Output list
Journal article
An integrative model of factors related to computing course performance
Published 1999
Journal of Educational Computing Research, 20, 3, 237 - 257
A path modeling approach is adopted to examine inter-relationships between factors influencing computing behavior and computing course performance. Factors considered are gender, personality, intellect and computing attitudes, ownership and experience. Among many other conclusions, intrinsic motivation is suggested as a major factor which can explain many variables' relationship with course performance. Similarly to the common finding for non-computing specialist students, a male advantage in previous computing experience is observed, gender differences in computer ownership partially explaining this. In the absence of an attitudinal gender difference, the ownership difference is suggested to stem from the perception of computers as objects stereotypically bought by and for males. However, while having implications with respect to the gender imbalance usually observed on programming-oriented and more technically-oriented applications courses, these differences are not shown to confer a male advantage in course performance.
Journal article
Published 1998
Journal of Educational Computing Research, 18, 2, 163 - 182
The characteristics of students taking programming-oriented and applications-oriented higher education courses are compared. Relative to the latter students, the former students' personalities are shown to be of a more schizoid nature, this providing an explanation of these students' greater computer engagement, programming experience and computing aptitude, at least as far as males are concerned. The extent to which programming experience is accumulated by females is concluded to be a major factor explaining the greater gender imbalance in enrolment on the programming-oriented course. Psychometric measures are found to be useful over and above cheaper, more easily obtainable, information in discriminating between the two types of student. However, psychometric measures are not found useful in increasing the association between correctness of course classification subsequent to Discriminant Function Analysis and success/failure on the courses. Finally, the same set of characteristics, involving among other things, greater involvement in computing, is found to be associated with success irrespective of course.