Insights from Student Solutions to SQL Homework Problems

Poulsen, Seth; Butler, Liia; Alawini, Abdussalam; Herman, Geoffrey L.

doi:10.1145/3341525.3387391

Cited by 15 publications

(3 citation statements)

References 17 publications

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“…Existing work in teaching SQL to undergraduates focuses on how students learn SQL [4], [29] and the types of semantic and syntax errors made [3], [23], [24]. Migler and Dekhtyar [4] break down the exercises students solve in an undergraduate databases course around their primary concept, and find that joins and subqueries are the most challenging.…”

Section: Re L a T E D Wo R Kmentioning

confidence: 99%

“…Poulsen, et al [29] study SQL queries students write in an upper-level databases course and find persistent issues with not just difficult semantic concepts such as nested queries and grouping, but syntax errors. Ahadi, et al [23] consider only syntax errors; we observe that students make significantly more semantic errors than syntax errors.…”

Section: Re L a T E D Wo R Kmentioning

confidence: 99%

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SQLRepair: Identifying and Repairing Mistakes in Student-Authored SQL Queries

Presler-Marshall

Heckman

Stolee

2021

2021 IEEE/ACM 43rd International Conference on Software Engineering: Software Engineering Education and Training (ICSE-SEET)

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Computer science educators seek to understand the types of mistakes that students make when learning a new (programming) language so that they can help students avoid those mistakes in the future. While educators know what mistakes students regularly make in languages such as C and Python, students struggle with SQL and regularly make mistakes when working with it. We present an analysis of mistakes that students made when first working with SQL, classify the types of errors introduced, and provide suggestions on how to avoid them going forward. In addition, we present an automated tool, SQLRepair, that is capable of repairing errors introduced by undergraduate programmers when writing SQL queries. Our results show that students find repairs produced by our tool comparable in understandability to queries written by themselves or by other students, suggesting that SQL repair tools may be useful in an educational context. We also provide to the community a benchmark of SQL queries written by the students in our study that we used for evaluation of SQLRepair.

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Section: Re L a T E D Wo R Kmentioning

confidence: 99%

Section: Re L a T E D Wo R Kmentioning

confidence: 99%

SQLRepair: Identifying and Repairing Mistakes in Student-Authored SQL Queries

Presler-Marshall

Heckman

Stolee

2021

2021 IEEE/ACM 43rd International Conference on Software Engineering: Software Engineering Education and Training (ICSE-SEET)

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“…Brass and Goldberg were the first to extend the work on errors of Reisner, Welty and Stemple by introducing logical errors: semantic errors that produce a -by definition incorrect-result, such as an empty result table, because of a contradictory WHERE clause [3]. Other recent research calculates query formulation success rates [4,9], or creates more specific lists of errors of a certain type: syntactic [1], semantic [2] or both [11,14]. Another newly introduced category is that of complications: queries that give the correct result but contain unnecessary elements [16].…”

Section: Introductionmentioning

confidence: 99%

Exploring the Prevalence of SQL Misconceptions: a Study Design

Miedema

Aivaloglou

Fletcher

2021

Proceedings of the 21st Koli Calling International Conference on Computing Education Research

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The Structured Query Language (SQL) is an established language for data manipulation in relational databases. It is widely used in industry, and therefore part of the typical Computer Science curriculum. From the large amounts of mistakes higher education students make while learning and using SQL, we know that this language is not easy to learn. Various researchers have examined the types of mistakes SQL novices make, and recently, the first step towards understanding the underlying reasons for these mistakes has been made. In this poster abstract, we propose a study to examine the prevalence of these origins, also called misconceptions. We hope the Computer Science Education community will help us reflect on and strengthen our methodology, and ultimately, our findings.

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