Students' syntactic mistakes in writing seven different types of SQL queries and its application to predicting students' success

Alireza Ahadi, Vahid Behbood, Arto Vihavainen, Julia Prior, Raymond Lister

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contribution

22 Citations (Scopus)

Abstract

The computing education community has studied extensively the errors of novice programmers. In contrast, little attention has been given to student's mistake in writing SQL statements. This paper represents the first large scale quantitative analysis of the student's syntactic mistakes in writing different types of SQL queries. Over 160 thousand snapshots of SQL queries were collected from over 2000 students across eight years. We describe the most common types of syntactic errors that students make. We also describe our development of an automatic classifier with an overall accuracy of 0.78 for predicting student performance in writing SQL queries.

Original languageEnglish
Title of host publicationSIGCSE 2016
Subtitle of host publicationProceedings of the 47th ACM Technical Symposium on Computing Science Education
Place of PublicationNew York, NY
PublisherAssociation for Computing Machinery, Inc
Pages401-406
Number of pages6
ISBN (Electronic)9781450338561
ISBN (Print)9781450336857
DOIs
Publication statusPublished - 2016
Externally publishedYes
EventACM Technical Symposium on Computing Science Education (47th : 2016) - Memphis, United States
Duration: 2 Mar 20165 Mar 2016

Conference

ConferenceACM Technical Symposium on Computing Science Education (47th : 2016)
Abbreviated titleSIGCSE 2016
CountryUnited States
CityMemphis
Period2/03/165/03/16

Keywords

  • online assessment
  • databases
  • SQL queries
  • machine learning

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  • Cite this

    Ahadi, A., Behbood, V., Vihavainen, A., Prior, J., & Lister, R. (2016). Students' syntactic mistakes in writing seven different types of SQL queries and its application to predicting students' success. In SIGCSE 2016: Proceedings of the 47th ACM Technical Symposium on Computing Science Education (pp. 401-406). New York, NY: Association for Computing Machinery, Inc. https://doi.org/10.1145/2839509.2844640