NoSQL data models such as document databases and wide-column databases have gained popularity in industry and education in the last two decades. The database design principles and query languages of different NoSQL systems differ from those of the relational model and SQL, which can cause challenges in transitioning from relational systems to NoSQL systems. In this mixed-methods study, we explore the challenges novices experience with database design and querying with two NoSQL systems: MongoDB and Cassandra. Our results show that NoSQL database design is perceived more difficult than relational due to access-pattern-first design patterns, old habits, and the limits imposed by database distribution. Querying, however, is not perceived as more difficult or easier due to similarities of query languages to previous experiences with SQL and programming. These results are applicable in the classroom in determining the teaching order of novel data models, and in focusing teaching on the challenging aspects in document and wide-column databases.

Courses on data systems in higher education are usually designed based on curriculum guidelines, textbooks, the instructor's own understanding of the subject, or a mix of these sources. As a result, the course contents may not always align with current industry standards, either in terms of theoretical foundations or the technical tools used to teach data systems concepts. In this mixed-methods study, we investigate the technical and non-technical skills needed in data systems industry, with the goal of bridging the gap of data systems education and the data systems industry. Our results show that SQL, data modeling and data pipelines, as well as cloud computing and data warehouses are the most sought after skills in Finnish industry. Our results are applicable in developing data systems syllabi and curricula to better meet industry expectations for future data professionals.

Abstract Error messages are one of the primary ways software developers communicate with database management systems in SQL query writing tasks. Even though reading and interpreting error messages is a significant part of a software developer’s work, the error messages of SQL compilers have received criticism in terms of readability and their perceived detrimental effects on user experience. Consequently, redesigned SQL error messages have also been proposed to tackle the problems in current error messages. In this study, we examine the effects of enhanced error messages on query writing from several perspectives. The results indicate that when compared to PostgreSQL error messages, the enhanced error messages facilitate faster error fixing, as well as perceived benefits in error finding and error recovery confidence. Our results are applicable in industry, where development time is of significant importance, as well as in educational contexts, where user experience, user confidence, and perceived support in learning play an important role.

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.

Reading and interpreting error messages are significant aspects of a software developer's work. Despite the importance and prevalence of error messages, especially for novices, SQL compiler error messages from various relational database management systems have seen limited development since their inception. This lack of progress may stem from the fact that it is not well-understood what constitutes an effective error message. With data from 568 participants across three student cohorts, we investigate whether novel SQL error message design guidelines can explain success in fixing SQL syntax errors. The results indicate that some of the guidelines indeed serve as building blocks toward more effective SQL error messages for novices. However, error messages that adhered to certain guidelines showed inconclusive or negative results. These findings can be applied to iterate on SQL error messages in SQL learning environments or SQL compilers.

Efficiency has been a pivotal aspect of the software industry since its inception, as a system that serves the end-user fast, and the service provider cost-efficiently benefits all parties. A database management system (DBMS) is an integral part of effectively all software systems, and therefore it is logical that different studies have compared the performance of different DBMSs in hopes of finding the most efficient one. This study systematically synthesizes the results and approaches of studies that compare DBMS performance and provides recommendations for industry and research. The results show that performance is usually tested in a way that does not reflect real-world use cases, and that tests are typically reported in insufficient detail for replication or for drawing conclusions from the stated results.

Software developers use a significant amount of time reading and interpreting error messages. However, error messages have often been based on either anecdotal evidence or expert opinion, disregarding novices, who arguably are the ones who benefit the most from effective error messages. Furthermore, the usability aspects of Structured Query Language (SQL) error messages have not received much scientific attention. In this mixed-methods study, we coded a total of 128 error messages from eight database management systems (DBMS), and using data from 311 participants, analysed 4,796 queries using regression analysis to find out if and how acknowledged error message qualities explain SQL syntax error fixing success rates. Additionally, we performed a conventional content analysis on 1,505 suggestions on how to improve SQL error messages, and based on the analysis, formulated a framework consisting of nine guidelines for SQL error message design. The results indicate that general error message qualities do not necessarily explain query fixing success in the context of SQL syntax errors and that even some novel NewSQL systems fail to account for basic error message design guidelines. The error message design framework and examples of its practical applications shown in this study are applicable in educational contexts as well as by DBMS vendors in understanding novice perspectives in error message design.

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.

Modern database management is often faced with a high number of concurrent end-users, and the need for database distribution to ensure fault tolerance and high throughput. To flexibly address these challenges, many modern database management systems (DBMS) provide highly automated and effortless, i.e., highly usable database distribution, deployment, and maintenance. However, the usability considerations are yet to extend from the aforementioned DBMS features to query language compilers. In this study, based on participant answers (N = 157), we compare the error message qualities of four modern DBMSs (CockroachDB, SingleStore, NuoDB, and VoltDB) using one objective and three subjective metrics. Our results show that some of the DBMSs provide the users with more useful error messages, even though many of these error messages violate even the most basic usability guidelines. These results (i) are applicable in further developing the usability aspects of query language compilers, (ii) provide a timely effort of bridging the gap between human-computer interaction and query language compilers, and (iii) offer suggestions on teaching novices, who require emphasized support in query formulation.

