Gender influences on school students' mental models of computer science

Brauner, Philipp; Ziefle, Martina; Schroeder, Ulrik; Leonhardt, Thiemo; Bergner, Nadine; Ziegler, Birgit

doi:10.1145/3196839.3196857

Cited by 13 publications

(21 citation statements)

References 26 publications

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“…When it comes to gender, children and technology, many contributions have been made regarding building initiatives to promote gender equality in: Computer Science (CS) and Science, Technology, Engineering and Mathematics (STEM) education [81], Coding [33] [78] and Making activities [59]. The underestimation of the bias in CS and STEM education and career paths has also been investigated extensively [18]. In particular, researchers raised the issue of girls' decreased interest in Information and Communication Technology (ICT) in middle and high school [83] when key decisions about future career paths happen.…”

Section: Gender Computing and Design Of Digital Technologymentioning

confidence: 99%

“…In the following sections, we report the specific approach taken in each phase, the procedure and the main findings. DST is a way for children to mediate the world's construction of meaning [18], including gender identity [56], hence, the process of creating a story and the related outcome reflect the child's gender-related belief [81].…”

Section: Research Approachmentioning

confidence: 99%

See 1 more Smart Citation

Raising Awareness of Stereotyping Through Collaborative Digital Storytelling: Design for Change with and for Children

Rubegni

Landoni

Malinverni

et al. 2022

International Journal of Human-Computer Studies

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Section: Gender Computing and Design Of Digital Technologymentioning

confidence: 99%

Section: Research Approachmentioning

confidence: 99%

Raising Awareness of Stereotyping Through Collaborative Digital Storytelling: Design for Change with and for Children

Rubegni

Landoni

Malinverni

et al. 2022

International Journal of Human-Computer Studies

View full text Add to dashboard Cite

“…Encouraging and supporting girls’ self-eﬃcacy can be achieved by “academic advising, faculty mentorship, tutoring, internship opportunities, and career and skill development” (ibid.). After finding that girls reported significantly lower levels of technical self-eﬃcacy and lower interest in computer science than boys, Brauner et al (2018) offered several guidelines to adjust children’s mental model, including inviting guests from STEM disciplines into the classrooms, organizing Girls’ Days, inviting boys and girls into universities for research internships and launching events that help boys and girls understand computing principles and increase their technical self-eﬃcacy.…”

Section: Related Workmentioning

confidence: 99%

Using social media advertisement data to monitor the gender gap in STEM: opportunities and challenges

Tamime¹,

Weber²

2022

PeerJ Computer Science

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Boosting the number of women and girls entering careers involving STEM (Science, Technology, Engineering and Maths) is crucial to achieving gender equality, one of the UN Sustainable Development Goals. Girls and women tend to gravitate away from STEM fields at multiple stages from childhood through mid-career. The leaky pipeline is a metaphor often used to describe the loss of women in STEM and arguably other fields before reaching senior roles. Do interests expressed on social media mirror the leaky pipeline phenomenon? In this article, we collected advertisement data (reach estimates) from Facebook and Instagram disaggregated by US metros, age, gender, and interests related to STEM. We computed the Gender Gap Index (GGI) for each US metro and age group. We found that on Instagram, the GGIs for interest in Science decrease as users’ age increases, suggesting that relatively there is evidence that that women, compared to men, are losing interest in STEM at older ages. In particular, we find that on Instagram, there are plausible relative trends but implausible absolute levels. Nevertheless, is this enough to conclude that online data available from Instagram mirror the leaky pipeline phenomenon? To scrutinize this, we compared the GGIs for an interest in Science with the GGIs for placebo interests unrelated to STEM. We found that the GGIs for placebo interests follow similar age patterns as the GGIs for the interest in Science across US metros. Second, we attempted to control for the time spent on the platform by computing a usage intensity gender ratio based on the difference between daily and monthly active users. This analysis showed that the usage intensity gender ratio is higher among teenagers (13–17 years) than other older age groups, suggesting that teenage girls are more engaged on the platform that teenage boys. We hypothesize that usage intensity differences, rather than inherent interest changes, might create the illusion of a leaky pipeline. Despite the previously demonstrated value and huge potential of social media advertisement data to study social phenomena, we conclude that there is little evidence that this novel data source can measure the decline in interest in STEM for young women in the USA.

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“…z. B. Beyer, 2008;Watt et al, 2013;Milesi et al, 2017;Brauner et al, 2018;Völkel et al, 2018, Starr, 2018Rosser, 2018;Pröbster et al, 2018). Sicherlich sind Studien mit einer größeren Datenbasis von Vorteil, da aufgrund kleinerer Konfidenzintervalle eine präzisere Schätzung möglich ist und eine höhere Effektstärke in den Analysen zu erwarten ist (Bortz & Döring, 2006, S. 29, 602-604).…”

Section: Erklärung Von Geschlechterunterschieden Am Beispiel Der Informatikunclassified

“…B. Cook & Cambell, 1979;Prein et al 1994, S. 15;Jacob et al, 2011, S. 75 Beyer, 2008;Watt et al, 2013;Milesi et al, 2017;Brauner et al, 2018;Völkel et al, 2018, Starr, 2018Rosser, 2018, Pröbster et al, 2018 (Prein et al, 1994). Das heißt, bei kleinen Stichproben ist die Wahrscheinlichkeit alle relevanten Gruppenunterschiede zu beobachten geringer als in großen Stichproben (vgl.…”

Section: Erklärung Von Geschlechterunterschieden Am Beispiel Der Informatikunclassified

"Wie groß ist der kleine Unterschied?" Eine quantitative Untersuchung von Geschlechterdifferenzen im Studium und im Beruf der Informatik am Beispiel der Bamberger Alumnae Tracking-Studie

Förtsch¹

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„Wie groß ist der kleine Unterschied?“ Diese Frage, angelehnt an ein bekanntes geflügeltes Wort, leitet diese Dissertation. Die Arbeit nimmt dabei einen sehr spezifischen Ausbildungs- und Berufszweig in den Blick, nämlich die Informatik. Im Zentrum dieser Arbeit steht die Frage nach Geschlechterdisparitäten in der Informatik. Dabei wird das Fähigkeitskonzept und die Karriereambitionen von Studierenden und Berufstätigen im Bereich der Informatik empirisch untersucht. In einem weiteren Schritt wird analysiert wie Leistungen und das Fähigkeitskonzept Karrierechancen beeinflussen. Grundlage der Analysen sind die Primärdaten der Bamberger Alumnae Tracking-Studie, welche sowohl Bamberger Informatikstudierende als auch Bamberger Informatikabsolventinnen und -absolventen von 2013 bis 2015 in mehreren Reihen befragt hat. In ihrer Themenstellung und ihrem Erkenntnisinteresse bewegt sich die vorliegende Arbeit im Grenzbereich zwischen der soziologisch und der psychologisch orientierten empirischen Bildungsforschung.

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Gender influences on school students' mental models of computer science

Cited by 13 publications

References 26 publications

Raising Awareness of Stereotyping Through Collaborative Digital Storytelling: Design for Change with and for Children

Raising Awareness of Stereotyping Through Collaborative Digital Storytelling: Design for Change with and for Children

Using social media advertisement data to monitor the gender gap in STEM: opportunities and challenges

"Wie groß ist der kleine Unterschied?" Eine quantitative Untersuchung von Geschlechterdifferenzen im Studium und im Beruf der Informatik am Beispiel der Bamberger Alumnae Tracking-Studie

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