Predicting Students' Academic Performance in Virtual Learning Environment Using Machine Learning

The growth of numerous statistical approaches used to evaluate data in educational settings has caused machine learning to recently become a novel subject of research. In this chapter, authors present a novel voting model for performance prediction that incorporates machine learning techniques and additional variables known as “student sentiment attributes.” The proposed voting system was also employed to boost student test scores and improve the effectiveness of the strategies. In terms of the parameters of correlation coefficient, mean absolute error, root mean square error, time taken to build the model, relative absolute error, and root relative squared error, the supplied test set with voting method outperforms the four model evaluation methods of cross validation, use training set, supplied test set, and percentage split models. Given this, the result shows the applicability of the proposed model and computes the cost analysis of the proposed voting procedure.

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Predicting Students' Academic Performance in Virtual Learning Environment Using Machine Learning

Cited by 2 publications

References 14 publications

Enhancing the Early Prediction of Learners Performance in a Virtual Learning Environment

Enhancing the Early Prediction of Learners Performance in a Virtual Learning Environment

Categorising Students' Academic Performance and Sentiments Using Voting Machine Learning Techniques

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