A Note on Performance and Satisfaction of Female Students Studying Computer Science

Ivanović, Mirjana; Putnik, Zoran; Sisarica, Anja; Budimac, Zoran

doi:10.11120/ital.2010.09010032

Cited by 4 publications

(2 citation statements)

References 14 publications

(35 reference statements)

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“…Other studies have contradicted these findings. In Serbia, one study (Mirjana, Zoran, Anja, & Zoran, 2010) found female students had a high level of confidence in computer science. Confidence is an important component in developing a computer identity which is imperative for women to persist in computer science (Lehman et al, 2016).…”

Section: Self-efficacymentioning

confidence: 99%

“…Other countries, such as Australia (Zagami et al, 2015) and Greece (Wagner, 2016), are also experiencing a shortage of women in the field. However, it is important to note that countries like Armenia and other republics of the former Soviet Union do not report gender inequity in computer science (Gharibyan & Gunsaulus, 2006;Mirjana et al, 2010). In countries like Taiwan, more women successfully complete computer science courses, but these women do not end up working as programmers (Michell, et al, 2017).…”

Section: Cultural Changementioning

confidence: 99%

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Eliminating gender inequity : why women choose computer science

Ypma¹

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This doctoral thesis explores the experiences of six women in an introductory computer science course at a small rural community college. Limited research exists that explores why women choose to enroll in computer science and this study helps to fill this void. This study found that stereotypes associated with computer science still exist. It also revealed that for some women studying computer science, stereotypical perceptions may be changing. Experiences in the classroom showed that women were frustrated being one of just a handful of women in the class and groupwork was problematic. Group dynamics allowed sexism to emerge as an issue; yet, the women were reluctant to identify the behaviors they experienced as sexism. The study used Interpretative Phenomenological Analysis to evaluate stereotype threat, specifically the Multi-Threat Framework was used to categorize the threats that emerged. To my knowledge, this appears to be the first study to use this framework to examine gender inequity in computer science.

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Section: Self-efficacymentioning

confidence: 99%