Teamwork is an integral part of computer science, but group projects in education do not always teach teamwork skills tailored to the context of software project teams. As a result, early-career computer scientists often struggle with team projects that involve interdependent tasks. This research focuses on identifying which teamwork skills are important to computer scientists at the start of their careers and how they perceive the career-education gap regarding teamwork. We conducted semi-structured interviews with 19 early-career computer scientists and analysed them using deductive and inductive coding. Social skills were perceived as the most important by the participants. Although leadership and coordination were among the most prevalent in university education, they were considered less important in industry. These findings can be used to motivate educational design choices in university group projects, focusing on teaching the teamwork skills that will be relevant to students' careers.

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.

Database education is a cornerstone under many of the more popular topics in computer science such as machine learning and visualization. Although, in recent years, more fundamental research into database education has come out, there are many more ways in which it can be extended. Research on the practice of teaching databases, namely on the educational materials and explanations of teachers, can help us create new building blocks for fundamental research. This working group aims to collect and present notional machines of different types, for a wide range of database subtopics. These materials offer and updated context for database educators to design their courses from, as well as open up pathways of further research into database education.

In recent years, researchers have developed several methods to automate discovering datasets and augmenting features for training Machine Learning (ML) models. Together with feature selection, these efforts have paved the way towards what is termed the feature discovery process. Data scientists and engineers use automated feature discovery over tabular datasets to add new features from different sources and enrich training data. By surveying data practitioners, we have observed that automated feature discovery approaches do not allow data scientists to use their domain knowledge during the feature discovery process. In addition, automated feature discovery methods can leak private features or introduce biased ones.

Computational Thinking (CT) is vital in today's digital era, especially in Engineering Education. While no official policy or teaching framework on CT education has been established in the Netherlands, a Western European country, there have been various initiatives for the integration of CT into the curriculum. Recognizing the crucial role of teachers in CT integration, we surveyed the perceptions and intentions of teachers in tertiary education in the Netherlands. Our survey encompassed two aspects: (1) teachers' perceptions of CT, and (2) their intentions to integrate CT into pedagogical activities. 38 teachers, mostly in Engineering Education, from across the Netherlands completed the questionnaire based on the UTAUT framework. Regarding CT perceptions, our investigation reveals that teachers possess an inadequate understanding of the relationship between CT and Computer Science, have limited training experiences in CT, and hold differing opinions on when and which constructs of CT should be integrated into different domains. Concerning teachers' intentions to integrate CT, the results exhibited a strong positive correlation between performance expectancy, attitude towards CT, and behavioral intention to implement CT in learning activities. To foster the integration of CT in tertiary education, our findings suggest the need for further development of higher education teacher training programs focused on CT and its relation to CS. Additionally, there is a call for further exploration of how to enhance teachers' performance expectancy and effort expectancy.

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.

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.

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.

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.

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.

Software Engineering and Computer Science (CS) programs often contain several mathematical courses or courses with large mathematics components or dependencies. Study success in those subjects can be influenced by students’ attitudes towards mathematics and their perceptions about their own relation with mathematics. To gain insights into how the mathematics perceptions of computer science students affect their studies, we collected qualitative data from fourteen CS students from two universities in the context of an elective mathematics course. In this qualitative research, we used a triangulation strategy by collecting data from three different sources (interviews, questionnaires, and math-history assignments) to develop a comprehensive understanding of CS students’ mathematics perceptions. The thematic analysis revealed the factors that affect students’ perceptions about mathematics, including various types of prior experiences, their self-efficacy, math anxiety, and motivation sources. The analysis also highlighted that students find mathematics important for skill transfer to specific CS topics and for supporting continuous learning.

With the growing interest in programming skills in society, programming will inevitably become part of national curricula. The research presented on this poster aims to address programming for everyone and hopes to contribute to the accessibility of programming lessons for both students and teachers with various backgrounds. In order to do so, we approach the learning and teaching of a programming language from a natural language perspective, and in particular, we focus on reading code, including one of the most important programming concepts: variables.

