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Analyzing Patterns in Student SQL Solutions via Levenshtein Edit Distance

Authors:

Sophia Yang,

Ziyuan Wei,

Geoffrey L. Herman,

Abdussalam AlawiniAuthors Info & Claims

L@S '21: Proceedings of the Eighth ACM Conference on Learning @ Scale

Pages 323 - 326

https://doi.org/10.1145/3430895.3460979

Published: 08 June 2021 Publication History

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Abstract

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.

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Structured Query Language (SQL) is highly structured and presents diverse ways for learners to acquire this skill, including data scientists and professionals who must interact with databases. Despite the significance of SQL to related fields, little research has been done to understand how students learn SQL as they work on assignments. We analyze students' SQL submissions to homework problems of the Database Systems course at the University of Illinois at Urbana-Champaign. For each student, we compute the Levenshtein Edit Distances between each current and final submission to understand how students reached their final solution by overcoming any obstacles in their learning process. Our system visualizes the edit distances between students' submissions to a SQL problem, enabling instructors to identify 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.

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References

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Guo QLi HZhu S(2024)Understanding the Characteristics of Students’ Behavioral Processes in Solving Computational Thinking Problems Based on the Behavioral SequencesJournal of Educational Computing Research10.1177/07356331241251397Online publication date: 29-Apr-2024
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Yang SHerman GAlawini AAivaloglou EFletcher GMiedema D(2023)Mining SQL Problem Solving Patterns using Advanced Sequence Processing AlgorithmsProceedings of the 2nd International Workshop on Data Systems Education: Bridging education practice with education research10.1145/3596673.3596973(37-43)Online publication date: 23-Jun-2023
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Li ZYang SCunningham KAlawini A(2023)Assessing Student Learning Across Various Database Query Languages2023 IEEE Frontiers in Education Conference (FIE)10.1109/FIE58773.2023.10343409(1-9)Online publication date: 18-Oct-2023
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Published In

L@S '21: Proceedings of the Eighth ACM Conference on Learning @ Scale

June 2021

380 pages

ISBN:9781450382151

DOI:10.1145/3430895

General Chair:
Christoph Meinel
Hasso Plattner Institute for Digital Engineering Potsdam, Germany
,
Program Chairs:
Mar Pérez-Sanagustín
Paul Sabatier University Toulouse III, France
,
Marcus Specht
TU Delft, Netherlands
,
Amy Ogan
Carnegie Mellon University, USA

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Published: 08 June 2021

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L@S '21: Eighth (2021) ACM Conference on Learning @ Scale

June 22 - 25, 2021

Virtual Event, Germany

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Guo QLi HZhu S(2024)Understanding the Characteristics of Students’ Behavioral Processes in Solving Computational Thinking Problems Based on the Behavioral SequencesJournal of Educational Computing Research10.1177/07356331241251397Online publication date: 29-Apr-2024
https://doi.org/10.1177/07356331241251397
Yang SHerman GAlawini AAivaloglou EFletcher GMiedema D(2023)Mining SQL Problem Solving Patterns using Advanced Sequence Processing AlgorithmsProceedings of the 2nd International Workshop on Data Systems Education: Bridging education practice with education research10.1145/3596673.3596973(37-43)Online publication date: 23-Jun-2023
https://dl.acm.org/doi/10.1145/3596673.3596973
Li ZYang SCunningham KAlawini A(2023)Assessing Student Learning Across Various Database Query Languages2023 IEEE Frontiers in Education Conference (FIE)10.1109/FIE58773.2023.10343409(1-9)Online publication date: 18-Oct-2023
https://doi.org/10.1109/FIE58773.2023.10343409
Yang SHerman GAlawini A(2023)Comparison of Student Learning Outcomes Among SQL Problem-Solving Patterns2023 IEEE Frontiers in Education Conference (FIE)10.1109/FIE58773.2023.10343395(1-9)Online publication date: 18-Oct-2023
https://doi.org/10.1109/FIE58773.2023.10343395
Yang SLi ZHerman GCunningham KAlawini A(2023)Uncovering Patterns of SQL Errors in Student Assignments: A Comparative Analysis of Different Assignment Types2023 IEEE Frontiers in Education Conference (FIE)10.1109/FIE58773.2023.10343207(01-09)Online publication date: 18-Oct-2023
https://doi.org/10.1109/FIE58773.2023.10343207
Yang SHerman GAlawini A(2022)Analyzing Student SQL Solutions via Hierarchical Clustering and Sequence Alignment ScoresProceedings of the 1st International Workshop on Data Systems Education10.1145/3531072.3535319(10-15)Online publication date: 12-Jun-2022
https://dl.acm.org/doi/10.1145/3531072.3535319

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