Detecting Learning Style through Biometric Technology for Mobile GBL

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. DOI: 10.4018/ijgbl.2012040104 56 International Journal of Game-Based Learning, 2(2), 55-74, April-June 2012 Copyright © 2012, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. Spada et al. (2008) assessed the potential for using a student’s mouse movement patterns as a means of gathering user-data for the detection of learning-styles on the FSLSM (Felder Silverman Learning Style Model). Spada et al. (2008) were able to predict, with a high level of accuracy, a user’s learning-style on the Global / Sequential dimension of the model. Global learners generally acquire data in large leaps as opposed to Sequential learners who tend to learn in small steps. They achieved a correlation coefficient of r=-0.8 between the user’s score on the FSILS (Felder Solomon Index of Learning Style) and their vertical speed when interacting with the system. As we move toward the development of intelligent adaptive systems for use in mobile learning environments, an alternative to the mouse is required. Biometric technologies such as accelerometers and eye-trackers can potentially provide this alternative. The term biometrics is derived from the Greek words ‘bios’ and ‘metric’ which directly translate as life measurement. Biometrics can be broken into two main categories, physical biometrics and behavioral biometrics. The main focus of this research is behavioral biometrics, which has mainly been used to date for verification purposes. Biometrics in this regard are generally concerned with measuring the characteristics an individual acquires naturally over a time, and are usually based on the measurement of patterns (e.g., typing rhythm or gait).