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.

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 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.

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.

The advancement of artificial intelligence (AI) has led to an increased demand for both a greater volume and quality of data. In many companies, data is dispersed across multiple tables, yet AI models typically require data in a single table format. This necessitates the merging of these tables and the selection of optimal features for the model, a process known as Tabular Data Augmentation (TDA). With the rapid growth of TDA, automated tools have been developed to streamline this process. However, these state-of-the-art tools often make assumptions about user workflows that may not align with the actual needs of data specialists, potentially making them efficient yet not fully user-friendly. Additionally, without thorough evaluation through user studies, these tools may overlook critical steps in the TDA process.

This thesis is divided into two main parts. The first part is dedicated to uncovering the assumptions and oversights within current TDA research through an exhaustive review of recent literature. This is followed by conducting interviews with 19 data specialists. These discussions aim to verify the identified assumptions and reveal any missing elements in state-of-the-art research. The second part focuses on creating a new tool to meet the requirements identified from validated assumptions and the gaps discovered. This tool is then subjected to evaluation interviews to assess its effectiveness.

The findings indicate that data specialists prefer a TDA tool that offers enhanced control and deeper insights into the data augmentation process. To meet these preferences, Human in the Loop AutoTDA was developed, embodying the desired functionalities. Feedback from the evaluation phase confirmed that data specialists find Human in the Loop AutoTDA suitable for their TDA workflows, marking a significant advancement in the field.

Scratch is a block-based programming language. It is designed to be simple and syntax error avoidant. This makes Scratch an accessible platform for cultivating coding skills. Many young learners are taught about different programming skills using various project types as examples. For instance, games are used as an engagement tool, and various games can motivate new learners to make their own. This influx of new published projects are manually classified into different types by using tags in the project descriptions. However, this manual classification only happens when the user adds the appropriate tags. This calls for an extension of that feature, which could help classify all projects published on the Scratch website. This has the goal of improving the browsing process, especially for new projects. To address this gap, in this initial iteration we leverage the fact that there are likely similarities, or even various project type defining features that would help improve the accuracy of classification through machine learning. Filling this gap also opens the possibility of automatic classification, depending on the accuracy of the results. Within this study, various machine learning models were tested with quantifiable project features as input data. The accuracy scores were compared to draw conclusions on how well various features extracted from Scratch projects performed for classification.

Although existing work demonstrates that the usage of Scratch strengthens programming skills, little is known about the effect of children encouraging or criticizing each other when programming on the development of their programming skills. To address this gap, we conducted a data analysis to reveal the correlation (or lack thereof) between the sentiment of comments on a Scratch project and the creator's inclination to continue producing projects.

In particular, using a known dataset of Scratch projects, which we enriched with new projects, we examined several independent variables that capture sentiment on comments from different perspectives, e.g. the absolute or relative number of comments with a particular sentiment score that a user receives, or the ratio of comments that fall in a certain category. We also examined several dependent variables that capture the creator's inclination to continue producing projects, e.g. the number of projects they create or the total time that they are active on the platform.

The results of our experiments reveal that the absolute and relative number of comments with a specific sentiment score given to a user have a low correlation to the time that a user is active on the platform. Additionally, the ratio of the number of positive and negative comments over the total number of comments given to a user has a low correlation to the time that a user is active on the platform. Finally, the number of comments that a user receives has a low correlation to the number of projects a user creates.

Scratch is a popular, visual programming language aimed at children, and is used by teachers and after school code clubs to teach their students about programming. Measuring whether they understand the underlying concepts, however, is a difficult task. In this research, we tried clustering Scratch projects by complexity to help students improve their programming skills. We did this by selecting an existing data set to extract features that indicate code complexity. Before, researchers attempted clustering on one metric that globalises the project’s complexity. Different researchers set out to measure the growth of the students by clustering the projects the students created.
With that in mind, we adopt a partition-based clustering algorithm to cluster the projects, as this method indicates outliers. We examine the quality of these clusters using the silhouette coefficient. We set up five experiments with different input vectors to make out the impact each input has on the clusters. We did not find a clear indication of the projects being clustered by the selected features. This could mean that Scratch projects are not suitable to measure a high-level understanding of programming concepts. Including the project name in the input vector had a negligible effect on the outcome of the experiments.

