Conceptual data modeling is a central activity in data work, yet how such models are created remains understudied. While data attributes play a key role, modeling is also shaped by tasks, tools, developers’ prior experiences, and often unfolds collaboratively between diverse stakeholders. In this study, we invited 22 participants with varying expertise in pairs to collaboratively sketch conceptual data models. We captured screen recordings, their evolving sketches, and conversations. Through a mixed-methods approach combining thematic analysis of dialogue with an examination of model artifacts, we identify how communication and collaboration patterns influenced the process. Our findings reveal a range of collaborative strategies and representations, as well as distinct ways dialogue shaped the emergence and expression of shared conceptual models. These insights deepen understanding of Human-Data Interaction in collaborative data work and point to design opportunities for tools that better support communication, negotiation, and sensemaking of data.

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.

The field of data systems has seen quick advances due to the popularization of data science, machine learning, and real-time analytics. In industry contexts, system features such as recommendation systems, chatbots and reverse image search require efficient infrastructure and data management solutions. Due to recent advances, it remains unclear (i) which topics are recommended to be included in data systems studies in higher education, (ii) which topics are a part of data systems courses and how they are taught, and (iii) which data-related skills are valued for roles such as software developers, data engineers, and data scientists. This working group aims to answer these points to explain the state of data systems education today and to uncover knowledge gaps and possible discrepancies between recommendations, course implementations, and industry needs. We expect the results to be applicable in tailoring various data systems courses to better cater to the needs of industry, and for teachers to share best practices.

Relational databases are ubiquitous in industry and have been for several decades. As such, almost all Computer Science bachelor degrees include one or more courses that teach about databases and their corresponding query language called Structured Query Language (SQL). However, many learners struggle with learning the language, making many mistakes and finding the problems hard to solve. In this paper, we explore the research done to identify learners’ issues as well as showcase some research that can support these learners.

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.

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.

No abstract available.

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.

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.

SQL is a typical query language for performing data analytics. Although its usage is ubiquitous, learners experience that query formulation in SQL is error-prone and time-consuming. Prior research has shown that this is due to low expressive ease, extensive training requirements and high cognitive load, all of which present a significant burden for SQL learners. Visual representations can assist learners to significantly lower this burden. The current dominant paradigm aims to facilitate SQL querying by helping users to avoid the syntax of SQL. Such Visual Querying Systems (VQS), however, are not effective for SQL learners as they hide the syntax of the language during query formulation, rather than assisting learners to write correct queries in SQL. Furthermore, training with VQSs is system specific, which leads to system dependency for learners. We argue that novices need support from Visual Query Representation (VQR) solutions which, instead, help them in learning how to write correct and portable SQL queries. In this paper we present SQLVis, a VQR to support novice SQL users in query writing. Our system represents the query as written in SQL by the user, which can improve the SQL writing proficiency of its users. Results of an in depth empirical study demonstrate the significant value of SQLVis for learners.