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constructs, student stress, student motivation and student demographics. Student

demographic variables used in this study included age, gender, number of similar courses

taken and college classification. Gender was the only categorical variable and was

dummy coded one for males and two for females. This data analysis procedure was

utilized to determine if dissimilar learning style measured by cognitive style gap can

explain student engagement. Classes were coded by letter according to the faculty

member's cognitive style score along the continuum of adaptiveness to innovativeness.

Therefore, the most adaptive faculty member was assigned the letter "A" and the letter

"I" was assigned to the most innovative faculty member.

Class A

 For Class A, backward stepwise regression was used to explain student

engagement. The best fitting model with the most explanation of the dependent variable

left five variables including sufficiency of originality cognitive style gap (P=-. 19), total

motivation (P=.25), total stress (P=.39), number of similar courses (P=.26) and college

classification (p=.30). In this model, the most important independent variable was total

stress, however the focus of the objective for this study was to examine the relationship

between cognitive style gap and student engagement. Students in Class A with an

innovative 5-point sufficiency of originality gap scored an average 0.80 points lower on

the measure of student engagement than students with no sufficiency of originality

cognitive style gap while controlling for motivation, stress, number of similar courses and

college classification. The total engagement measure had a range of 24 to 96, a scale of

72 points. Also, note that this faculty member was more adaptive. The data suggests that

in Class A, controlling for motivation, stress, number of similar courses and college

classification, students having a more innovative sufficiency of originality gap with the