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The work applies CRISP-DM to educational machine learning, extracting knowledge and features from a dataset of 6,690 records and 21 academic and socioeconomic variables. Preprocessing and classification are evaluated via AUC and accuracy; XGBoost reaches AUC 87.75%. 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Predicting Academic Success of College Students Using Machine Learning Techniques. Data 2024, 9, 60. [https://doi.org/](https://doi.org/)[ ](https://doi.org/)[10.3390/data9040060](10.3390/data9040060)  \nAcademic Editor: Antonio Sarasa Cabezuelo  \nReceived: 31 January 2024  \nRevised: 13 April 2024  \nAccepted: 15 April 2024  \nPublished: 22 April 2024  \nCopyright: © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \n1 Facultad de Ciencias de la Ingeniería, Universidad Técnica Estatal de Quevedo, Quevedo 120508, Ecuador; [jorgeguanin@uteq.edu.ec](jorgeguanin@uteq.edu.ec)  \n2 Facultad de Ingeniería, Pontificia Universidad Católica del Ecuador, Quito 170525, Ecuador  \n3 Department of Computer Science and Artificial Intelligence, University of Granada, 18071 Granada, Spain; [casillas@decsai.ugr.es](casillas@decsai.ugr.es)  \n* Correspondence: [eguana953@puce.edu.ec](eguana953@puce.edu.ec); Tel.: +593-995000484  \nAbstract: College context and academic performance are important determinants of academic success; using students’ 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.  \nKeywords: educational data mining; machine learning; educational analysis; higher education; academic success  \n1. Introduction  \nHigher education has developed a fundamental role due to the versatility and complexity of today’s world, which has led to the rapid growth of scientific literature dedicated to predicting academic success or the risk of student dropout [1–7] . Higher education institutions and their traditional role of knowledge dissemination have changed; innovation in new knowledge especially with the irruption of artificial intelligence [8] and the training of qualified professionals make many of them interact in different areas of society. In fact, their missions through teaching, research, and the ability to share and transfer this knowledge constitute central functions of their academic and cultural activity, with the aim of improving the level of knowledge in society. They have the important role of transmitting knowledge, skills, and values to students to create competitive professionalsin society. Therefore, channeling students towards academic success is transcendental, as HEIs must c","cbCaifZvLXu8zSsE","https://ap.wps.com/l/cbCaifZvLXu8zSsE","pdf",1918932,27,"English","# Introduction\n## Educational context and motivation\n## Role of machine learning and predictive modeling\n# Methods and evaluation\n## CRISP-DM and feature extraction\n## Preprocessing and classification algorithms\n## Effectiveness metrics (AUC)","[{\"question\":\"What dataset and variables are used to predict academic success?\",\"answer\":\"The study uses 6,690 records with 21 variables covering academic and socioeconomic information.\"},{\"question\":\"Which machine learning approaches are compared and how is effectiveness measured?\",\"answer\":\"Preprocessing and classification algorithms are analyzed, and effectiveness is measured using AUC; XGBoost achieves AUC 87.75%.\"},{\"question\":\"What limitations does the study identify for the proposed algorithms?\",\"answer\":\"The main limitation is assessing fairness and discrimination of the algorithms.\"}]","Predicting Academic Success of College Students Using Machine Learning Techniques - research report | PDF",68]