An Efficient Data Mining Technique for Assessing Satisfaction Level With Online Learning for Higher Education Students During the COVID-19

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 k-Nearest Neighbor (k-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.