Behsi, ZeynepDereli, SerhanÇakar, TunaPatel, Jay NimishCicek, GultekinDrias, Yassine2025-10-052025-10-0520259798331566555https://doi.org/10.1109/SIU66497.2025.11112134https://hdl.handle.net/20.500.11779/3102Isik UniversityThis study develops a machine learning model integrating survey data and performance metrics to predict student success in the UpSchool education program. Students' personality traits assessed by DISC analysis, financial management, social, and emotional skills were clustered into "Successful,""Unsuccessful,"and "Moderately Successful"groups using K-means clustering. The SMOTE technique addressed data imbalance issues, and algorithms such as Logistic Regression, Random Forest, LightGBM, and AdaBoost were tested. After hyperparameter optimization, AdaBoost and LightGBM achieved the highest predictive performance. Results demonstrated the effectiveness of machine learning models in forecasting student success in educational programs. Future studies are recommended to enhance model performance through expanded datasets and to validate the model's applicability across diverse educational contexts. © 2025 Elsevier B.V., All rights reserved.trinfo:eu-repo/semantics/closedAccessAdaboostK-Means ClusteringLightGBMSmoteStudent Success PredictionEducation ComputingLearning SystemsLogistic RegressionMachine LearningStudentsCase-StudiesEducational ProgramK-Means++ ClusteringLightgbmMachine Learning ModelsMachine-LearningSMOTEStudent SuccessSurvey DataForecastingConference Object10.1109/SIU66497.2025.111121342-s2.0-105015390707