The Structured Query Language (SQL) is a widely taught database query language in computer science, data science, and software engineering programs. While highly expressive, SQL is challenging to learn for novices. Various research has explored the errors and mistakes that SQL users make. Specific attributes of SQL code, such as the number of tables and the degree of nesting, have been found to impact its understandability and maintainability. Furthermore, prior studies have shown that novices have significant issues using SQL correctly, due to factors such as expressive ease, existing knowledge and misconceptions, and the impact of cognitive load.

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, to cross-pollinate perspectives and methods, and to 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, an industry panel discussion, and paper and poster presentations.

Scratch enables children to learn about programming by creating games and animations, and is currently one of the most popular introductory programming languages. While Scratch has been found to increase students' motivation and interest in programming, it has been debated whether Scratch users practice and learn about core programming concepts such as loops, conditional expressions, procedures and variables. This paper presents a large scale study of the progression of the programming concepts practiced by Scratch users through an analysis of their complete public project portfolios. A dataset of over 112 thousand authors and their 1 million projects was constructed and analyzed from three viewpoints. First, we investigate the development of programming concepts by looking at block usage statistics for each project in the users' repositories. Second, we score and analyze the dataset using a computational thinking rubric. Third, we identify users that have left the Scratch platform and evaluate the learning goals they have achieved. Our results show that, while users progress in Scratch, there is a positive trend in the use of all concepts that were examined. Within the least utilized concepts, even after the 20th project of Scratch users, are procedures, conditional loops and logic operations. Examining the users who have left the Scratch platform after creating at least the mean amount of nine projects, we measured that half had left without ever utilizing procedures, and a third had left without ever utilizing conditional loops.

SQL is the most commonly taught database query language. While previous research has investigated the errors made by novices during SQL query formulation, the underlying causes for these errors have remained unexplored. Understanding the basic misconceptions held by novices which lead to these errors would help improve how we teach query languages to our students. In this paper we aim to identify the misconceptions that might be the causes of documented SQL errors that novices make. To this end, we conducted a qualitative think-aloud study to gather information on the thinking process of university students while solving query formulation problems. With the queries in hand, we analyzed the underlying causes for the errors made by our participants. In this paper we present the identified SQL misconceptions organized into four top-level categories: misconceptions based in previous course knowledge, generalization-based misconceptions, language-based misconceptions, and misconceptions due to an incomplete or incorrect mental model. A deep exploration of misconceptions can uncover gaps in instruction. By drawing attention to these, we aim to improve SQL education.

Motivation Only 27% of computer and mathematical scientists in the United States and 18% of IT specialists in Europe are women. The under-representation of women in the field of Computer Science is, among other things, influenced by stereotypes of computer scientists. These stereotypes include being male, asocial and having an (obsessive) interest in computers. Even though stereotypical beliefs can develop at an early age, research on children’s stereotypes of computer scientists is sparse and inconclusive.

Courses in computer science curricula often involve group programming assignments. Instructors are required to take several decisions on assignment setup and monitoring, team formation policies, and grading systems. Group programming projects provide unique monitoring opportunities due to the availability of both product and process data, as well as challenges in team composition, with students of varying levels of prior programming experience. To gain insights into the experiences and perceptions of students about the assignment setup and grading policies in group programming projects, we interviewed 20 computer science students from four universities. The thematic analysis highlighted factors in group composition that are considered important, as well as advantages and disadvantages of the self-selection of the teams. It also indicated three grading strategies experienced by the students, namely, being assigned the same group grade, individual grades distributed by the instructor, and grade distribution determined by the team, with perceptions about them varying greatly. Several practices for monitoring team contributions were identified. Checking the source code repositories was considered useful in recognizing slacking members, but automated metrics are not always representative of the work distribution. The analysis also uncovered student perceptions on the grading factors for programming assignments, including coding efficiency and skill.

Programming education currently begins at the elementary school age. In this paper we are exploring what affects the learning performance of young students in programming classes. We present the results collected during an eight-week experimental Scratch programming course run in elementary schools. We emphasize factors that have been found to affect learning performance in adult students, including self-efficacy and motivation, and measure how they affect students of this age group. We further explore the students' view of programming as a career path, and measure the effects of the course, their performance, and the stereotypes that they assume for computer scientists. We find that students' intrinsic and extrinsic motivation and previous programming experience are important factors, being strongly correlated with their self-efficacy and their inclination towards a CS career. For female students only, we also find CS career orientation to be strongly correlated with their self-efficacy.

