Predictive Model to Analyse Real and Synthetic Data for Learners' Performance Prediction Using Regression Techniques

Predicting learner performance with precision is critical within educational systems, offering a basis for tailored interventions and instruction. The advent of big data analytics presents an opportunity to employ Machine Learning (ML) techniques to this end. Real-world data availability is often hampered by privacy concerns, prompting a shift towards synthetic data generation. This study presents an empirical comparison of real, synthetic, and mixed (real + synthetic) data sets in forecasting learner performance, deploying an array of regression-based ML algorithms, including Random Forest, Gradient Boosting, XG Boost, K-nearest Neighbor, and Support Vector Regression. Our methodology encompasses the generation of synthetic data via generative model, followed by the application of these algorithms to each data set. The models are evaluated using precision metrics to assess their predictive accuracy. The study unveils that synthetic data can rival real data in predictive capabilities, with combined data sets achieving up to 87.76% accuracy, underscoring the efficacy of hybrid data approaches. These insights advocate for the integration of synthetic data as a practical substitute in scenarios with limited access to real data, fostering advancements in educational technology and ML.