The Effects of Learning Styles and Perceptions on Application of Interactive Learning Guides for Web-based Courses

Abstract

Background: As the great deal of courses provided online increases rapidly, it is crucial for instructors to identify specific learner characteristics of successful online students. An analysis of the relationship between learning styles, perception, interactive learning mode and course achievement will offer administrators with the vital information they need to prepare courses that cater to the needs of learners involved. Little research exists on how robot engineering students learn in different learning environments. Despite literature on the effectiveness of online instruction, little is known about the influence of learning styles and perceptions in online interactive learning. Purpose: This research investigated the effects of learning styles on learner perceptions of the use of interactive learning guides (ILGs) for web-based courses. This research reported on a study that compared an online group of juniors mechanical engineering majors with an equivalent on-campus group to found if their individual learning styles play a role in the selection of course delivery mode and in their academic achievement. Design/Method: The present research was undertaken using as subjects third-year robot engineering majors, 90 of which were enrolled in the online portion while the remaining 95 were enrolled in the face-to-face portion of 'Introduction to Robot Engineering'. Meanwhile, the Kolb Learning Styles Inventory (LSI), a statistically reliable and valid questionnaire, in which respondents attempted to depict their learning style, was administered online to student groups 1 week after the start of the course. The format of the LSI is a forced-choice format that ranks an individual's relative choice preferences among the four modes of the learning cycle: concrete experience (CE), reflective observation (RO), abstract conceptualization (AC), or active experimentation (AE). Combining the scores of the four learning modes and following the formulas [AC]-[CE] and [AE]-[RO] results in two combination scores. By sketching the combination scores a grid and identifying the quadrant where the two scores intersect, one can determine a specific learning style from among the four styles. Results: The author found no significant statistical differences were detected in learning styles and learning performance between the two groups. In addition, significant main effects for both gender and learning style, and gender and the perception of utility. The relationship between learning styles and gender was statistically significant. Conclusions: Based on the results of this research, there are several important issues indicated: (1) the online classroom, either as a sole instructional way or as a complement for on-campus learning, provides the tools to address different learning styles. Because learners in the F2F or online classroom do not differ in the way they process information, the same learning activities could be designed and utilized in both environments. (2) Instructors may want to consider providing ILGs in their campus-based courses as supplemental materials and make their use optional. These types of ILGs may also be helpful to online learners. Regardless of the learning which they are implemented, the design should be consistent and user-friendly. (3) Understanding their learning styles, students can effectively choose the tools that will add the most value to the learning experience.