Predicting the Impact of Emotional Intelligence on Academic Performance Using Machine Learning Models

Predicting the Impact of Emotional Intelligence on Academic Performance Using Machine Learning ModelsThis research presents an innovative and interdisciplinary approach to understanding the impact of Emotional Intelligence (EI) on academic performance through the integration of Transfer Learning, Machine Learning, and Educational Data Mining techniques. The study develops a comprehensive framework that analyzes emotional, behavioral, and academic factors, including self-awareness, self-management, social awareness, relationship management, attendance, participation, and stress levels, to predict student success more accurately. A synthetic dataset comprising 1,750 student records was created to simulate real-world educational environments and evaluate the effectiveness of various machine learning models. By leveraging pre-trained deep learning models such as ResNet and InceptionV3, the proposed transfer learning framework successfully identifies emotional patterns and incorporates them into academic performance prediction models. The experimental results reveal a strong positive correlation between emotional intelligence and academic achievement, demonstrating that students with higher EI exhibit better engagement, resilience, learning efficiency, and overall academic outcomes. Furthermore, clustering and classification techniques effectively identify high performing students, balanced learners, and academically at risk groups, enabling targeted interventions and personalized support strategies. The study highlights the potential of emotionally intelligent AI systems to transform traditional educational practices by providing real-time insights, adaptive learning experiences, and data driven decision making support for educators, administrators, and policymakers. Ultimately, the research establishes that integrating emotional intelligence analytics with advanced machine learning methodologies can significantly enhance personalized learning, improve student well being, promote academic excellence, and contribute to the development of sustainable and emotionally responsive educational ecosystems.