In recent years, database education has been receiving more attention, with research in various directions such as the development of tools for education, the analysis of students’ homework, and the exploration of misconceptions. Misconceptions are mistakes in student reasoning that lead to errors during problem-solving. Recent work has documented misconceptions and errors in SQL. In this study we test the prevalence of several of these misconceptions through a multiple-choice questionnaire, to see if they hold on a larger, more diverse, student population. We found that all misconceptions are held to some extent, with prevalence scores ranging from one to fifty-two percent of the student population. Additionally, we have uncovered previously unidentified areas of struggle, allowing us to identify new misconceptions.@en

This report summarizes the outcomes of the first international workshop on Data Systems Education: Bridging Education Practice with Education Research (DataEd '22). The workshop was held in conjunction with the SIGMOD '22 conference in Philadelphia, USA on June 17, 2022. The aim of the workshop was to provide a dedicated venue for presenting and and discussing data management systems education experiences and research by bringing together the database and the computing education research communities to share findings, to crosspollinate perspectives and methods, and to shed light on opportunities for mutual progress in data systems education. The program featured two keynote talks, ten research paper presentations, a discussion session, and an industry panel discussion. In this report, we present the workshop's main results, observations, and emerging research directions.

@en

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.

@en

Prior studies in the Computer Science education literature have illustrated that novices make many mistakes in composing SQL queries. Query formulation proves to be difficult for students. Only recently, some headway was made towards understanding why SQL leads to so many mistakes, by uncovering student misconceptions. In this article, we shed new light on SQL misconceptions by analyzing the hypotheses of SQL experts on the causes of student errors. By examining the experts' perceptions, we draw on their understanding of students' misconceptions and on their experiences with studying and teaching SQL. For our analysis, we chose the Policy Delphi, a questionnaire instrument specifically designed for gathering opinions and evidence. Through a two-round process, our nineteen participants proposed and voted on underlying causes for SQL errors which resulted in a set of hypotheses per error. Our main contribution to this article is this new set of possible misconceptions. With them, we can design more complete educational approaches to address misconceptions underlying SQL errors made by students, leading to more effective SQL education.

@en

Computing containment relations between massive collections of sets is a fundamental operation in data management, for example in graph analytics and data mining applications. Motivated by recent hardware trends, in this paper we present two novel solutions for computing set-containment joins over massive sets: the Patricia Trie-based Signature Join (PTSJ) and PRETTI+, a Patricia trie enhanced extension of the state-of-the-art PRETTI join. The compact trie structure not only enables efficient use of main-memory, but also significantly boosts the performance of both approaches. By carefully analyzing the algorithms and conducting extensive experiments with various synthetic and real-world datasets, we show that, in many practical cases, our algorithms are an order of magnitude faster than the state-of-the-art.@en