An empirical study on evolutionary feature selection in intelligent tutors for learning concept detection. Issue 3 (16th April 2018)

• Record Type: Journal Article
• Authors: Gunel, Korhan; Erdogdu, Kazim; Polat, Refet; Ozarslan, Yasin
• Other Names: Rocha Álvaro guestEditor.; Anwar Sajid guestEditor.
• Is Part Of: Expert systems. Volume 36:Issue 3(2019)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2018-04-16
• Subjects: ant colony optimization -- artificial intelligence in educational technology -- concept map mining -- evolutionary computation -- feature selection -- genetic algorithm -- particle swarm optimization; Expert systems (Computer science); 006.33
• DOI: 10.1111/exsy.12278
• Languages: English
• ISSNs: 0266-4720

Abstract

Abstract: Concept map mining (CMM) has emerged as a new research area with recent developments in computational intelligence in educational technology. CMM includes the following steps: extracting the learning concepts from educational content, specifying relations among them, and generating a concept map as a result. The purpose of this study was to develop a mechanism using data mining technique to determine the features that characterize a learning concept extracted automatically from a single educational text. The 3 major features that distinguish the real learning concepts from other sequences of strings are detected by using a hybrid system of a feed‐forward neural network and some evolutionary algorithms. Ant colony optimization and genetic algorithm and particle swarm optimization are used as a binary feature selection method. In addition, the aforementioned methods are hybridized to get better accuracy and precision. The performance comparisons with two different state‐of‐the‐art algorithms have been made from the viewpoint of a typical classification problem.