A segment of studies on group structure and performance in software engineering (SE) project-based learning (PjBL) have focused on roles, including studies that use Belbin team roles and studies that address problematic roles such as social loafing. The present study focuses on the status, which is basically missing in SE PjBL studies, although relating to roles. The study investigates the aspects that students identified as indicators of rising or declining status in their project groups. The status theory was utilized as the framework that motivated the research and on which the results were reflected. An inductive qualitative content analysis was applied to learning reports in which students reflected on their statuses. The indicators of rising status included technical know-how, commitment, management responsibility, and idea ownership, while also group-level attributes such as a caring atmosphere and joint responsibility. The indicators of a declining status included aspects that appear as counterparts of rising status indicators, while also more refined aspects such as no one willing to be a leader or study background. The results are concluded to provide material for educating students about intra-group relations and promoting self-regulation for fruitful collaboration in groups. The authors believe that the results also initiate further PjBL research in which status theory can be utilized.

Geographically distributed information system development (ISD) projects are more and more common, especially among organisations operating in global markets. Distributed ISD yields potential competitive advantages by developing new products near the target markets, utilizing global labour markets, and exposing the organisation to innovations, ideas and new paradigms. However, distributed ISD also presents challenges and problems which organisations must take into consideration. The pivotal challenge is usually communication. People working on the same project in different locations find it difficult to communicate due to lack of formal and informal face-to-face communication, different working cultures and languages, and time difference. In this study, we set out to investigate what challenges may rise in geographically distributed ISD, and how these challenges and problems of geographical distribution could be mitigated.

Esports has generated an industry of increasing economic and cultural importance. In recent years, universities and other higher education institutions have responded to its growth by establishing undergraduate courses to satisfy the needs of innovators operating in the area. However, there is not yet consensus on what an esports curriculum should include. Despite being a technology-driven sector with ethical and professional dimensions that intersect computing, current ACM and IEEE curricula do not mention esports. Furthermore, existing courses tend to provide teaching and training on a wide variety of topics aside from those traditionally in computer science. These include: live events management; psychological research; sports science; marketing; public relations; video (livestream) production; and community management; in addition to coaching. This working group seeks to examine the requirements for developing esports studies at universities with a focus on understanding career prospects in esports and on the challenges presented by its disciplinary complexity. The group will identify key learning outcomes and assess how they align with industry needs, paving the way for a framework to support the design of esports curricula in higher education.

The database and the database management system (DBMS) are two of the main components of any information system. Structured Query Language (SQL) is the most popular query language for retrieving data from the database, as well as for many other data management tasks. During system development and maintenance, software developers use a considerable amount of time to interpret compiler error messages. The quality of these error messages has been demonstrated to affect software development effectiveness, and correctly formulating queries and fixing them when needed is an important task for many software developers. In this study, we set out to investigate how participants (N=152) experienced the qualities of error messages of four popular DBMSs in terms of error message effectiveness, perceived usefulness for finding and fixing errors, and error recovery confidence. Our results show differences between the DBMSs by three of the four metrics, and indicate a discrepancy between objective effectiveness and subjective usefulness. The results suggest that although error messages have perceived differences in terms of usefulness for finding and fixing errors, these differences may not necessarily result in differences in query fixing success rates.

The learning of practical Structured Query Language (SQL) skills often takes place in digital environments, where the learner writes queries against an exercise database. The exercise database is usually designed and implemented by the teacher, and populated with makeshift data. Although this approach is common, and SQL taught in almost all database courses, little scientific attention has been given to the nature of the exercise database.

This Full Research Paper presents enterprise architecture (EA) modeling tools utilized in an educational context. EA is a well-known and a commonly used approach for organizational development aiming to improve the alignment of business operations and information technology. This high level design of information technology (IT) driven business operations lays the foundations on lower level technical activities such as the design and implementation of application programs and features, system boundary interfaces, database distribution and data pipes, and system recovery. Organizations' architectures are made visible by creating EA artefacts, such as business process diagrams, data models and development roadmaps for the betterment of a holistic understanding and future planning of organizational IT solutions. It follows that IT students as future IT professionals need to understand the high level organizational IT landscape in order to understand, for example, software interface design, feature prioritization, and the evaluation of suitable technologies. Although EA is one of the core competency areas of the academic information systems graduate curriculum, the means of teaching EA are seldom discussed, and studies specifically focusing on modeling EA are lacking. In this paper, we report our experiences on teaching a practical course on EA, and our findings based on data collected from students who took the course. By discussing our findings in relation to a widely acknowledged competency model for graduate degree programs in information systems as well as prior research, we conclude that it is possible to effectively teach the modeling of some of the most essential EA artefacts with different tools. Perhaps most importantly, our findings show that modeling tools that are strict in EA standard conformance are perceived easier to learn and use by students, than merely illustrative tools with lenient or nonexistent conformance checks.

