Improving the prediction accuracy in blended learning environment using synthetic minority oversampling technique. Issue 2 (20th May 2019) - Dimic, Gabrijela; Rancic, Dejan; Macek, Nemanja; Spalevic, Petar; Drasute, Vida
Abstract : Purpose: This paper aims to deal with the previously unknown prediction accuracy of students' activity pattern in a blended learning environment. Design/methodology/approach: To extract the most relevant activity feature subset, different feature-selection methods were applied. For different cardinality subsets, classification models were used in the comparison. Findings: Experimental evaluation oppose the hypothesis that feature vector dimensionality reduction leads to prediction accuracy increasing. Research limitations/implications: Improving prediction accuracy in a described learning environment was based on applying synthetic minority oversampling technique, which had affected results on correlation-based feature-selection method. Originality/value: The major contribution of the research is the proposed methodology for selecting the optimal low-cardinal subset of students' activities and significant prediction accuracy improvement in a blended learning environment.