When children learn to read, they almost invariably start with oral reading: reading the words and sentences out loud. Experiments have shown that when novices read text aloud, their comprehension is better then when reading in silence. This is attributed to the fact that reading aloud focuses the child's attention to the text. We hypothesize that reading code aloud could support program comprehension in a similar way, encouraging novice programmers to pay attention to details. To this end we explore how novices read code, and we found that novice programmers vocalize code in different ways, sometimes changing vocalization within a code snippet. We thus believe that in order to teach novices to read code aloud, an agreed upon way of reading code is needed. As such, this paper proposes studying code phonology, ultimately leading to a shared understanding about how code should be read aloud, such that this can be practiced. In addition to being valuable as an educational and diagnostic tool for novices, we believe that pair programmers could also benefit from standardized communication about code, and that it could support improved tools for visually and physically disabled programmers.

Scratch is increasingly popular, both as an introductory programming language and as a research target in the computing education research field. In this paper, we present a dataset of 250K recent Scratch projects from 100K different authors scraped from the Scratch project repository. We processed the projects' source code and metadata to encode them into a database that facilitates querying and further analysis. We further evaluated the projects in terms of programming skills and mastery, and included the project scoring results. The dataset enables the analysis of the source code of Scratch projects, of their quality characteristics, and of the programming skills that their authors exhibit. The dataset can be used for empirical research in software engineering and computing education.

Abstract Spreadsheets are popular end user programming tools, especially in the industrial world. This makes them interesting research targets. However, there does not exist a reliable grammar that is concise enough to facilitate formula parsing and analysis and to support research on spreadsheet codebases. This paper presents a grammar for spreadsheet formulas that can successfully parse 99.99% of more than 8 million unique formulas extracted from 4 spreadsheet datasets. Our grammar is compatible with the spreadsheet formula language, recognizes the spreadsheet formula elements that are required for supporting spreadsheets research, and produces parse trees aimed at further manipulation and analysis. Additionally, we use the grammar to analyze the characteristics of the formulas of the 4 datasets in 3 different dimensions: complexity, functionality, and data utilization. Our results show that (1) most Excel formulas are simple, however formulas with more than 50 functions or operations exist, (2) almost all formulas use data from other cells, which is often not local, and (3) a surprising number of referring mechanisms are used by less than 1% of the formulas.

Block-based programming languages like Scratch, Alice and Blockly are becoming increasingly common as introductory languages in programming education. There is substantial research showing that these visual programming environments are suitable for teaching programming concepts. But, what do people do when they use Scratch? In this paper we explore the characteristics of Scratch programs. To this end we have scraped the Scratch public repository and retrieved 250,000 projects. We present an analysis of these projects in three different dimensions. Initially, we look at the types of blocks used and the size of the projects. We then investigate complexity, used abstractions and programming concepts. Finally we detect code smells such as large scripts, dead code and duplicated code blocks. Our results show that 1) most Scratch programs are small, however Scratch programs consisting of over 100 sprites exist, 2) programming abstraction concepts like procedures are not commonly used and 3) Scratch programs do suffer from code smells including large scripts and unmatched broadcast signals.

Recently, block-based programming languages like Alice, Scratch and Blockly have become popular tools for programming education. There is substantial research showing that block-based languages are suitable for early programming education. But can block-based programs be smelly too? And does that matter to learners? In this paper we explore the code smells metaphor in the context of block-based programming language Scratch. We conduct a controlled experiment with 61 novice Scratch programmers, in which we divided the novices into three groups. One third receive a non-smelly program, while the other groups receive a program suffering from the Duplication or the Long Method smell respectively. All subjects then perform the same comprehension tasks on their program, after which we measure their time and correctness. The results of the experiment show that code smell indeed influence performance: subjects working on the program exhibiting code smells perform significantly worse, but the smells did not affect the time subjects needed. Investigating different types of tasks in more detail, we find that Long Method mainly decreases system understanding, while Duplication decreases the ease with which subjects modify Scratch programs.

