Volume 4, Issue 2 (6-2012)                   IJICTR 2012, 4(2): 11-26 | Back to browse issues page

XML Print


Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

Montazer G A, Khoshniat H. E-Learners’ Activity Categorization Based on Their Learning Styles Using ART Family Neural Network . IJICTR. 2012; 4 (2) :11-26
URL: http://ijict.itrc.ac.ir/article-1-184-en.html
1- Associate Professor, School of Engineering Tarbiat Modares University Tehran, Iran
2- MSc student, School of Engineering Tarbiat Modares University Tehran, Iran
Abstract:   (2115 Views)

Adaptive learning means providing the most appropriate learning materials and strategies considering students' characteristics. Grouping students based on their learning styles is one of the approaches which has been followed in this area. In this paper, we introduce a mechanism in which learners are divided into some categories according to their behavioral factors and interactions with the system in order to adopt the most appropriate recommendations. In the proposed approach, learners' grouping is done using ART neural network variants including Fuzzy ART, ART 2A, ART 2A-C and ART 2A-E. The clustering task is performed considering some features of learner's behavior chosen based on their learning style. Additionally, these networks identifythe number of students' categories according to the similarities among their actions during the learning processautomatically. Having employed mentioned methods in a web-based educational system and analyzed their clustering accuracy and performance, we achieved remarkable outcomes as presented in this paper.

Full-Text [PDF 876 kb]   (914 Downloads)    
Type of Study: Research | Subject: Information Technology

Add your comments about this article : Your username or Email:
CAPTCHA code