This paper aims to deal with the previously unknown prediction accuracy of students’ activity pattern in a blended learning environment.
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.
Experimental evaluation oppose the hypothesis that feature vector dimensionality reduction leads to prediction accuracy increasing.
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.
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.
