Authors:
Paul Christ
1
;
Torsten Munkelt
2
and
Jörg M. Haake
1
Affiliations:
1
Faculty of Computer Science and Mathematics, Distance University Hagen, Universitätsstraße 11, Hagen, Germany
;
2
Faculty of Mathematics and Computer Science, HTWD, Friedrich-List-Platz 1, Saxony, Germany
Keyword(s):
SQL, Relational Database, AIG, Automatic Item Generation, Knowledge Graph, Large Language Model.
Abstract:
SQL is still one of the most popular languages used in todays industry across many fields. Poorly written SQL remains one of the root causes of performance issues. Thus, achieving a high level of mastery for SQL is important. Achieving mastery requires practicing with many SQL assessment items of varying complexity and content. The manual creation of such items is very labor-some and expensive. Automatic item generation reduces the cost of item creation. This paper proposes an approach for automatically generating SQL-query items of varying complexity, content, and human-like natural language problem statements (NLPS). The approach is evaluated by human raters regarding the complexity and plausibility of the generated SQL-queries and the preference between two alternative NLPS. The results show agreement on the plausibility of the generated SQL-queries, while the complexity and the NLPS preference show higher variance.