020). Investigating similarities and differences between South African and Sierra Leonean school outcomes using Machine Learning. arXiv preprint arXiv:2004.11369."},{"key":"12007_CR72","doi-asserted-by":"publisher","first-page":"7","DOI":"10.1145\/1007730.1007734","volume":"6","author":"GM Weiss","year":"2004","unstructured":"Weiss, G. M. (2004). Mining with rarity: A unifying framework. ACM Sigkdd Explorations Newsletter, 6, 7\u201319.","journal-title":"ACM Sigkdd Explorations Newsletter"},{"key":"12007_CR73","doi-asserted-by":"crossref","unstructured":"Yap, B. W. (2014). An application of oversampling, undersampling, bagging and boosting in handling imbalanced datasets. Proceedings of the first international conference on advanced data and information engineering (DaEng-2013) (s. 13--22). Springer.","DOI":"10.1007\/978-981-4585-18-7_2"}],"container-title":["Education and Information Technologies"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10639-023-12007-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10639-023-12007-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10639-023-12007-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,4,1]],"date-time":"2024-04-01T13:05:04Z","timestamp":1711976704000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10639-023-12007-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7,17]]},"references-count":73,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2024,4]]}},"alternative-id":["12007"],"URL":"https:\/\/doi.org\/10.1007\/s10639-023-12007-w","relation":{},"ISSN":["1360-2357","1573-7608"],"issn-type":[{"value":"1360-2357","type":"print"},{"value":"1573-7608","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,7,17]]},"assertion":[{"value":"2 March 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 June 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 July 2023","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 declare that they have no conflicts of interest to report regarding the present study.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflicts of interest"}}]}}