Computational Thinking (CT), particularly abstraction, is essential in engineering education, enabling students to break down complex systems into manageable parts. Abstraction helps learners focus on key elements of a problem, ignoring extraneous details. The PGK framework, suggested by Per-renet, Groote, and Kaasenbrood, defines abstraction across four cognitive levels: problem, algorithm, program, and execution. At a higher education institution that focuses on engineering education, we assessed students' abstraction skills using sorting algorithms, chosen for their foundational role and suitability for testing such skills. Our study focused on two areas: (1) the performance of computer science (CS) and non-CS students on algorithmic abstraction tasks, and (2) how factors like demographics, training, programming proficiency, and self-assessed abstraction mastery correlate with task performance. Results showed that all students, especially non-CS majors (including Engineering), need stronger skills at the algorithm, program (coding algorithms), and execution (code functionality) levels. Many non-CS students overestimated their abilities, highlighting a gap in mastery. Students with programming experience performed better, underscoring the importance of hands-on training. These findings suggest interventions for non-CS students are needed to gain experience in programming and to bridge the gap between perceived and actual skills. Future research should focus on discipline-specific curricula and long-term studies to ensure that all students develop the essential CT skills for the digital era.

Even though the field of Computer Science (CS) affects different aspects of society, several groups of society are underrepresented, including women and nonbinary people. Children might have different learning opportunities in CS due to their project preferences. Girls are likelier to work on stories and simple programs in Scratch, whereas boys tend to create games and more complex programs. We explore whether preferences and program implementation differ between genders within a story, game and visual adventure in Hedy, a gradual textual programming language. We analysed 14,233 programs within five Hedy levels created by 2,819 users who turned 10 to 14 in 2023. We found that boys, girls and nonbinary children worked most on the game adventure. Within the individual adventures, gender differences occur in all three adventures in the most elaborate Hedy level analysed. However, for some levels, no gender differences were found. Thus, programming assignments can be created in which children of different genders work on similar programs in terms of size and number of (unique) commands used.

Computational Thinking (CT) is considered a core 21st century digital skill. The aspect of assessment is crucial and knowing what, who, when, how, and where to assess is important for assessment design. In this study, we conducted an umbrella review to gain insights regarding CT assessment in higher education. In total, we analyzed 11 reviews, focusing on: (1) bibliographical and methodological characteristics of the reviews; (2) aspects relevant of assessment design, including a) assessed constructs, b) applied assessment methodologies, and c) assessment contexts. Our findings suggest an increased attention on this topic. However, hardly any reviews reasoned the selection of their review methodology, and most of the reviews did not thoroughly examine existing reviews. Regarding assessment design aspects, most reviews did not confine their scope to higher education; however, findings on interventions and educational settings show commonalities. We identified 120 unique assessed constructs and around 10 types of assessment methods. Though a combined use of distinct assessment methods is suggested in reviews, guidelines for appropriate assessment design are yet to be constructed. Based on the findings, we argue that it is necessary to explore different combinations of assessment design in various contexts to construct assessment guidelines.

Motivation. Variable naming practices are part of the software developer’s profession, influencing program comprehension and code quality. Yet, little is known about how variable naming practices are taught in beginner courses. Objective. This paper investigates naming beliefs, self-reported teaching practices, and observations regarding variable naming practices of teachers of introductory Python programming courses. Methods. We adopted an in-depth qualitative approach by interviewing ten teachers from secondary education and higher education and developed several themes in order to answer our research questions. Results. Among various opinions and practices, we found that teachers agree on using meaningful names, but have conflicting beliefs about what is meaningful. Moreover, the described teaching practices do not always match teacher’s views on meaningful names, and teachers rarely encourage students to use them. Instead, they express that naming practices should not be enforced and that students will develop them by example. Whereas some teachers report focusing solely on conventions, others deliberately dedicate time for students to engage with naming, create their own guidelines, provide continuous feedback, or include naming exercises on exams. Discussion. Naming practices do not seem to be deliberately taught, even though they influence program understanding and code quality. We also identified inconsistencies in teachers’ self-reported naming practices. As such, we encourage intentional conversations about naming practices in educational settings, specifically linking naming to code quality and readability. We see room for group and peer activities as a means to this end, as well as providing formative feedback dedicated to naming.

From an early age, girls may opt out of Computer Science (CS) for not fitting the CS stereotypes of being male, asocial and technology-oriented. These stereotypes might be strengthened by children's books on programming, but little is known about this. Therefore, this paper explores the gender, social interactions and interests of characters illustrated in ten popular extracurricular Scratch and Python children's books. We found more masculine than feminine characters in all but one book. Furthermore, nearly half of the characters are illustrated alone, and 15% are interacting with computers & robots. Over two-thirds of the characters fit at least one stereotypical trait. With this paper, we aim to create awareness of stereotypes in CS books among creators, publishers and buyers. Making and using more inclusive CS materials will help close the gender gap.

Multiple works in data management research focus on automating the processes of data augmentation and feature discovery to save users from having to perform these tasks manually. Yet, this automation often leads to a disconnect with the users, as it fails to consider the specific needs and preferences of the actual end-users of data management systems for machine learning. To explore this issue further, we conducted 19 semi-structured, think-aloud use-case studies based on a scenario in which data specialists were tasked with augmenting a base table with additional features to train a machine learning model. In this paper, we share key insights into the practices of feature discovery on tabular data performed by real-world data specialists derived from our user study. Our research uncovered differences between the user assumptions reported in the literature and the actual practices, as well as some areas where literature and real-world practices align.

