Preprint Article Version 1 This version is not peer-reviewed

Gender Gap in Computer Science: Preferences and Performance

Version 1 : Received: 22 December 2017 / Approved: 26 December 2017 / Online: 26 December 2017 (02:30:21 CET)

How to cite: Berdousis, I.; Kordaki, M. Gender Gap in Computer Science: Preferences and Performance. Preprints 2017, 2017120180 (doi: 10.20944/preprints201712.0180.v1). Berdousis, I.; Kordaki, M. Gender Gap in Computer Science: Preferences and Performance. Preprints 2017, 2017120180 (doi: 10.20944/preprints201712.0180.v1).

Abstract

The aim of the present study is to investigate both the performance and preferences of males and females Computer Science (CS) graduates. In order to attain the above goal, a quantitative case study was conducted regarding 89 degrees, acquired from 2006 to 2012, from the Department of Computer Science and Technology, University of Peloponnese, Greece. The analysis of the data revealed that in terms of performance, no significant differences between the mean grades of males and females exist, in almost most of the courses included in the curriculum of the aforementioned CS department. Any statistically significant differences in performances were present in almost equal number of courses in favor of males and females. It seems also, that females performed better in the courses they selected more than males. Regarding preferences, in CS courses, it seems that gender differences are existent. Males preferred more than females did core programming courses and advanced topics of Software Systems, computer networks, computer engineering, robotics and mathematics, whereas females preferred more the study of algorithms and security issues, computer fractals, data management, computer architecture, and mobile communication. In addition, females preferred courses in reference with humanities and social sciences, CS terminology, and career opportunities. Yet, females did not select any of programming lab-based courses, computer engineering, computer network issues and robotics.

Subject Areas

computer science; tertiary education; course selection; performance; gender gap

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