Echelon: An AI Tool for Clustering Student-Written SQL Queries

Matthew Weston, Haorong Sun, Geoffrey L. Herman, Hisham Benotman, Abdussalam Alawini

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


As part of teaching SQL, instructors often rely on auto-grading systems for marking students' assignments. However, such systems lack essential insights into the approaches students use to solve these assignments, allowing subtle flaws in student intuition to go unseen. Further, manual analysis of students' code submissions ranges from costly to impossible, depending on the assessments' frequency. In this paper, we present a system capable of extracting features that instructors deem significant from students' SQL queries and using them to generate clusters that capture the key approaches taken. To supplement this, we project the extracted information to an interactive dashboard and demonstrate its usefulness in allowing database systems professors and teaching staff to quickly identify trends in students' solutions.

Original languageEnglish (US)
Title of host publicationProceedings - 2021 IEEE Frontiers in Education Conference, FIE 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665438513
StatePublished - 2021
Event2021 IEEE Frontiers in Education Conference, FIE 2021 - Lincoln, United States
Duration: Oct 13 2021Oct 16 2021

Publication series

NameProceedings - Frontiers in Education Conference, FIE
ISSN (Print)1539-4565


Conference2021 IEEE Frontiers in Education Conference, FIE 2021
Country/TerritoryUnited States


  • Databases
  • Education
  • Visualization

ASJC Scopus subject areas

  • Software
  • Education
  • Computer Science Applications


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