{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T16:17:57Z","timestamp":1772122677435,"version":"3.50.1"},"reference-count":34,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2025,3,21]],"date-time":"2025-03-21T00:00:00Z","timestamp":1742515200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"University of Rijeka, Croatia","award":["uniri-iskusni-drustv-23-236"],"award-info":[{"award-number":["uniri-iskusni-drustv-23-236"]}]},{"name":"MDPI","award":["uniri-iskusni-drustv-23-236"],"award-info":[{"award-number":["uniri-iskusni-drustv-23-236"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Educational data mining (EDM) and learning analytics (LA) are widely applied to predict student performance, particularly in determining academic success or failure. This study presents the development of a scoring algorithm for the early identification of students at risk of failing science, technology, engineering, and mathematics (STEM) courses. The proposed approach follows a structured process: First, educational data are collected, processed, and statistically analyzed. Next, numerical variables are transformed into dichotomous predictors, and their relevance is assessed using Cram\u00e9r\u2019s V measure to quantify their association with course outcomes. The final step involves constructing a scoring system that dynamically evaluates student performance over 15 weeks of instruction. Prospective validation of the model demonstrated excellent predictive performance (accuracy = 0.93, sensitivity = 0.95, specificity = 0.92), confirming its effectiveness in early risk detection. The resulting scoring algorithm is distinguished by its methodological simplicity, ease of implementation, and adaptability to different educational settings, making it a practical tool for timely interventions.<\/jats:p>","DOI":"10.3390\/a18040177","type":"journal-article","created":{"date-parts":[[2025,3,21]],"date-time":"2025-03-21T04:58:38Z","timestamp":1742533118000},"page":"177","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["A Scoring Algorithm for the Early Prediction of Academic Risk in STEM Courses"],"prefix":"10.3390","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-8585-007X","authenticated-orcid":false,"given":"Vanja","family":"\u010coti\u0107 Poturi\u0107","sequence":"first","affiliation":[{"name":"Faculty of Informatics and Digital Technologies, University of Rijeka, 51000 Rijeka, Croatia"},{"name":"Faculty of Engineering, University of Rijeka, 51000 Rijeka, Croatia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1272-093X","authenticated-orcid":false,"given":"Sanja","family":"\u010candrli\u0107","sequence":"additional","affiliation":[{"name":"Faculty of Informatics and Digital Technologies, University of Rijeka, 51000 Rijeka, Croatia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1463-996X","authenticated-orcid":false,"given":"Ivan","family":"Dra\u017ei\u0107","sequence":"additional","affiliation":[{"name":"Faculty of Engineering, University of Rijeka, 51000 Rijeka, Croatia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,3,21]]},"reference":[{"key":"ref_1","unstructured":"Siemens, G., and Baker, R. (May, January 29). Learning analytics and educational data mining: Towards communication and collaboration. Proceedings of the 2nd International Conference on Learning Analytics and Knowledge, Vancouver, BC, Canada."},{"key":"ref_2","first-page":"98","article-title":"Educational data mining and learning analytics: Differences, similarities, and time evolution","volume":"12","year":"2015","journal-title":"Int. J. Educ. Technol. High. Educ."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"SoLAR, Lang, C., Siemens, G., Friend Wise, A., Ga\u0161evi\u0107, D., and Merceron, A. (2022). Predictive modelling in teaching and learning. Handbook of Learning Analytics, Nature Publishing Group. [2nd ed.].","DOI":"10.18608\/hla22.001"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"140731","DOI":"10.1109\/ACCESS.2021.3119596","article-title":"Prediction of Students\u2019 Academic Performance Based on Courses\u2019 Grades Using Deep Neural Networks","volume":"9","author":"Nabil","year":"2021","journal-title":"IEEE Access"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Fan, Y., John, S., Singh, S., Jovanovi\u0107, J., and Ga\u0161evi\u0107, D. (2021, January 12\u201316). A learning analytic approach to unveiling self-regulatory processes in learning tactics. Proceedings of the LAK21: 11th International Learning Analytics and Knowledge Conference, Irvine, CA, USA.","DOI":"10.1145\/3448139.3448211"},{"key":"ref_6","first-page":"301","article-title":"Evaluation of academic self-efficiency, community feeling, and academic achievement of students in the process of the covid-19 pandemic by data mining techniques","volume":"36","author":"Karabatak","year":"2024","journal-title":"F\u0131rat Univ. Muhendis. Bilim. Derg."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"661","DOI":"10.1002\/cae.22479","article-title":"Development of a student engagement score for online undergraduate engineering courses using learning management system interaction data","volume":"30","author":"Kittur","year":"2022","journal-title":"Comput. Appl. Eng. Educ."},{"key":"ref_8","first-page":"97","article-title":"Scale up predictive models for early detection of at-risk students: A feasibility study","volume":"12","author":"Cui","year":"2020","journal-title":"Inf. Learn. Sci."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Caicedo-Castro, I. (2023). Course Prophet: A System for Predicting Course Failures with Machine Learning: A Numerical Methods Case Study. Sustainability, 15.","DOI":"10.3390\/su151813950"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1186\/s40537-023-00835-z","article-title":"Designing and evaluating a big data analytics approach for predicting students\u2019 success factors","volume":"10","author":"Fahd","year":"2023","journal-title":"J. Big Data"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Wang, Y., and Joe, H. (2023). OISE: Optimized Input Sampling Explanation with a Saliency Map Based on the Black-Box Model. Appl. Sci., 13.","DOI":"10.3390\/app13105886"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1007\/s12559-023-10179-8","article-title":"Interpreting Black-Box Models: A Review on Explainable Artificial Intelligence","volume":"16","author":"Hassija","year":"2024","journal-title":"Cogn. Comput."