Improving the prediction accuracy in blended learning environment using synthetic minority oversampling technique

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.

Citation

Dimic, G., Rancic, D., Macek, N., Spalevic, P. and Drasute, V. (2019), "Improving the prediction accuracy in blended learning environment using synthetic minority oversampling technique", Information Discovery and Delivery, Vol. 47 No. 2, pp. 76-83. https://doi.org/10.1108/IDD-08-2018-0036