TY - GEN
T1 - Analyzing Patterns in Student SQL Solutions via Levenshtein Edit Distance
AU - Yang, Sophia
AU - Wei, Ziyuan
AU - Herman, Geoffrey L.
AU - Alawini, Abdussalam
N1 - Publisher Copyright:
© 2021 Owner/Author.
PY - 2021/6/8
Y1 - 2021/6/8
N2 - Structured Query Language (SQL), the standard language for relational database management systems, is an essential skill for software developers, data scientists, and professionals who need to interact with databases. SQL is highly structured and presents diverse ways for learners to acquire this skill. However, despite the significance of SQL to other related fields, little research has been done to understand how students learn SQL as they work on homework assignments. In this paper, we analyze students' SQL submissions to homework problems of the Database Systems course offered at the University of Illinois at Urbana-Champaign. For each student, we compute the Levenshtein Edit Distances between every submission and their final submission to understand how students reached their final solution and how they overcame any obstacles in their learning process. Our system visualizes the edit distances between students' submissions to a SQL problem, enabling instructors to identify interesting learning patterns and approaches. These findings will help instructors target their instruction in difficult SQL areas for the future and help students learn SQL more effectively.
AB - Structured Query Language (SQL), the standard language for relational database management systems, is an essential skill for software developers, data scientists, and professionals who need to interact with databases. SQL is highly structured and presents diverse ways for learners to acquire this skill. However, despite the significance of SQL to other related fields, little research has been done to understand how students learn SQL as they work on homework assignments. In this paper, we analyze students' SQL submissions to homework problems of the Database Systems course offered at the University of Illinois at Urbana-Champaign. For each student, we compute the Levenshtein Edit Distances between every submission and their final submission to understand how students reached their final solution and how they overcame any obstacles in their learning process. Our system visualizes the edit distances between students' submissions to a SQL problem, enabling instructors to identify interesting learning patterns and approaches. These findings will help instructors target their instruction in difficult SQL areas for the future and help students learn SQL more effectively.
KW - SQL
KW - database education
KW - levenshtein edit distance
KW - online assessment
UR - http://www.scopus.com/inward/record.url?scp=85108059570&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85108059570&partnerID=8YFLogxK
U2 - 10.1145/3430895.3460979
DO - 10.1145/3430895.3460979
M3 - Conference contribution
AN - SCOPUS:85108059570
T3 - L@S 2021 - Proceedings of the 8th ACM Conference on Learning @ Scale
SP - 323
EP - 326
BT - L@S 2021 - Proceedings of the 8th ACM Conference on Learning @ Scale
PB - Association for Computing Machinery
T2 - 8th Annual ACM Conference on Learning at Scale, L@S 2021
Y2 - 22 June 2021 through 25 June 2021
ER -