Improving the prediction accuracy in blended learning environment using synthetic minority oversampling technique. Issue 2 (20th May 2019)

• Record Type: Journal Article
• Authors: Dimic, Gabrijela; Rancic, Dejan; Macek, Nemanja; Spalevic, Petar; Drasute, Vida
• Is Part Of: Information discovery and delivery. Volume 47:Issue 2(2019)
• Page Start: 76
• Page End: 83
• Publication Date: 2019-05-20
• Subjects: Machine learning -- Educational data mining -- Naïve Bayes classifier -- Learning management system -- Feature selection -- Oversampling technique; Information retrieval -- Periodicals; Document delivery -- Periodicals; Digital libraries -- Periodicals; Information storage and retrieval systems -- Periodicals; 025.524
• DOI: 10.1108/IDD-08-2018-0036
• Languages: English
• ISSNs: 2398-6247

Abstract

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.