EXPLAINABLE ML MODEL FOR ROBOTICS COURSE SUCCESS PREDICTION
Keywords:
Explainable AI, Machine Learning, Student Success Prediction, Robotics Education, SHAP, LIME, Interpretability, Education Technology, Personalized LearningAbstract
The increasing complexity of robotics education calls for an effective mechanism to predict student performance and course success. This study presents an explainable machine learning (ML) model for predicting the success of students in robotics courses. The model incorporates a variety of features, including prior knowledge in mathematics and programming, time spent on practical exercises, participation in group projects, and engagement with course materials. By leveraging explainable AI techniques, the model not only predicts student outcomes but also provides interpretable insights into the factors influencing those predictions. The results demonstrate the model's capability to predict student success with a high degree of accuracy, while also offering valuable feedback to educators for improving course design and student support strategies.
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