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Article
Publication date: 28 February 2019

Gabrijela Dimic, Dejan Rancic, Nemanja Macek, Petar Spalevic and Vida Drasute

This paper aims to deal with the previously unknown prediction accuracy of students’ activity pattern in a blended learning environment.

220

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.

Details

Information Discovery and Delivery, vol. 47 no. 2
Type: Research Article
ISSN: 2398-6247

Keywords

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Article
Publication date: 6 June 2019

Xu Du, Jui-Long Hung and Chih-Hsiung Tu

387

Abstract

Details

Information Discovery and Delivery, vol. 47 no. 2
Type: Research Article
ISSN: 2398-6247

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