CoChat
Sign Up
Cited by 32Teaching and Learning ProgrammingLogic, programming, and type systemsVisual and Cognitive Learning Processes

Engage Against the Machine: Rise of the Notional Machines as Effective Pedagogical Devices

Open access
Dickson, Paul E., Brown, Neil C. C., Becker, Brett A.·2020·Proceedings of the 2020 ACM Conference on Innovation and Technology in Computer Science Education
DOI 10.1145/3341525.3387404
Open this paper in CoChat — free account

Abstract

The term "the machine" is commonly used to refer to the complicated physical hardware running similarly complex software that ultimately executes programs. The idea that programmers write programs for a notional machine - an abstract model of an execution environment - not the machine itself, has risen to the point of gaining acceptance as a useful device in computing education. This has seeded a growing discussion about how explicitly utilizing notional machines in teaching can help students construct more accurate mental models, which is essential for learning programming. Much of the existing literature necessarily involves specific languages, visualization, and/or facilitating tools, and is not very accessible to many practitioners. Less focus has been put on how teachers can make explicit use of notional machines in their teaching. In this paper we describe notional machines and their use in a manner that is more accessible to a general educator audience in order to facilitate more effective computing education at all levels. We advocate explicitly delineating between visualization tools and the notional machines they depict, isolating and clarifying the notional machine so that it is conspicuous, apparent and useful. We present examples of how this approach can facilitate a more consistent method of teaching computing, and be used in more effective pedagogical practice for teaching computing.

Integrity signals

Registry

Crossref · 10.1145/3341525.3387404

Cites

38 of 38 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Generative AI is likely to alter how students conceptualise the computer and program execution.

    “It is also interesting, given how little is known about the true role of notional machines in the classroom [76], that generative AI is likely to alter how students conceptualise the computer and program execution.”

    From The Robots Are Here: Navigating the Generative AI Revolution in Computing Education (2023) · cites this paper as [76]

Cites

Flagged first, then by citations

Cited by

Sorted by citations

Related

Authors
Dickson, P. E., Brown, N. C. C., & Becker, B. A.
Affiliations
  • Ithaca College (US)
  • King's College London (GB)
  • University College Dublin (IE)
ORCID
Venue
Proceedings of the 2020 ACM Conference on Innovation and Technology in Computer Science Education
Publisher
ACM
Published
2020-06-15
Pages
159-165
DOI
10.1145/3341525.3387404
OpenAlex
W3033106251
Cited by
32
References
38
Citations by year
2026: 7 · 2025: 3 · 2024: 5 · 2023: 7 · 2022: 5 · 2021: 5
FWCI
4.93
Type
conference paper
Language
en
Open access
Licence
Locations
  • ACM Conference on Innovation and Technology in Computer Science Education (ITiCSE) (publishedVersion)
  • Research Repository UCD (University College Dublin) (publishedVersion, cc-by-nc-nd)
  • Digital Collections - Ithaca College Library (Ithaca College) (submittedVersion, public-domain)
  • Research Portal (King's College London) (submittedVersion, other-oa)
Topics
  • Physical Sciences › Computer Science › Computer Science Applications › Teaching and Learning Programming
  • Physical Sciences › Computer Science › Artificial Intelligence › Logic, programming, and type systems
  • Social Sciences › Psychology › Experimental and Cognitive Psychology › Visual and Cognitive Learning Processes
Keywords
notional machine, abstract machine model, programming education, mental models, visualization tools, teaching computing

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review