Academic journal article
By Ng, Pin; Pinto, James; Williams, Susan K.
Academy of Educational Leadership Journal , Vol. 15, No. 1
As faculty, one of our goals is to provide the best possible learning environment for our students. In order to create an ideal learning environment, it is important to understand our students' different learning styles (Coffield, Moseley, Hall, & Ecclestone, 2004; Dunn, Griggs, Gorman, & Beasley, 1995). Students "preferentially focus on different types of information, tend to operate on perceived information in different ways, and achieve understanding at different rates" (Felder, 1993, p. 286). Acknowledging that students have different learning styles then behooves the instructor to utilize a variety of teaching strategies in order to engage students of all learning styles (Buxeda & Moore, 1999). All students will then have opportunities to use their preferred learning style and opportunities to improve their less-preferred learning style (Hawk & Shah, 2007). Having incorporated a variety of learner-centered and integrative teaching strategies into a business statistics course, we wanted to know if a student's learning-style had an effect on their course performance.
It is important to design a course that allows students of all learning styles to succeed. As noted by Felder, "Students whose learning styles are compatible with the teaching style of a course instructor tend to retain information longer, apply it more effectively, and have more positive post-course attitudes toward the subject than do their counterparts who experience learning/teaching style mismatches," (Felder, 1993, p. 286). If the results of our study had shown a relationship between the overall course score and a student's learning style, then additional components could have been designed into the course or students could have been appropriately advised about how best to adapt to the teaching style that does not match their preferred learning style (Campbell, 1991; Coffield, et al. 2004).
To accomplish this analysis, we collected student performance data on the various components of the course (quizzes, exams and projects), attributes of student learning styles, achievement on pre- and post-assessment, and attendance in the course. We analyzed the data using ordinary least squares regression analysis and quantile regression (Koenker & Bassett, 1978). Quantile regression allowed investigation of a more complete picture of student performance over the entire student population distribution. For example, a least squares regression analysis for course score with learning styles as the independent variables estimated the mean effect of learning styles on course performance. Quantile regression, however, provided information about the performance of, for example, the lower performing 25% of the class. The significant factors that affected performance for the lower 25% could have been different from the significant factors that affected the performance for the top performing 25% and this difference could only be discovered using quantile regression. Thus, quantile regression provided information about the entire distribution of course performance that the ordinary least squares regression did not provide.
We found that learning style was insignificant in determining a student's overall course score for the entire group of students. This provided some evidence that the design of the course did not favor students with any particular learning style. For small cohorts of students, learning styles were statistically significant in determining exam average. That is, some students experienced either a disadvantage or advantage by their learning style for the exam course component, as elaborated in more detail in the Results section. However, for the overall course performance, a student with a particular learning style was neither advantaged nor disadvantaged.
The next section provides, in brief, a survey of existing literature on learning styles, a description of our interpretative learner-centered business statistic course and an introduction to quantile regression. …