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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. 

We analyze submissions for homework assignments of 527 students in an upper-level database course offered at the University of Illinois at Urbana-Champaign. The ability to query databases is becoming a crucial skill for technology professionals and academics. Although we observe a large demand for teaching database skills, there is little research on database education. Also, despite the industry's continued demand for NoSQL databases, we have virtually no research on the matter of how students learn NoSQL databases, such as MongoDB. In this paper, we offer an in-depth analysis of errors committed by students working on MongoDB homework assignments over the course of two semesters. We show that as students use more advanced MongoDB operators, they make more Reference errors. Additionally, when students face a new functionality of MongoDB operators, such as texttt$group operator, they usually take time to understand it but do not make the same errors again in later problems. Finally, our analysis suggests that students struggle with advanced concepts for a comparable amount of time. Our results suggest that instructors should allocate more time and effort for the discussed topics in our paper. 

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.