Insights from Student Solutions to MongoDB Homework Problems

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Abstract

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.

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APA

Alkhabaz, R., Poulsen, S., Chen, M., & Alawini, A. (2021). Insights from Student Solutions to MongoDB Homework Problems. In Annual Conference on Innovation and Technology in Computer Science Education, ITiCSE (pp. 276–282). Association for Computing Machinery. https://doi.org/10.1145/3430665.3456308

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