Gender issue in the ICT field in higher education B. Divjak, V. Vidaček-Hainš & M. Ostroški - Varaždin County - Faculty of Organization and Informatics Varaždin, University of Zagreb VIIth Conference of the European Regions on Equal Opportunities Timisoara (Timis, Romania), October 11-12, 2007

Population aged 15 or more according to the level of education (%): (Croatian Bureau of Statistics – 2001 Census)

Varaždin County (Croatian Bureau of Statistics – 2001 Census)

Croatian Employment Service Unemployment rate by gender July 2007: Only 4.2% of unemployed persons have a HE degree Most of them are aged over 50 (28.7 %) Male Female

Proportion of female full professors Source: National Science Foundation (2003). In Beyond Bias and Barriers: Fulfilling the Potential of Women in Academic Science and Engineering, pp 17. National Academy of Sciences, N.A. of Engineering and Institute of Medicine. National Academy Press, Washington D.C.

Percentage of female and male doctoral scientists (Computer Science) Source: National Science Foundation (2003). In Beyond Bias and Barriers: Fulfilling the Potential of Women in Academic Science and Engineering, pp 171. National Academy of Sciences, N.A. of Engineering and Institute of Medicine. National Academy Press, Washington D.C. Legend: 1= percentage of female d.s. ; 2= percentage of male d.s.

Croatia: Doctors of science – technical sciences Female students (20,3%) Male students (79,8%)

Gender budgeting Tools and processes designed to facilitate a gender analysis in the formulation of government budgets and the allocation of resources European Women’s Lobby – more than 3000 women’s associations in Europe

Our research – the goals To define connections between the social and economic status and secondary education as pre-entry factors determining results achieved in higher education (HE) Information and Communications Technology (ICT)

Respondents N = 271 first-year students of Information Systems in the field of Organization and Computer Sciences Male Female Part-time Full-time

Measurement (Survey)

Results Full-time vs. part-time students: Average number of learning hours per week Legend: Y axis = average number of hours ● Full time students achieved better results Male vs. Female students: ● Female students study 1.46 h more than male students; female students – more motivated to fulfill their tasks than male students (r= , p>0.05): Pearson correlation between gender and motivation

Results Statist. sign. variables: Not significant variables: Size of residence Availability of the Internet Type of secondary school (r=0.219, p>o.o5) Gender differences (t=3,255; df=128;p>0.01) Female students learn more Very important: curriculum and pre-knowledge from secondary school Full-time students vs. Part-time (full-time learn more)

Conclusion Pedagogical implications of the research: The need to familiarize the students with the aims and outcomes of courses The redefined role of the teacher in ICT supported learning environment Meta-skills – “learning to learn” strategies

Prerequisites for teachers 1. Objective insight into students’ behavior 2. Adapting the learning strategies to students / teachers 3. Quality communication

Thank you!