},{"key":"ref_13","unstructured":"Doshi-Velez, F., and Kim, P. (2017). Towards A Rigorous Science of Interpretable Machine Learning. arXiv."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Ribeiro, M.T., Singh, S., and Guestrin, C. (2016, January 13\u201317). \u201cWhy Should I Trust You?\u201d: Explaining the Predictions of Any Classifier. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA.","DOI":"10.1145\/2939672.2939778"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"\u017dlahti\u010d, B. (2024). Transferring Black-Box Decision Making to a White-Box Model. Electronics, 13.","DOI":"10.3390\/electronics13101895"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"6476","DOI":"10.1073\/pnas.1916903117","article-title":"Active learning narrows achievement gaps for underrepresented students in undergraduate science, technology, engineering, and math","volume":"117","author":"Theobald","year":"2020","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_17","first-page":"336","article-title":"Developing engagement in the learning management system supported by learning analytics","volume":"42","author":"Hamid","year":"2022","journal-title":"Comput. Syst. Sci. Eng."},{"key":"ref_18","first-page":"91","article-title":"A predictive model implemented in KNIME based on learning analytics for timely decision making in virtual learning environments","volume":"12","year":"2022","journal-title":"Int. J. Inf. Educ. Technol."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1177\/2042753020909217","article-title":"Exploring lecturer and student perceptions and use of a learning management system in a postgraduate public health environment","volume":"17","author":"Kite","year":"2020","journal-title":"E-Learn. Digit. Media"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"364","DOI":"10.1038\/s41586-019-1466-y","article-title":"A National Experiment Reveals Where a Growth Mindset Improves Achievement","volume":"573","author":"Yeager","year":"2019","journal-title":"Nature"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1038\/s41539-021-00095-7","article-title":"First-Year Students\u2019 Math Anxiety Predicts STEM Avoidance and Underperformance Throughout University, Independently of Math Ability","volume":"6","author":"Daker","year":"2021","journal-title":"NPJ Sci. Learn."},{"key":"ref_22","unstructured":"Santos, R., and Henriques, R. (2023). Predicting Student Performance From Moodle Logs in Higher Education: A Course-Agnostic Approach. Education and New Developments, inScience Press."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1186\/s40561-019-0083-4","article-title":"Developing an Early-Warning System for Spotting at-Risk Students by Using eBook Interaction Logs","volume":"6","author":"Hasnine","year":"2019","journal-title":"Smart Learn. Environ."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"108","DOI":"10.33009\/fsop_jpss132082","article-title":"Predictive Identification of At-Risk Students: Using Learning Management System Data","volume":"2","author":"Osborne","year":"2023","journal-title":"J. Postsecond. Stud. Success"},{"key":"ref_25","first-page":"57","article-title":"Controlling attrition in blended courses by identifying students at risk: A case study on MS-Teams","volume":"11","author":"Zakopoulos","year":"2021","journal-title":"Int. J. Financ. Insur. Risk Manag."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"858","DOI":"10.12669\/pjms.35.3.217","article-title":"Relationship between admission criteria and academic performance: A correlational study in nursing students","volume":"35","author":"Yousafzai","year":"2019","journal-title":"Pak. J. Med. Sci."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"120","DOI":"10.22581\/muet1982.2301.12","article-title":"Quality enhancement at higher education institutions by early identifying students at risk using data mining","volume":"42","author":"Mahboob","year":"2023","journal-title":"Mehran Univ. Res. J. Eng. Technol."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Sandoval-Palis, I., Naranjo, D., Vidal, J., and Gilar-Corbi, R. (2020). Early dropout prediction model: A case study of university leveling course students. Sustainability, 12.","DOI":"10.3390\/su12229314"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3449086","article-title":"Characterizing student engagement moods for dropout prediction in question pool websites","volume":"5","author":"Mogavi","year":"2020","journal-title":"Proc. ACM Hum.-Comput. Interact."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Won, H.S., Kim, M.J., Kim, D., Kim, H.S., and Kim, K.M. (2023). University student dropout prediction using pretrained language models. Appl. Sci., 13.","DOI":"10.3390\/app13127073"},{"key":"ref_31","unstructured":"Ribi\u0107, G. (2016). Motivacija za Studij kod u\u010dEnika Srednjih \u0161Kola: Uloga Osnovnih Psiholo\u0161kih Potreba i Identiteta, Diplomski Rad. [Ph.D. Thesis, University of Rijeka]."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1002\/1097-0142(1950)3:1<32::AID-CNCR2820030106>3.0.CO;2-3","article-title":"Index for rating diagnostic tests","volume":"3","author":"Youden","year":"1950","journal-title":"Cancer"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1080\/14786440009463897","article-title":"On the criterion that a given system of deviations from the probable in the case of a correlated system of variables is such that it can be reasonably supposed to have arisen from random sampling","volume":"50","author":"Pearson","year":"1900","journal-title":"Lond. Edinb. Dublin Philos. Mag. J. Sci."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Cram\u00e9r, H. (1946). Mathematical Methods of Statistics, Princeton University Press.","DOI":"10.1515\/9781400883868"}],"container-title":["Algorithms"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-4893\/18\/4\/177\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T16:57:39Z","timestamp":1760029059000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-4893\/18\/4\/177"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3,21]]},"references-count":34,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2025,4]]}},"alternative-id":["a18040177"],"URL":"https:\/\/doi.org\/10.3390\/a18040177","relation":{},"ISSN":["1999-4893"],"issn-type":[{"value":"1999-4893","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,3,21]]}}}