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1. A Quantitative Analysis of Student Solutions to Graph Database Problems

   https://doi.org/10.1145/3430665.3456314  

   Chen, Mei; Poulsen, Seth; Alkhabaz, Ridha; Alawini, Abdussalam (June 2021, Proceedings of the 26th ACM Conference on Innovation and Technology in Computer Science Education)

   null (Ed.)

   As data grow both in size and in connectivity, the interest to use graph databases in the industry has been proliferating. However, there has been little research on graph database education. In response to the need to introduce college students to graph databases, this paper is the first to analyze students' errors in homework submissions of queries written in Cypher, the query language for Neo4j---the most prominent graph database. Based on 40,093 student submissions from homework assignments in an upper-level computer science database course at one university, this paper provides a quantitative analysis of students' learning when solving graph database problems. The data shows that students struggle the most to correctly use Cypher's WITH clause to define variable names before referencing in the WHERE clause and these errors persist over multiple homework problems requiring the same techniques, and we suggest a further improvement on the classification of syntactic errors. 

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2. Student's Learning Challenges with Relational, Document, and Graph Query Languages

   https://doi.org/10.1145/3596673.3596976  

   Alkhabaz, Ridha; Li, Zepei; Yang, Sophia; Alawini, Abdussalam (June 2023, DataEd '23: Proceedings of the 2nd International Workshop on Data Systems Education: Bridging education practice with education research)

   As the need for database management skills continues to grow, there is an increasing demand for education on database models and their corresponding query languages. However, the body of research addressing the difficulties encountered by novice learners when working with query languages in database systems is still limited. In this study, we examined over 357215 submissions from 462 students’ homework problems during the Fall 2022 semester covering concepts in SQL, MongoDB, and Neo4j query languages. Our analysis through breaking down the most common syntax errors by concept confirms previous research and demonstrates that certain data operations pose challenges to students across different database systems. Specifically, we found that aggregation operations and Join operations were particularly difficult for students, which aligns with prior SQL education research. Therefore, we suggest that instructors consider incorporating visuals and assignments that enable students to build mental models for different database models. 

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3. 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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4. Insights from Student Solutions to SQL Homework Problems

   https://doi.org/10.1145/3341525.3387391  

   Poulsen, Seth; Butler, Liia; Alawini, Abdussalam; Herman, Geoffrey L. (June 2020, Proceedings of the 2020 ACM Conference on Innovation and Technology in Computer Science Education)

   null (Ed.)

   We analyze the submissions of 286 students as they solved Structured Query Language (SQL) homework assignments for an upper-level databases course. Databases and the ability to query them are becoming increasingly essential for not only computer scientists but also business professionals, scientists, and anyone who needs to make data-driven decisions. Despite the increasing importance of SQL and databases, little research has documented student difficulties in learning SQL. We replicate and extend prior studies of students' difficulties with learning SQL. Students worked on and submitted their homework through an online learning management system with support for autograding of code. Students received immediate feedback on the correctness of their solutions and had approximately a week to finish writing eight to ten queries. We categorized student submissions by the type of error, or lack thereof, that students made, and whether the student was eventually able to construct a correct query. Like prior work, we find that the majority of student mistakes are syntax errors. In contrast with the conclusions of prior work, we find that some students are never able to resolve these syntax errors to create valid queries. Additionally, we find that students struggle the most when they need to write SQL queries related to GROUP BY and correlated subqueries. We suggest implications for instruction and future research. 

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5. Does Starting Deep Learning Homework Earlier Improve Grades?

   Raff, Edward; Matuszek, Cynthia (October 2023, Proceedings of the European Conference on Artificial Intelligence (ECAI))

   Intuitively, students who start a homework assignment earlier and spend more time on it should receive better grades on the assignment. However, existing literature on the impact of time spent on homework is not clear-cut and comes mostly from K-12 education. It is not clear that these prior studies can inform coursework in deep learning due to differences in demographics, as well as the computational time needed for assignments to be completed. We study this problem in a post-hoc study of three semesters of a deep learning course at the University of Maryland, Baltimore County (UMBC), and develop a hierarchical Bayesian model to help make principled conclusions about the impact on student success given an approximate measure of the total time spent on the homework, and how early they submitted the assignment. Our results show that both submitting early and spending more time positively relate with final grade. Surprisingly, the value of an additional day of work is apparently equal across students, even when some require less total time to complete an assignment. 

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