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1. Analyzing Patterns in Student SQL Solutions via Levenshtein Edit Distance

   https://doi.org/10.1145/3430895.3460979  

   Yang, Sophia; Wei, Ziyuan; Herman, Geoffrey L.; Alawini, Abdussalam (June 2021, Proceedings of the Eighth ACM Conference on Learning @ Scale)

   null (Ed.)

   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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2. Building learned federated query optimizers

   Victor Giannakouris, Immanuel Trummer (September 2022, CEUR workshop proceedings)

   The goal of this thesis is to introduce a new design for building federated query optimizers, based on machine learning. We propose a modular and flexible architecture, allowing a federated query optimizer to integrate with any database system that supports SQL, with close-to-zero engineering effort. By observing the performance of the external systems, our optimizer learns and builds cost models on-the-fly, enabling federated query optimization with negligible communication with the external systems. To demonstrate the potential of this research plan, we present a prototype of our federated query optimizer built on top of Spark SQL. Our implementation effectively accelerates federated queries, achieving up to 7.5x better query execution times compared to the vanilla implementation of Spark SQL. 

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3. Mining SQL Problem Solving Patterns using Advanced Sequence Processing Algorithms

   https://doi.org/10.1145/3596673.3596973  

   Yang, Sophia; Herman, Geoffrey L.; Alawini, Abdussalam (June 2023, DataEd '23: Proceedings of the 2nd International Workshop on Data Systems Education: Bridging education practice with education research)

   SQL is a crucial language for managing relational database systems, and is an essential skill for individuals in roles such as researchers, developers, and business professionals who work with databases. However, learning SQL can be a challenge, presenting an opportunity to study the various methods students use to arrive at semantically equivalent SQL queries. In this study, we examined students’ SQL submissions to homework assignments in the Database Systems course offered to upper-level undergraduate and graduate students at the University of Illinois Urbana-Champaign during the Fall 2022 semester. Our goal was to understand how students arrive at SQL solutions and overcome challenges in the learning process by building on prior research on line chart visualizations that instructors can use to increase visibility on students who are struggling. However, a major limitation of this approach was the difficulty for instructors to sift through a large number of visuals representing each student’s performance on a SQL problem and generate action items at scale, especially when dealing with enrollments of over 700 students. To overcome this limitation, we developed a novel technique to generate textual representations of the student submission sequence using global sequence alignment scores and regular expression algorithms to further compact these submission sequences. This allows instructors to gain insights quickly, on an aggregate level, and in an automated manner, enabling them to identify students who may be struggling with SQL based on their submission sequence characteristics and take appropriate action to improve database education. Our study discovered common textual submission patterns and pattern elements, and we present our recommendations to instructors to improve database education based on these findings. 

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4. PolyFrame: a retargetable query-based approach to scaling dataframes

   https://doi.org/10.14778/3476249.3476281  

   Sinthong, Phanwadee; Carey, Michael J. (July 2021, Proceedings of the VLDB Endowment)

   null (Ed.)

   In the last few years, the field of data science has been growing rapidly as various businesses have adopted statistical and machine learning techniques to empower their decision-making and applications. Scaling data analyses to large volumes of data requires the utilization of distributed frameworks. This can lead to serious technical challenges for data analysts and reduce their productivity. AFrame, a data analytics library, is implemented as a layer on top of Apache AsterixDB, addressing these issues by providing the data scientists' familiar interface, Pandas Dataframe, and transparently scaling out the evaluation of analytical operations through a Big Data management system. While AFrame is able to leverage data management facilities (e.g., indexes and query optimization) and allows users to interact with a large volume of data, the initial version only generated SQL++ queries and only operated against AsterixDB. In this work, we describe a new design that retargets AFrame's incremental query formation to other query-based database systems, making it more flexible for deployment against other data management systems with composable query languages. 

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5. Are Large Language Models Smart Enough for SQL Tutoring and Assessment?

   Naha, K; Rubaiat, S; Jamil, H (July 2025, IEEE)

   The rise of Large Language Models (LLMs) as powerful knowledge-processing tools has sparked a wave of innovation in tutoring and assessment systems. Despite their well-documented limitations, LLMs offer unique capabilities that have been effectively harnessed for automated feedback generation and grading in intelligent learning environments. In this paper, we introduce {\em Project 360}, an experimental intelligent tutoring system designed for teaching SQL. Project 360 leverages the concept of {\em query equivalence} to assess the accuracy of student queries, using ChatGPT’s advanced natural language analysis to measure their semantic distance from a reference query. By integrating LLM-driven evaluation, Project 360 significantly outperforms traditional SQL tutoring and grading systems, offering more precise assessments and context-aware feedback. This study explores the feasibility and limitations of using ChatGPT as the analytical backbone of Project 360, evaluating its reliability for autonomous tutoring and assessment in database education. Our findings provide valuable insights into the evolving role of LLMs in education, highlighting their potential to revolutionize SQL learning while identifying areas for further refinement and improvement. 

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