A Scoring Algorithm for the Early Prediction of Academic Risk in STEM Courses
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, techno...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-03-01
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| Series: | Algorithms |
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| Online Access: | https://www.mdpi.com/1999-4893/18/4/177 |
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| author | Vanja Čotić Poturić Sanja Čandrlić Ivan Dražić |
| author_facet | Vanja Čotić Poturić Sanja Čandrlić Ivan Dražić |
| author_sort | Vanja Čotić Poturić |
| collection | DOAJ |
| description | 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 Cramér’s 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. |
| format | Article |
| id | doaj-art-8aecbdc1eacc4e75b5a5eb457bdfeb20 |
| institution | OA Journals |
| issn | 1999-4893 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Algorithms |
| spelling | doaj-art-8aecbdc1eacc4e75b5a5eb457bdfeb202025-08-20T02:17:14ZengMDPI AGAlgorithms1999-48932025-03-0118417710.3390/a18040177A Scoring Algorithm for the Early Prediction of Academic Risk in STEM CoursesVanja Čotić Poturić0Sanja Čandrlić1Ivan Dražić2Faculty of Informatics and Digital Technologies, University of Rijeka, 51000 Rijeka, CroatiaFaculty of Informatics and Digital Technologies, University of Rijeka, 51000 Rijeka, CroatiaFaculty of Engineering, University of Rijeka, 51000 Rijeka, CroatiaEducational 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 Cramér’s 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.https://www.mdpi.com/1999-4893/18/4/177educational data mininglearning analyticsacademic risk predictionscoring algorithmCramér’s Vearly intervention |
| spellingShingle | Vanja Čotić Poturić Sanja Čandrlić Ivan Dražić A Scoring Algorithm for the Early Prediction of Academic Risk in STEM Courses Algorithms educational data mining learning analytics academic risk prediction scoring algorithm Cramér’s V early intervention |
| title | A Scoring Algorithm for the Early Prediction of Academic Risk in STEM Courses |
| title_full | A Scoring Algorithm for the Early Prediction of Academic Risk in STEM Courses |
| title_fullStr | A Scoring Algorithm for the Early Prediction of Academic Risk in STEM Courses |
| title_full_unstemmed | A Scoring Algorithm for the Early Prediction of Academic Risk in STEM Courses |
| title_short | A Scoring Algorithm for the Early Prediction of Academic Risk in STEM Courses |
| title_sort | scoring algorithm for the early prediction of academic risk in stem courses |
| topic | educational data mining learning analytics academic risk prediction scoring algorithm Cramér’s V early intervention |
| url | https://www.mdpi.com/1999-4893/18/4/177 |
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