This pilot study explores how visualization strategies, grounded in multiple representations theory, impact novice students’ engagement, and cognitive load during program tracing tasks. Students were were shown a visualization of the three-variable swap problem at the start of an introductory programming course (CS1) at a large public North American research-intensive university. We compared three conditions: interactive multiple representations, Python Tutor (a single-representation tool), and text-only methods. Preliminary results indicate that interactive multiple representations increase engagement for students with prior programming experience, while no significant differences were observed for students without prior experience. These findings suggest that while multiple representations may boost engagement, identifying how to effectively support students of all experience levels and reduce cognitive load requires further study.

Despite extensive studies from the software engineering community on how naming practices influence programming behavior, the topic receives little attention in education. Prior work indicated little agreement on good naming because it depends on many factors. Students are told that "naming is important'' and "should be meaningful,'' yet its practical implementation is rarely discussed and feedback is lacking. The current work presents a dialogic teaching approach focused on teaching a critical reflection on naming practices through five activity types: (A) perceptions and experiences, (B) create names, (C) evaluate through ranking, (D) compare codes, and (E) locate a mistake. We developed, ran, and analyzed a one-hour workshop, that we present here and share our experiences, leading to recommendations for teachers. Our contribution is twofold: (1) we provide a set of (adaptable) activities and exercises for supporting deliberate naming practices, thereby assisting teachers interested in adopting naming practices into their curriculum; (2) we provide insights regarding the student perspective on naming practices, derived from the activities, revealing potential issues and opportunities in teaching the topic.

Motivation. Many people interested in learning a programming language choose online courses to develop their skills. The concept of variables is one of the most foundational ones to learn, but can be hard to grasp for novices. Variables are researched, but to our knowledge, few empirical observations on how the concept is taught in practice exist. Objective. We investigate how the concept of variables, and the respective naming practices, are taught in introductory Massive Open Online Courses (MOOCs) teaching programming languages. Methods. We gathered qualitative data related to variables and their naming from 17 MOOCs. Collected data include connections to other programming concepts, formal definitions, used analogies, and presented names. Results. We found that variables are often taught in close connection to data types, expressions, and program execution and are often explained using the 'variable as a box' analogy. The latter finding represents a stronger focus on 'storing values', than on naming, memory, and flexibility. Furthermore, MOOCs are inconsistent when teaching naming practices. Conclusions. We recommend teachers and researchers to pay deliberate attention to the definitions and analogies used to explain the concept of variables as well as to naming practices, and in particular to variable name meaning.

Background and Context. Increasing gender diversity in the field of Computer Science (CS) benefits the economy as well as gender equality. However, several obstacles - including underdeveloped CS interests, lack of programming experience, and a misfit with the stereotypes of computer scientists - prevent women from entering the field. Although these barriers develop from an early age, research focused on children is limited. Furthermore, limited work is done within European countries. Objectives. In this study, we research the interest children aged 7 to 14 have in a CS career. Additionally, we look into whether children with different characteristics have a different interest in CS. As such our research question: How does children's interest in a CS career differ based on their a) age, b) gender, c) computer interests, d) programming experience, and e) stereotypical beliefs? Method. We collected data from 200 children in a science museum located in the Netherlands. We gathered data on their gender, age, computer interests, programming experience, stereotypical beliefs and interest in becoming a programmer - as representative of a CS career. We used self-reported closed questions and reduced-length Child Implicit Association Tests. Findings. In general, the participating children are not interested in a CS career. We did find several characteristics related to a higher interest in a CS career: being a boy, having an interest in computers or video gaming, gaining programming experience at home or at an out-of-school activity, and the belief that programmers are social. Implications. To increase the participation of women in CS, we suggest motivating girls more to engage in computer and programming activities - especially out-of-school activities - while ensuring that these activities are gender inclusive.

Querying a relational database is typically taught in practice by using an exercise database. Such databases may be simple toy examples or elaborate and complex schemas that mimic the real world. Which of these are preferable for students is yet unknown. Research has shown that while more complex exercise databases may hinder learning, they also benefit student engagement, as more complex databases are seen as more realistic. In our mixed-methods study, we explore what aspects of an exercise database contribute to student engagement in database education. To gain insight into what students would deem engaging, we asked 56 students to design, implement, and reflect on engaging databases for database education. The results imply that students are engaged by highly diverse yet easily understood database business domains, relatively simple database structures, and conceivable yet seemingly realistic amounts of data. The results challenge some previous study results while supporting approaches found in some textbooks, and provide guidelines and inspiration for educators designing exercise databases for querying and introducing relational database concepts.

Interest in data systems education is increasing, especially with the rise in demand for well-trained and re-trained data scientists. The database and the computing education research communities have complementary perspectives and experiences to share with each other. The DataEd workshop is organized as a dedicated venue for these communities to come together to share findings, cross-pollinate perspectives and methods, and shed light on opportunities for mutual progress in data systems education. In the DataEd workshop, we will present and discuss data management systems education experiences and research via keynotes and paper and poster presentations.

Several educational studies have argued for the contextualization of assignments, i.e., for providing a context or a story instead of an abstract or symbolic problem statement. Such contextualization may have beneficial effects such as higher student engagement and lower dropout rates. In the domain of database education, textbooks and educators typically provide an example database for context. These are then used to introduce key concepts related to database design, and to illustrate querying. However, it remains unstudied what kinds of database contexts are engaging for novices. In this paper, we study which aspects of database domain and complexity students find engaging through student reflections on a database creation assignment. We identify six factors regarding engaging domains, and five factors for engaging complexity. The main factor for domain-related engagement was Personal interest, the main factor for complexity engagement was Matching information requirements. Our findings can help database educators and book authors to design engaging exercise databases targeted for novices.