Enhancing educational evaluation through predictive student assessment modeling

Abstract

This study evaluates several machine learning models used in predicting student performance. The data utilized in this study was collected from 253 undergraduate students participating in five classes within one of three courses offered by VnCodelab, an interactive learning management system, to provide insights into student performance. Leveraging the data-rich environment of the interactive learning management system proposed earlier, this study focuses on training a predictive model that forecasts student grades based on the comprehensive data collected during the teaching process. The proposed model capitalizes on the data obtained from students' engagement patterns, time spent on exercises, and progress tracking across learning activities. This study compared five different base classifiers— Random Forest (RF), Logistic Regression (LR), Support Vector Machine (SVM), Naïve Bayes (NB), and k-nearest Neighbor (k-NN), and an ensemble learning method Stacking Classifier —utilizing a dataset comprising 13 features. The research assesses the model's accuracy, reliability, and implications, contributing to the evolution of educational evaluation by introducing predictive assessment as a transformative tool. The results indicate that the Stacking Classifier accurately predicts students' grade ranges, surpassing individual base classification models by effectively combining their predictive capabilities. Integrating data-driven forecasting into the educational ecosystem can transform teaching methodologies and foster an informed, engaged, and empowered learning environment. This approach cultivates a proactive learning community by empowering students with real-time academic progress forecasts. Educators benefit from data-informed insights that facilitate more effective and objective performance evaluation.