Code generation is becoming one of the most important tools in an Integrated Development Environment (IDE) for programmers, be they novices or experts. It allows them to produce code faster, avoid typos and other mistakes, be more efficient and easily turn ideas into code. As such, there are many in-IDE code generation models to choose from, each with their own promises, functionalities and implementation techniques. This report presents the results of a literature survey into the code generation models that have been integrated into programming environments and serves as an overview and analysis of them. The results of this paper provide a set of guidelines for code generation that have been summarized from the selected literature.

Since large language models (LLM) have been emerged, they took a present role in today’s soci- ety. From society, they also found their way into the field of education that is why in this research paper, we looked into assignments and exams from the TU Delft Computer science and engineering bach- elor and assessed which problems Generative pre- trained transformer (GPT) version 3.5, the current version used by ChatGPT, performs well on (i.e. at least above a pass rate) and on which problems it performs less good (i.e. below pass rate). For our research, we collected assignments by asking professors for consent, to make sure our research was ethically correct. Upon receiving consent, pro- fessors had the option to send material, which al- lowed a deeper analysis, or they could also allow a Brightspace (site where TU Delft courses are hosted) course page scrapping. Once all the ques- tions were gathered, we processed them by prompt- ing them into ChatGPT. We gathered the results and categorized them as wrong or right. We did this all with as few modifications to the questions as pos- sible. The only modifications we did were correc- tions of copy errors from a PDF, for example: C becoming e after copying. From the results, we found that ChatGPT has its limitations, particularly in large code understanding and complex mathe- matical reasoning. However, the model performed well in defining concepts and connecting different ideas. We suggest that GPT lacks a comprehensive understanding of coding principles, which hinders its ability to comprehend code. Future work could include exploring other LLMs like GPT-4 and com- paring their performance. Further work could also look at assignments from other universities, pos- sibly in different educational fields. Additionally, investigating different prompting techniques to en- hance the model’s accuracy and reliability could be done as well.

Using AI to support programming has recently gained a lot of popularity. Researchers have been developing tools to support programming activities using GPT models such as ChatGPT and Codex In this paper, we present the most common pro-gramming activities that these models can support. We show that they have a varying range of success
across solving Code Generation, Code Explana-tion,and Data Visualisation problems, but are often able to solve around 50% of problems on the first try. Multiple tries can raise these averages to 75%. Additionally, specialized tools using GPT models have seen promising results regarding Data Visuali-sation, Software Vulnerability Detection, and Gen-
eral Programming Support. This shows a promis-ing trend, and can mean we will all be pair pro-gramming with AI in the near-future.

The recent emergence of AI-driven code generation models can potentially transform programming education. To pinpoint the current state of research on using AI code generators to support learning and teaching programming, we conducted a systematic literature review with 21 papers published since 2018. The review presents the teaching and learning practices in programming education that utilize these models, the characteristics and performance indicators of the code generation models, and aspects to be considered when utilizing the models in programming education, including the risks and challenges of using code generation models for educational practices. AI code generators can be an assistive tool for both learners and instructors if the risks are mitigated.

In recent years, significant progress has been made in the field of natural language processing (NLP) through the development of large language models (LLMs) like BERT and ChatGPT. These models have showcased remarkable abilities across a range of NLP tasks. However, effectively harnessing their potential requires meticulous prompt engineering and a comprehensive understanding of their limitations.

Additionally, LLMs have attracted attention in the educational domain for their potential to enhance learning and teaching experiences, particularly in fostering the development of computational thinking skills.
This paper aims to explore the potential of leveraging NLP and prompt engineering techniques to generate successful solutions to coding problems following initial failures. Furthermore, the research explores the potential applications of NLP techniques in teaching and learning practices involving LLMs and their potential drawbacks in this context.