@inproceedings{da9bc8fc0c4249699dbb3eb48e64da5f,
title = "Echelon: An AI Tool for Clustering Student-Written SQL Queries",
abstract = "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.",
keywords = "Databases, Education, Visualization",
author = "Matthew Weston and Haorong Sun and Herman, {Geoffrey L.} and Hisham Benotman and Abdussalam Alawini",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 2021 IEEE Frontiers in Education Conference, FIE 2021 ; Conference date: 13-10-2021 Through 16-10-2021",
year = "2021",
doi = "10.1109/FIE49875.2021.9637203",
language = "English (US)",
series = "Proceedings - Frontiers in Education Conference, FIE",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "Proceedings - 2021 IEEE Frontiers in Education Conference, FIE 2021",
address = "United States",
}