An Improved Feature Selection Approach for Prediction of Students’ Academic Performance in a Virtual Learning Environment

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dc.contributor.authorFelicia Ojiyovwi ADELODUN
dc.date.accessioned2025-06-05T13:34:34Z
dc.date.available2025-06-05T13:34:34Z
dc.date.issued2024-12
dc.description.abstractIn the past, only machine learning algorithms were used for predicting Students’ Academic Performance. In recent times both feature selection methods and machine learning algorithms are important in the prediction process. In previous research, the focus has been on demographic information. Research specifically analyzing video interaction of learners is limited. This study provides an opportunity to investigate the interactions of learners in a Virtual Learning Environment (VLE), The study further investigated for clarification whether or not if Feature Selection should be skipped during the prediction process as some previous studies suggested. The study proposed a novel model named PF-PSO as an improved Feature Selection (FS) method comprising of a combination of three existing feature selection methods to improve machine learning models’ accuracies in predicting students’ academic performance in a VLE. The closen feature selection methods are Principal Component Analysis (PCA), Forward Selection Method (FOR) and Particle Swarm Optimization (PSO). Students’ educational datasets were retrieved from secondary sources such as Kaggle.com. This unbiased study used two approaches- with FS and without FS to train machine learning models. The evaluation metrics include MSE, R2 and MAE for the regression tasks. Accuracy, Precision, and F1 measure for the classification tasks. The results from the study showed that while PSO proved promising, the proposed system achieved great success with Random Forest and Gradient Boosting performing very well in both regression and classification tasks and could explain 65% to 89% variance in the target variable. Logistic Regression as best for classification tasks with accuracy in the range of 61% and 75%. The proposed system can contribute to enhancing students’ academic prediction. The findings of the study show the importance of incorporating a hybrid feature selection for predicting students’ academic performance. Keywords: Feature Selection approach for Pearson Correlation Coefficient, Forward Selection method, Particle Swarm Optimization, Prediction of , Students’ Academic Performance, Virtual Learning Environment Word Counts: 301
dc.identifier.citationKate Turabia
dc.identifier.otherP.hD
dc.identifier.urihttps://repository.lcu.edu.ng/handle/123456789/859
dc.language.isoen
dc.publisherLead City University, Ibadan
dc.relation.ispartofseriesP.hD
dc.subjectFeature Selection approach for Pearson Correlation Coefficient
dc.subjectForward Selection method
dc.subjectParticle Swarm Optimization
dc.subjectPrediction of
dc.subjectStudents’ Academic Performance
dc.subjectVirtual Learning Environment
dc.titleAn Improved Feature Selection Approach for Prediction of Students’ Academic Performance in a Virtual Learning Environment
dc.typeThesis

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