Gender differences in learning styles: Nurturing a gender and style sensitive computer science classroom

Abstract

The gender digital divide has been widely discussed and researched over the years. Previous studies have focused on a number of factors such as computer attitude, computer anxiety, computer self-efficacy, and computer experience. This study empirically tested the sensitivity of a learning style instrument, the Gregorc Style Delineator (GSD), to gender in a sample of students who studied computer programming in Hong Kong secondary schools. Results indicated that females had higher preference for concrete sequential (CS) and abstract random (AR) compared with males. Males had higher preference for concrete random (CR) than females. From these results, we proposed learning style-based pedagogical practices to teach computer programming. In order to address the needs of female students, we further proposed that learning style-based pedagogical practices need to move from a gender neutral to gender sensitive approach. This proposal helps to improve student learning through individualisation in pedagogy in terms of gender and learning styles and contributes to a gender and style sensitive computer science classroom. Eventually, through various remedial interventions, we anticipate more female participation in computer science field and hence the gender issue is addressed.