Spreadsheets are popular end-user computing applications and one reason behind their popularity is that they offer a large degree of freedom to their users regarding the way they can structure their data. However, this flexibility also makes spreadsheets difficult to understand. Textual documentation can address this issue, yet for supporting automatic generation of textual documentation, an important pre-requisite is to extract metadata inside spreadsheets. It is a challenge though, to distinguish between data and metadata due to the lack of universally accepted structural patterns in spreadsheets. Two existing approaches for automatic extraction of spreadsheet metadata were not evaluated on large datasets consisting of user inputs. Hence in this paper, we describe the collection of a large number of user responses regarding identification of spreadsheet metadata from participants of a MOOC. We describe the use of this large dataset to understand how users identify metadata in spreadsheets, and to evaluate two existing approaches of automatic metadata extraction from spreadsheets. The results provide us with directions to follow in order to improve metadata extraction approaches, obtained from insights about user perception of metadata. We also understand what type of spreadsheet patterns the existing approaches perform well and on what type poorly, and thus which problem areas to focus on in order to improve.

Spreadsheets are ubiquitous in the industrial world and often perform a role similar to other computer programs, which makes them interesting research targets. However, there does not exist a reliable grammar that is concise enough to facilitate formula parsing and analysis and to support research on spreadsheet codebases. This paper presents a grammar for spreadsheet formulas that is compatible with the spreadsheet formula language, is compact enough to feasibly implement with a parser generator, and produces parse trees aimed at further manipulation and analysis. We evaluate the grammar against more than one million unique formulas extracted from the well known EUSES and Enron spreadsheet datasets, successfully parsing 99.99%. Additionally, we utilize the grammar to analyze these datasets and measure the frequency of usage of language features in spreadsheet formulas. Finally, we identify smelly constructs and uncommon cases in the syntax of formulas.

Spreadsheets are used heavily in many business domains around the world. They are easy to use and as such enable end-user programmers to and build and maintain all sorts of reports and analyses. In addition to using spreadsheets for modeling and calculation, spreadsheets are often also used for creating reports and dashboards: combining data from different sources and creating overviews. For this, lookup functions can be used: they search for a value in a range and return a corresponding row or column. Lookup functions are common: according to recent research the VLOOKUP is the fifth most common Excel function. In this paper we investigate the use of lookup functions in more detail. We analyze lookup functions within the newly released Enron spreadsheet corpus. The results show that 1) a minority of 43% of lookup formulas use the default setting where an approximate match may be returned, 2) 77% of approximate matches are used unnecessary and 3) 23% of approximate lookups is problematic: they search over unsorted ranges, while this is specifically advised against in the specification, and might lead to wrong results.

Abstract The integration of wireless sensor networks with different network systems gives rise to many research challenges to ensure security, privacy and trust in the overall architecture. The main contribution of this paper is a generic security, privacy and trust framework providing context‐aware adaptability, flexibility and scalability which allows customisation of wireless sensor networks to a diverse set of application spaces. Suitable protocols and mechanisms are identified, which when combined according to the framework form a complete toolbox solution which fits the architecture of Beyond 3G environments. Performance evaluation results demonstrate the feasibility and estimate the benefits of the security framework for a variety of scenarios. Copyright © 2008 John Wiley & Sons, Ltd.

In sensor networks, privacy can be addressed in different levels of the network stack and at different points of the information flow. This paper presents an application level scheme for controlling information disclosure at the points of data capture. The scheme includes a trust model for facilitating in-network privacy decisions. The trust model exploits the pre-deployment knowledge on the network topology and the information flows, and combines aspects from alternative approaches on trust establishment on common evaluation metrics, in order to allow for flexibility in the trust establishment process. The trust assigned to each data requestor is used to determine if the data or only a sample of it will be disclosed, or if the request will be rejected. The scheme allows the use of various mechanisms, including negative surveys, for publishing samples of data to partially trusted requestors. The proposed scheme has been validated through simulation. The results and analysis demonstrate its effectiveness in managing trust relationships and data disclosure operations.

Sensor networks are set to become a truly ubiquitous technology that will affect the lives of the people in their application environment. While providing the opportunity for sophisticated, context-aware services, at the same time sensor networks impose great privacy risks. This paper discusses privacy issues in sensor networks, by identifying the requirements for privacy preserving deployments, analysing the challenges faced when designing them, and discussing the main solutions that have been proposed.