Research in teaching and learning space has shown that students tend to learn with their individual learning styles while teachers deliver lessons in class using appropriate teaching strategies that are best suited to the instructional goals and subject domains or teachers usually present material in a manner that suits their own individual teaching style or preference. Such teaching strategies may not match students’ diverse learning styles. Learning will be more effective if teachers can provide different teaching strategies that cater to students with different learning styles. The development of SMALT (SMart Advisor for Learning & Teaching) aims to systematically bridge the gap between learning styles and teaching strategies through dynamic alignment of the two in order to achieve optimal learning performance. Detecting students' individual learning styles is the essential step in the research towards attaining the goal of dynamic alignment. In order to avoid the psychometric flaws of the traditional explicit measuring instruments such as surveys or questionnaires, and also to enable continuous tracking and more accurate determination of learning styles, an implicit learning styles detection method based on Felder-Silverman Learning Style Model is proposed. Students’ learning preferences are established implicitly by analyzing their behavioural patterns when they interact with an online tutoring system. This paper will highlight the method of collecting and analyzing students’ behavioural patterns while interacting with the online tutoring system as well as the method of determining students’ learning styles using these behavioural pattern values.
Proceedings of the 9th International CDIO Conference, Massachusetts Institute of Technology and Harvard University School of Engineering and Applied Sciences, Cambridge, Massachusetts, June 9 – 13, 2013.