article · IEEE Access
This research explores an efficient data mining technique to assess student satisfaction with online learning during the COVID-19 pandemic. It focuses on Educational Data Mining (EDM) and the critical role of Feature Selection (FS) in building accurate satisfaction models. A dataset was collected from student reviews on online courses via questionnaires. The study analysed the performance of 11 wrapper-based FS algorithms alongside k-Nearest Neighbor (k-NN) and Support Vector Machine (SVM) classifiers. The findings identified the optimal dimensionality for feature subsets and the most effective FS method, demonstrating that reducing features can significantly improve predictive accuracy. The approach achieved up to 80% feature size reduction and up to 100% classification accuracy on the dataset.
Understanding student satisfaction with online learning is vital for improving educational quality and adapting teaching methods. This research provides a more efficient and accurate way for educational organisations to identify key factors influencing student experience, enabling them to develop better support and learning environments.
This research could lead to the development of analytical tools for academic institutions to monitor and improve student satisfaction with online learning. Such tools, based on the identified efficient feature selection methods, could help administrators and educators derive constructive educational strategies. This appears to be applied research, tested with a real-time dataset, suggesting it is a step towards practical implementation for educational management systems.
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All the educational organizations mainly aim at elevating the academic performance of students for improving the overall quality of education. In this direction, Educational Data Mining (EDM) is a rapidly trending research area that utilizes the essence of Data Mining (DM) concepts to help academic institutions figure out useful information on the Student Satisfaction Level (SSL) with the Online Learning process (OL) during COVID-19 lock-down. Different practices have been tried with EDM to predict students’ behaviors to reach the best educational settings. Therefore, Feature Selection (FS) is typically employed to find the most relevant subset of features with minimum cardinality. As the predictive accuracy of a satisfaction model is significantly influenced by the FS process, the effectiveness of the SSL model is elaborately studied in this paper in connection with FS techniques. In this connection, a dataset was first collected online via a questionnaire of student reviews on OL courses. Using this datatset, the performance of wrapper FS techniques in DM and classification algorithms was analyzed in terms of fitness values. Ultimately, the goodness of subsets with different cardinalities is evaluated in terms of prediction accuracy and number of selected features by measuring the quality of 11 wrapper-based FS algorithms and the <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>-Nearest Neighbor (<inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>-NN) and Support Vector Machine (SVM) as base-line classifiers. Based on the experiments, the optimal dimensionality of the feature subset was revealed, as well as the best method. The findings of the present study evidently support the well-known conjunction of the existence of minimum number of features and an increase in predictive accuracy. It is remarkable the relevancy of FS for high-accuracy SSL prediction, as the relevant set of features can effectively assist in deriving constructive educational strategies. Our study contributes a feature size reduction of up to 80% along with up to 100% classification accuracy on the adopted real-time dataset.
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DOI: 10.1109/access.2022.3143035
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