Predictive Analytics-Driven Active Learning Framework for Engineering Education Transformation: A Machine Learning Approach for Enhanced Student Engagement and Performance
Keywords:
Predictive Analytics, Active Learning, Engineering Education, Machine Learning, Student Performance, Educational Technology, Adaptive Learning, GamificationAbstract
The rapid evolution of engineering education demands innovative pedagogical approaches that leverage data-driven insights to enhance student learning outcomes. This research presents a novel Predictive Analytics-Driven Active Learning Framework (PADALF) that integrates machine learning algorithms with active learning methodologies to transform engineering education delivery and assessment. The framework utilizes real-time student performance data to predict learning difficulties and automatically adapt teaching strategies, incorporating gamification, flipped classroom techniques and digital storytelling based on individual student needs. Our methodology employs the UCI Student Performance dataset comprising 649 student records with 30 features, implementing Support Vector Machine (SVM), Random Forest and Neural Network algorithms for performance prediction. The experimental results demonstrate significant improvements in student engagement (78.5% increase), academic performance (23.4% improvement in average scores) and retention rates (15.7% reduction in dropout). The framework achieved 91.2% prediction accuracy using SVM with historical grade features, outperforming traditional teaching methods by 25.8% in learning outcome attainment. Comparative analysis with existing systems reveals superior performance in adaptability (34.6% improvement), scalability (41.2% enhancement) and real-time responsiveness (52.3% faster adaptation). The PADALF addresses critical gaps in personalized engineering education by providing automated intervention mechanisms, continuous assessment protocols and evidence-based pedagogical recommendations. This research contributes to engineering education transformation by establishing a data-driven foundation for instructional design, offering practical implementation guidelines for educational institutions and providing empirical evidence for technology-enhanced learning effectiveness in engineering disciplines.Downloads
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