Machine Learning Algorithms in Study of Student Performance Prediction in Virtual Learning Environment

Authors: Shilpa Patra, Pinaki Pratim Acharjya

Abstract:

One of the biggest challenges in education today is accurately forecasting student achievement. Identifying learners who require more support early on can have a big impact on their educational performance. Developing a theoretical framework that forecasts online learning outcomes for students in a virtual learning environment (VLE) using machine learning techniques is the aim of this study. Resolving the flaws in different forecasting models and increasing accuracy are major goals of the study.

Keywords: Virtual Learning Environments, K-Nearest Neighbors, KNN, Random Forest, Extra Trees.

References:

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