Geodesic
Classification and forecasting in students' progress using Multiple-Criteria Decision Making, K-Nearest Neighbors, and Multilayer Perceptron methods
Computer Science and Information Systems, Tome 22 (2025) no. 3

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The research paper addresses students' performance in higher education. It proposes using the MCDM method - Promethee II to assess students' knowledge and the K-Nearest Neighbors (KNN) and Multilayer Perceptron (MLP) methods for grade classification. The main goals are tracking and diagnosing students' knowledge levels, predicting their outcomes, and providing tailored recommendations. It helps to identify students at risk of not passing the course and evaluates teaching methods. This encourages student engagement and progress during the course. The research demonstrates the suitability of Promethee II, MLP, and KNN methods for effectively monitoring, classifying, and predicting students' progress during the semester, enhancing the objectivity of the assessment process.
Keywords: Promethee II, MLP, KNN, student's grades mark classification, student's achievement forecasting, Matthews Correlation Coefficient, Class Balance Accuracy 
Slađana Spasić; Violeta Timašević. Classification and forecasting in students' progress using Multiple-Criteria Decision Making, K-Nearest Neighbors, and Multilayer Perceptron methods. Computer Science and Information Systems, Tome 22 (2025) no. 3. http://geodesic.mathdoc.fr/item/CSIS_2025_22_3_a10/
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     author = {Sla{\dj}ana Spasi\'c and Violeta Tima\v{s}evi\'c},
     title = {Classification and forecasting in students' progress using {Multiple-Criteria} {Decision} {Making,} {K-Nearest} {Neighbors,} and {Multilayer} {Perceptron} methods},
     journal = {Computer Science and Information Systems},
     year = {2025},
     volume = {22},
     number = {3},
     url = {http://geodesic.mathdoc.fr/item/CSIS_2025_22_3_a10/}
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