Hash Table Notional Machines: A Comparison of 2D and 3D Representations

Colleen Lewis, Craig S. Miller, Johan Jeuring, Janice L. Pearce, Andrew Petersen

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

Abstract

Background: Notional machines appear to be an essential aspect of computing education, but there are few papers that identify strengths and weaknesses of particular notional machines. Purpose: This article fills a gap in the notional machine literature by using a randomized controlled trial to compare the effectiveness of different notional machine representations. Methods: Our study used notional machines for two hash table algorithms: chaining and open addressing. Students were randomly assigned a video sequence using either 2D or 3D representations. Findings: We found minimal effect of 2D vs 3D representational form on students' learning and perceptions of helpfulness. Implications: Our paper provides an example of how educational research can inform the design and evaluation of notional machines.
Original languageEnglish
Title of host publicationSIGCSE Virtual 2024: Proceedings of the 2024 on ACM Virtual Global Computing Education Conference
PublisherAssociation for Computing Machinery
Pages109-115
DOIs
Publication statusPublished - 5 Dec 2024

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