This Full Research Paper presents the most prominent query formulation errors in Structured Query Language (SQL), and maps these errors to their cognitive explanations. Understanding query formulation errors is a key to teaching SQL. more effectively. However, studies on what kind of errors novices struggle with are relatively scarce when compared to, for example, programming languages. Although committing errors is a crucial part in learning, some errors are relatively easy to fix, and their commonness is not necessarily an indication of their difficulty. Other errors, however, halt the learning process, and are never fixed by the query writer. Using a previously established error taxonomy and queries from four cohorts with a total of 987 students, we set out to identify common errors which students are unable to correct, i.e., errors that are likely to cause query formulation failures. Our results indicate that on a general level, logical errors are the most common cause for query formulation failures, while syntax and semantic errors are usually fixed by query writers. Although query concepts, for example, expressions, joins and grouping, have a strong influence on what types of errors are committed, some errors are common regardless of query concepts. Specifically, our results indicate that missing expressions, extraneous or omitted grouping columns, incorrect comparison operators, missing joins, and missing ordering columns are the most common errors that novices are unable to fix. Based on the results, we speculate on the reasons behind the most common persistent errors using previously identified cognitive explanations. Finally, we present that solutions for mitigating the causes behind query formulation errors are already available. In order to more effectively teach query formulation, educators should emphasize natural language patterns, query planning, and increasingly ambiguous exercises.

This work-in-progress research investigates teacher-student communication via Learning Management Systems (LMS) in highly populated courses. An LMS called TIM (The Interactive Material) includes a specific commenting technology that attempts to make teacher-student dialog effortless. The research goal is to explore students' willingness to use the technology and identify patterns of usage. To these ends, a survey with both Likert and open-ended questions was issued to CS1 and CS2 students. A favorable student evaluation was observed while several critical viewpoints that inform technology development were revealed. We noticed that besides appreciating the possibility of making comments, many students found benefit from peripheral participation without being active in commenting themselves. Informal communication appared to be preferred, and the commenting technology was considered second to best channel in this regard, following face-to-face interaction. The results are discussed in the light of Transactional Distance Theory and related literature to inform basic research.

Structured Query Language (SQL) skills are crucial in software engineering and computer science. However, teaching SQL effectively requires both pedagogical skill and considerable knowledge of the language. Educators and scholars have proposed numerous considerations for the betterment of SQL education, yet these considerations may be too numerous and scattered among different fora for educators to find and internalize, as no systematic mappings or literature reviews regarding SQL education have been conducted. The two main goals of this mapping study are to provide an overview of educational SQL research topics, research types, and publication fora, and to collect and propagate SQL teaching practices for educators to utilize. Additionally, we present a short future research agenda based on insights from the mapping process. We conducted a systematic mapping study complemented by snowballing techniques to identify applicable primary studies. We classified the primary studies according to research type and utilized directed content analysis to classify the primary studies by their topic. Out of our selected 89 primary studies, we identified six recurring topics: (i) student errors in query formulation; (ii) characteristics and presentation of the exercise database; (iii) specific and (iv) non-specific teaching approach suggestions; (v) patterns and visualization; and (vi) easing teacher workload. We list 66 teaching approaches the primary studies argued for (and in some cases against). For researchers, we provide a systematic map of educational SQL research and future research agenda. For educators, we present an aggregated body of knowledge on teaching practices in SQL education over a time frame of 30 years. In conclusion, we suggest that replication studies, studies on advanced SQL concepts, and studies on aspects other than data retrieval are needed to further educational SQL research.

In the process of learning a new computer language, writing erroneous statements is part of the learning experience. However, some errors persist throughout the query writing process and are never corrected. Structured Query Language (SQL) consists of a number of different concepts such as expressions, joins, grouping and ordering, all of which by nature invite different possible errors in the query writing process. Furthermore, some of these errors are relatively easy for a student to fix when compared to others. Using a data set from three student cohorts with the total of 744 students, we set out to explore which types of errors are persistent, i.e., more likely to be left uncorrected by the students. Additionally, based on the results, we contemplate which types of errors different query concepts seem to invite. The results show that syntax and semantic errors are less likely to persist than logical errors and complications. We expect that the results will help us understand which kind of errors students struggle with, and e.g., help teachers generate or choose more appropriate data for students to use when learning SQL.

SQL is taught in almost all university level database courses, yet SQL has received relatively little attention in educational research. In this study, we present a database management system independent categorization of SQL query errors that students make in an introductory database course. We base the categorization on previous literature, present a class of logical errors that has not been studied in detail, and review and complement these findings by analyzing over 33,000 SQL queries submitted by students. Our analysis verifies error findings presented in previous literature and reveals new types of errors, namely logical errors recurring in similar manners among different students. We present a listing of fundamental SQL query concepts we have identified and based our exercises on, a categorization of different errors and complications, and an operational model for designing SQL exercises.