A Critical Discourse Analysis of Gender Representation in Software Engineering Education Videos on YouTube

• URL: http://arxiv.org/abs/2606.18423v1
• Date: Tue, 16 Jun 2026 19:18:37 GMT
• Title: A Critical Discourse Analysis of Gender Representation in Software Engineering Education Videos on YouTube
• Abstract summary: This study presents a critical discourse analysis of 200 manually analysed English and German software engineering tutorials on YouTube.<n>Our results show that male characters and masculine linguistic defaults dominate the tutorials.<n>We identified an agency gap, in which technical and decision-making roles are almost exclusively assigned to male actors.
• Score: 54.52098098905923
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Educational resources may frame students' perceptions of who belongs in software engineering, which is relevant given the field's ongoing gender gap. However, we know little about the hidden curriculum regarding gender in online learning spaces. This study presents a critical discourse analysis of 200 manually analysed English and German software engineering tutorials on YouTube, examining gender representation through contextual domains and linguistic identity markers. Our results show that male characters and masculine linguistic defaults dominate the tutorials. We identified an agency gap, in which technical and decision-making roles are almost exclusively assigned to male actors, while female actors are either absent or tend to passive, low-agency roles. The findings indicate that software engineering education on YouTube may reproduce gendered norms, in which linguistic and representational gatekeeping may serve as a symbolic barrier to software engineering.

Related papers

• Dynamics of Gender Bias in Software Engineering [0.0]
  The field of software engineering is embedded in both engineering and computer science, and may embody gender biases endemic to both.<n>This paper surveys software engineering's origins and its long-running attention to engineering professionalism, profiling five leaders.<n>It then examines the field's recent attention to gender issues and gender bias.
  arXiv  Detail & Related papers  (2025-08-28T17:54:49Z)
• Gender Dynamics in Software Engineering: Insights from Research on Concurrency Bug Reproduction [0.5284425534494986]
  We present a literature review to assess the gender ratio in this field.<n>Our findings indicate that female researchers are underrepresented compared to their male counterparts in this area.
  arXiv  Detail & Related papers  (2025-02-27T17:15:23Z)
• Gender Bias in Text-to-Video Generation Models: A case study of Sora [63.064204206220936]
  This study investigates the presence of gender bias in OpenAI's Sora, a text-to-video generation model.<n>We uncover significant evidence of bias by analyzing the generated videos from a diverse set of gender-neutral and stereotypical prompts.
  arXiv  Detail & Related papers  (2024-12-30T18:08:13Z)
• Beyond Binary Gender: Evaluating Gender-Inclusive Machine Translation with Ambiguous Attitude Words [85.48043537327258]
  Existing machine translation gender bias evaluations are primarily focused on male and female genders. This study presents a benchmark AmbGIMT (Gender-Inclusive Machine Translation with Ambiguous attitude words) We propose a novel process to evaluate gender bias based on the Emotional Attitude Score (EAS), which is used to quantify ambiguous attitude words.
  arXiv  Detail & Related papers  (2024-07-23T08:13:51Z)
• Breaking Barriers: Investigating the Sense of Belonging Among Women and Non-Binary Students in Software Engineering [1.9075820340282934]
  Women are far less likely to pursue a career in the software engineering industry. Reasons for women and other underrepresented minorities to leave the industry are a lack of opportunities for growth and advancement. This research explores how the potential to cultivate or uphold an industry unfavourable to women and non-binary individuals in software engineering education.
  arXiv  Detail & Related papers  (2024-05-06T20:07:45Z)
• VisoGender: A dataset for benchmarking gender bias in image-text pronoun resolution [80.57383975987676]
  VisoGender is a novel dataset for benchmarking gender bias in vision-language models. We focus on occupation-related biases within a hegemonic system of binary gender, inspired by Winograd and Winogender schemas. We benchmark several state-of-the-art vision-language models and find that they demonstrate bias in resolving binary gender in complex scenes.
  arXiv  Detail & Related papers  (2023-06-21T17:59:51Z)
• Keywords for Bias [0.10499611180329804]
  We are using several NLP approaches and compare them based on their capability of detecting keywords to analyse bias. The overall findings show that our proposed approach gives comparable results with the state-of-the-art approaches on different benchmark datasets.
  arXiv  Detail & Related papers  (2022-10-31T18:19:04Z)
• They, Them, Theirs: Rewriting with Gender-Neutral English [56.14842450974887]
  We perform a case study on the singular they, a common way to promote gender inclusion in English. We show how a model can be trained to produce gender-neutral English with 1% word error rate with no human-labeled data.
  arXiv  Detail & Related papers  (2021-02-12T21:47:48Z)
• Mitigating Gender Bias in Machine Learning Data Sets [5.075506385456811]
  Gender bias has been identified in the context of employment advertising and recruitment tools. This paper proposes a framework for the identification of gender bias in training data for machine learning.
  arXiv  Detail & Related papers  (2020-05-14T12:06:02Z)
• Multi-Dimensional Gender Bias Classification [67.65551687580552]
  Machine learning models can inadvertently learn socially undesirable patterns when training on gender biased text. We propose a general framework that decomposes gender bias in text along several pragmatic and semantic dimensions. Using this fine-grained framework, we automatically annotate eight large scale datasets with gender information.
  arXiv  Detail & Related papers  (2020-05-01T21:23:20Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.