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Title: A Quantitative Analysis of Student Solutions to Graph Database Problems
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.  more » « less
Award ID(s):
2021499
NSF-PAR ID:
10277378
Author(s) / Creator(s):
; ; ;
Date Published:
Journal Name:
Proceedings of the 26th ACM Conference on Innovation and Technology in Computer Science Education
Page Range / eLocation ID:
283 to 289
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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