Detecting Learning Style through Biometric Technology for Mobile GBL

Detecting Learning Style through Biometric Technology for Mobile GBL

Tracey J. Mehigan (University College Cork, Ireland) and Ian Pitt (University College Cork, Ireland)
Copyright: © 2012 |Pages: 20
DOI: 10.4018/ijgbl.2012040104
OnDemand PDF Download:
$30.00
List Price: $37.50

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.
Article Preview

Learning-Styles

Learning-styles have become a consideration in the development of adaptive learning systems. Learning-style has been defined as “the generalized differences in learning orientation based on the degree to which people emphasize the four modes of the learning process” (Kolb, 1999, p. 41), “the ways in which individuals begin to concentrate on, process, internalize, and retain new and difficult information” (Dunn & Griggs, 2003, pp. 81-86), and the characteristic strengths and preferences in the ways individuals take in and process information (Felder & Silverman, 1988).

Learning-styles reflect students’ preferences for acquiring retrieving and retaining information for learning purposes. A number of models are available for this purpose including the Myer-Briggs Model (Pittenger, 1993), The Big Five Model (Busato et al., 1999), the Dunn and Dunn Model (Dunn & Griggs, 2003), Kolb’s Model (Kolb, 1999) and the Felder Silverman Model (Felder & Silverman, 1988).

It should be noted that the popularity of learning-styles in recent years has led to the questioning by many researchers of their existence, validity, reliability and benefits. Reviews have been published by both Coffield et al. (2004) and Pashler et al. (2009). Coffield et al. (2004) do not dismiss learning-styles overall and acknowledge the benefits of learning-styles including “self-awareness and metacognition” (Coffield et al., 2004, p. 132), “a lexicon of learning for dialogue” (Coffield et al., 2004, p. 78), “a catalyst for the individual, organizational or even systematic change” (Coffield et al., 2004, p. 134). The research conducted by Pashler et al. (2009) is based on “the claim that presentation should mesh with the learner’s own proclivities” (Pashler et al., 2009, p. 108). Pashler et al. (2009) note that learning-style questionnaires give repeatable results and that the instruments are measuring ‘something’ rather than producing random figures.

Complete Article List

Search this Journal:
Reset
Open Access Articles: Forthcoming
Volume 7: 4 Issues (2017)
Volume 6: 4 Issues (2016)
Volume 5: 4 Issues (2015)
Volume 4: 4 Issues (2014)
Volume 3: 4 Issues (2013)
Volume 2: 4 Issues (2012)
Volume 1: 4 Issues (2011)
View Complete Journal Contents Listing