Sarah Jane Delany

The persistent gender imbalance in computing remains a global concern, and universities offer a key part of the pipeline to address it. Although research has identified practices that support under-represented student groups, translating this evidence into actionable guidance remains challenging. This paper first presents a novel web‑based toolkit (TechMate) designed to address this gap by helping computing educators implement gender‑inclusive initiatives through practical research-informed guidance. The toolkit defines over 25 actions, ranging from operational to strategic, and provides case studies and implementation resources. Second, this work reports on the evaluation of TechMate, capturing educators’ first impressions of its usefulness and usability through authentic tasks, and eliciting unanticipated insights about structural barriers to gender-inclusive practice in computing higher education. Eighteen computing educators of varying seniority from eight universities in Ireland assessed the toolkit using nine research‑driven attributes: four usefulness measures (novelty, relevance, trustworthiness, actionability) and five usability measures (effectiveness, aesthetics, navigation, terminology, user satisfaction). Data was analysed using mixed methods: deductive coding, quantitative sentiment scoring, and inductive thematic analysis. Educators rated TechMate highly for user satisfaction and praised its novelty and trustworthiness, with over 60% of participants reporting positively on all attributes, suggesting that research‑informed tools can be designed for real‑world use. Thematic analysis highlighted other challenges, including women students’ isolation, institutional resistance to change, and the scarcity of women role models, while also surfacing proposed solutions such as leveraging student ambassadors and fostering supportive learning environments. This work contributes to the broader question of how open-access digital tools can support equitable participation in computing, with implications for design and governance in education and social good contexts.

We report on a trans-Atlantic PhD student mobility program that connects two graduate research training initiatives in the US and Ireland, centered on developing future researchers in artificial intelligence (AI) and machine learning (ML). We discuss both the structure of the student exchange experiences and share key learnings from this international collaboration. The most important lesson learned is that providing a structured mobility program and matched visiting pairs is a highly effective way to improve learning outcomes compared to more typical ad-hoc individual visits.

Shweta Soundararajan, Sarah Jane Delany. Proceedings of the 6th Workshop on Gender Bias in Natural Language Processing (GeBNLP). 2025.

Manuela Jeyaraj, Sarah Delany. Proceedings of the 5th Workshop on Gender Bias in Natural Language Processing (GeBNLP). 2024.

Nasim Sobhani, Sarah Delany. Proceedings of the 5th Workshop on Gender Bias in Natural Language Processing (GeBNLP). 2024.

Many initiatives across the world try to address the issue of gender imbalance in computing education. Evaluation of the impact of these initiatives is not always straightforward. Some interventions are able to demonstrate impact based on recruitment and retention numbers of underrepresented gender groups, in particular, women, often requiring a longer-term study to see impact. Others, especially shorter term interventions and those taking place during the teaching and learning process, frequently use feedback and other instruments as a measure of impact. This work reviews existing evaluation methods from the literature, categorises them as using statistical data, feedback or instruments, grouping different approaches into sub categories. It then looks at how the different types of initiatives map to the categories of evaluation practices, discusses trends, and makes a recommendation suitable for measuring the impact on shorter term than obtaining recruitment or retention statistics. The Perceived Stress Scale is proposed as a lightweight and time saving approach to evaluate some of the retention based gender initiatives.