13 research outputs found
Toward a Fundamental Understanding of SQL Education
Get PDFRelational 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
Curriculum analysis for data systems education.
Get PDFThe 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
Data systems education: curriculum recommendations, course syllabi, and industry needs.
Get PDFData systems have been an important part of computing curricula for decades, and an integral part of data-focused industry roles such as software developers, data engineers, and data scientists. However, the field of data systems encompasses a large number of topics ranging from data manipulation and database distribution to creating data pipelines and data analytics solutions. Due to the slow nature of curriculum development, it remains unclear (i) which data systems topics are recommended across diverse higher education curriculum guidelines, (ii) which topics are taught in higher education data systems courses, and (iii) which data systems topics are actually valued in data-focused industry roles. In this study, we analyzed computing curriculum guidelines, course contents, and industry needs regarding data systems to uncover discrepancies between them. Our results show, for example, that topics such as data visualization, data warehousing, and semi-structured data models are valued in industry, yet seldom taught in courses. This work allows professionals to further align curriculum guidelines, higher education, and data systems industry to better prepare students for their working life by focusing on relevant skills in data systems education
Data systems education : curriculum recommendations, course syllabi, and industry needs
Get PDFData systems have been an important part of computing curricula for decades, and an integral part of data-focused industry roles such as software developers, data engineers, and data scientists. However, the field of data systems encompasses a large number of topics ranging from data manipulation and database distribution to creating data pipelines and data analytics solutions. Due to the slow nature of curriculum development, it remains unclear (i) which data systems topics are recommended across diverse higher education curriculum guidelines, (ii) which topics are taught in higher education data systems courses, and (iii) which data systems topics are actually valued in data-focused industry roles. In this study, we analyzed computing curriculum guidelines, course contents, and industry needs regarding data systems to uncover discrepancies between them. Our results show, for example, that topics such as data visualization, data warehousing, and semi-structured data models are valued in industry, yet seldom taught in courses. This work allows professionals to further align curriculum guidelines, higher education, and data systems industry to better prepare students for their working life by focusing on relevant skills in data systems education
Students' Perceptions on Engaging Database Domains and Structures
No full textSeveral 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.peerReviewe
So many brackets! An analysis of how SQL learners (mis)manage complexity during query formulation
No full textThe 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. In this paper we identify another factor: self-inflicted query complexity, where users hinder their own problem solving process. We analyse 8K intermediate and final student attempts to six SQL exer-cises, approaching complexity from four perspective: correctness, execution order, edit distance and query intricacy. Through our analyses, we find that our students are hindered in their query formulation process by mismanaging complexity through writing overly elaborate queries containing unnecessary elements, overusing brackets and nesting, and incrementally building queries with persistent errors.</p
