This paper covers a systematic literature review of documented interventions aimed at increasing gender diversity in the computer science field, focusing on primary and secondary education. Despite historical contributions by women in computing, gender stereotypes and misconceptions continue to persist, resulting in significant underrepresentation of females in this domain. Using the SALSA framework, the study builds an analysis of various educational interventions, such as camps, workshops, and hands-on activities, that successfully enhanced female participation in computer science. Key strategies identified include innovative teaching and instruction methods through the use of mentorship and female role models. The review highlights a positive impact on participating girls' self-esteem and community belonging that increases the rates of interest in computer science. The findings provide a comprehensive view of the successful approaches, suggesting that targeted educational practices can effectively mitigate gender disparities. Therefore, the computer science field continuously needs effort to create an inclusive environment for female primary and secondary school students.

The underrepresentation of women in computer science (CS) programs is an important issue that needs to be addressed in order to promote gender inclusivity and innovation. The following study is a literature review of 42 papers published between 2005 and 2024. It investigates the effectiveness of different higher education policies and practices in retaining female students in computer science programs. Key areas of the paper include holistic admissions and bridge programs, anti-discrimination policies, support programs, and inclusive curriculum design, such as introducing practical context to education and adopting inclusive learning techniques. The research findings show that all these strategies enhance female retention in CS, by, for example, creating a sense of belongingness, increasing motivation and engagement, removing economic barriers, providing networking and career opportunities. A key takeaway of the paper is that each strategy has its own unique contributions and combining different policies strengthens and complements individual effects. Because of that, any party interested in applying these policies needs to carefully study the educational environment and then choose the most adequate set of measures.

This study evaluates the gender inclusivity of selection materials for computer science programs at TU Delft, TU Eindhoven, and Vrije Universiteit Amsterdam. By scrutinizing wording, imagery, and pronoun use, we determine the supportiveness and potential biases of these materials. While all three universities employ gender-neutral language and second-person pronouns, TU Delft stands out for its balanced gender representation in imagery and extensive preparatory resources using a normalized scoring system. In contrast, TU Eindhoven and Vrije Universiteit Amsterdam show greater gender imbalance in visuals and rely on absolute scores, potentially introducing bias. Future research should include non-binary gender representations and extend to more European universities to develop a comprehensive understanding of gender inclusivity in academic selection processes.

The underrepresentation of women in Computer Science remains a significant issue. Stereotypes portraying Computer Science as a male-dominated field, populated by people who prefer solitude and have an obsessive interest in technology, are known to deter women from pursuing and staying in Computer Science careers. These stereotypes are often perpetuated by educational materials, further discouraging female participation.
This study investigates the extent to which course materials from two introductory Computer Science courses at Delft University of Technology, 'CSE1300: Reasoning and Logic' and 'CSE1500: Web and Database Technology', reflect these documented stereotypes. By analyzing characters and scenarios depicted in textbooks, lecture slides, and videos, the research assesses whether these materials reinforce gender biases.
The methodology involves a comprehensive inventory and classification of characters based on gender, social interactions, and interests. The findings suggest that both courses frequently employ stereotypical representations, especially making use of male examples, potentially contributing to the persistence of gender biases in Computer Science education. Addressing these issues by promoting diverse and inclusive examples in educational content could be a step toward increasing female retention in Computer Science programs.

The field of Computer Science is male-dominated. This means that there is a large lack of female representation. This causes women to be less interested in CS and have a lower sense of belonging. This lower sense of belonging does not only increase the already existing gender gap, it also has an impact on women’s performances in CS. A lack of women in education causes a lack of women in the professional field. Education therefore plays an important part in decreasing the gender gap. This study aims to investigate potential gender biases in assignments of first-year courses for Computer Science and Engineering at the TU Delft. This study analyzes assignments from two courses based on four heuristics: The gender of characters and the way they are portrayed, gender-neutral pronouns, Whether assignments relate to People or Things, and The opportunities for collaboration and competitiveness. This paper shows that both courses suffer from a lack of story-based assignments. This lack of story-based assignments causes a lack of social relevance as well as a lack of characters and personal pronouns. Both courses allow for collaboration. Despite allowing collaboration, Computer Organization also has competitive elements in its assignments. This paper recommends that Computer Science courses use more story-based assignments.

The goal of this thesis is expanding quantum algorithm datasets to enhance our capability to benchmark quantum systems and to open up possibilities for using machine learning techniques in quantum circuit mapping. Both of these areas are currently hindered by the lack of a wide range of useful quantum algorithms. To solve this problem, KetGPT is presented, a model that uses the revolutionary transformer machine learning architecture to generate synthetic, yet realistic looking, quantum circuits. By visual inspection, KetGPT generated circuits are easily distinguishable from random circuits, and show desirable qualities such as structure and human-like programming factors including applying gates in the order of ascending qubits. Consequently, they might be more suitable for certain tasks like benchmarking and training a reinforcement learning compiler. In an attempt to quantify the quality of circuits generated by KetGPT, a separate transformer classifier model was trained on the task of classifying the synthetic circuits generated by KetGPT as either real circuits, or as random circuits. However, although this classifier might capture realistic features of quantum circuits, the classifier has not been unambiguously proven to be reliable, and can therefore not be used as a standalone tool to determine the quality of KetGPT generated quantum circuits. Nevertheless, KetGPT and the transformer classifier are novel, promising approaches in an attempt to expand quantum algorithm datasets.