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A Quantitative Analysis of Student Solutions to Graph Database Problems

Authors:

Ridha Alkhabaz,

Abdussalam AlawiniAuthors Info & Claims

ITiCSE '21: Proceedings of the 26th ACM Conference on Innovation and Technology in Computer Science Education V. 1

Pages 283 - 289

https://doi.org/10.1145/3430665.3456314

Published: 26 June 2021 Publication History

Abstract

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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Cited By

Ehlers JMonga MLonati VBarendsen ESheard JPaterson J(2024)Teaching Multiple Data Models and Query LanguagesProceedings of the 2024 on Innovation and Technology in Computer Science Education V. 110.1145/3649217.3653628(234-240)Online publication date: 3-Jul-2024
https://dl.acm.org/doi/10.1145/3649217.3653628
Li ZYang SCunningham KAlawini A(2023)Assessing Student Learning Across Various Database Query Languages2023 IEEE Frontiers in Education Conference (FIE)10.1109/FIE58773.2023.10343409(1-9)Online publication date: 18-Oct-2023
https://doi.org/10.1109/FIE58773.2023.10343409
Fekete ARöhm U(2022)Teaching about Data and DatabasesACM SIGMOD Record10.1145/3552490.355250451:2(52-60)Online publication date: 29-Jul-2022
https://dl.acm.org/doi/10.1145/3552490.3552504

Index Terms

A Quantitative Analysis of Student Solutions to Graph Database Problems

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Published In

cover image ACM Conferences

ITiCSE '21: Proceedings of the 26th ACM Conference on Innovation and Technology in Computer Science Education V. 1

June 2021

611 pages

ISBN:9781450382144

DOI:10.1145/3430665

General Chairs:
Carsten Schulte
Paderborn University, Germany
,
Brett A. Becker
University College Dublin, Ireland
,
Program Chairs:
Monica Divitini
NTNU, Norway
,
Erik Barendsen
Radboud University & Open University, The Netherlands

Copyright © 2021 Owner/Author.

This work is licensed under a Creative Commons Attribution International 4.0 License.

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SIGCSE: ACM Special Interest Group on Computer Science Education

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 26 June 2021

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National Science Foundation

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ITiCSE 2021

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SIGCSE

ITiCSE 2021: 26th ACM Conference on Innovation and Technology in Computer Science Education

June 26 - July 1, 2021

Virtual Event, Germany

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Overall Acceptance Rate 552 of 1,613 submissions, 34%

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Cited By

Ehlers JMonga MLonati VBarendsen ESheard JPaterson J(2024)Teaching Multiple Data Models and Query LanguagesProceedings of the 2024 on Innovation and Technology in Computer Science Education V. 110.1145/3649217.3653628(234-240)Online publication date: 3-Jul-2024
https://dl.acm.org/doi/10.1145/3649217.3653628
Li ZYang SCunningham KAlawini A(2023)Assessing Student Learning Across Various Database Query Languages2023 IEEE Frontiers in Education Conference (FIE)10.1109/FIE58773.2023.10343409(1-9)Online publication date: 18-Oct-2023
https://doi.org/10.1109/FIE58773.2023.10343409
Fekete ARöhm U(2022)Teaching about Data and DatabasesACM SIGMOD Record10.1145/3552490.355250451:2(52-60)Online publication date: 29-Jul-2022
https://dl.acm.org/doi/10.1145/3552490.3552504

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