Analyzing student online learning behaviors and academic performance in science education using machine learning techniques

HIGHLIGHTS

• What: This study investigates the factors influencing student engagement and performance in online science education through the application of machine learning models specifically Random Forests Decision Trees and Support Vector Machines (SVM). The study reveals that integrating machine_learning can address traditional assessment challenges, enabling more nuanced evaluations of student performance and understanding in scientific disciplines. The target variable, or dependent variable, is the outcome that the model aims to predict or classify. In this study, the three selected machine_learning models underwent 5-fold cross-validation, and their average accuracy on the validation set was calculated.
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