Welcome to the InfoSci Platform
Reference Hub9
Detecting Learning Style through Biometric Technology for Mobile GBL

Detecting Learning Style through Biometric Technology for Mobile GBL

Tracey J. Mehigan, Ian Pitt
Copyright: © 2012 |Volume: 2 |Issue: 2 |Pages: 20
ISSN: 2155-6849|EISSN: 2155-6857|EISBN13: 9781466612204|DOI: 10.4018/ijgbl.2012040104
Cite Article Cite Article

MLA

Mehigan, Tracey J., and Ian Pitt. "Detecting Learning Style through Biometric Technology for Mobile GBL." IJGBL vol.2, no.2 2012: pp.55-74. http://doi.org/10.4018/ijgbl.2012040104

APA

Mehigan, T. J. & Pitt, I. (2012). Detecting Learning Style through Biometric Technology for Mobile GBL. International Journal of Game-Based Learning (IJGBL), 2(2), 55-74. http://doi.org/10.4018/ijgbl.2012040104

Chicago

Mehigan, Tracey J., and Ian Pitt. "Detecting Learning Style through Biometric Technology for Mobile GBL," International Journal of Game-Based Learning (IJGBL) 2, no.2: 55-74. http://doi.org/10.4018/ijgbl.2012040104

Export Reference

Mendeley
Favorite Full-Issue Download

Abstract

Adaptive learning systems tailor content delivery to meet specific needs of the individual for improved learning-outcomes. Learning-styles and personalities are usually determined through the completion of questionnaires. There are a number of models available for this purpose including the Myer-Briggs Model (MBTI), the Big Five Model, and the Felder Silverman Learning-Style Model (FSLSM). Most models classify the student on a number of scales. Recently, a number of studies have investigated the possibility of determining an individual’s learning-style directly through their interaction patterns when using a system. Automatic learning-style detection could play a significant role in the advancement of educational gaming through personalized learning environments. Biometric devices, such as accelerometers and eye-trackers, are now available for use with mobile devices. These provide an opportunity to move toward adaptive mobile gaming environments, giving potential to track learning-styles directly through avatar movement. This paper examines mobile learning (mLearning) with an emphasis on mobile game-based environments. Adaptive learning systems are introduced. The results of studies conducted to assess the potential of biometric devices as a means of automatically detecting students’ learning-styles are discussed. The potential of this research for mobile game-based learning is outlined.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.