Abstract:

Instructional design and learning styles play critical roles that influence student retention and success in web-based learning. If instruction and learning resources compliment learning styles, learners will feel contented and learning will be more effective. To improve the learning progress of students and to make learning easier for them, learning styles are considered more and more in technology enhanced learning systems. Examples for systems that incorporate learning styles and provide courses that fit to the learning style of the students are based on the Adaptive Hypermedia Architecture (AHA) and the Adaptive Educational Hypermedia Systems. This approach for adaptation in technology driven learning has significantly improved learning process by adapting course content presentation to user learning styles and has been implemented in the Adaptive E-Learning Hypermedia System based on Learning Styles system (AEHS-LS).

The investigations about learning styles and the development of adaptive systems are motivated by learning style models which state that learners have different ways they prefer to learn. In this study we explore learning styles, achievements and behaviors in VLE for engineering students in order to make comparison between used learning objects and learning styles to achieve easier adaptation of Virtual Learning Enviroment (VLE). The focus of our research is on students in engineering that have strong preferences to specific learning styles, since such student generally have more problems to adapt to learning strategy which is inconsistent with their learning style. As a result, we provide more detailed information about characteristics of the learning styles for students with strong preferences. Such detailed information about learning styles improves student modeling and helps to provide more suitable adaptive learning systems that aim to incorporating learning styles for providing courses that fit to the individual learning styles of students.

Keywords:

VLE, learning styles, adaptive learning systems.