Cited by 719Teaching and Learning ProgrammingVisual and Cognitive Learning ProcessesData Visualization and Analytics

A Meta-Study of Algorithm Visualization Effectiveness

HUNDHAUSEN, CHRISTOPHER D., DOUGLAS, SARAH A., STASKO, JOHN T.·2002·Journal of Visual Languages & Computing

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1006/jvlc.2002.0237

Cites

1 of 1 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Cited by

Sorted by citations

Authors
HUNDHAUSEN, C. D., DOUGLAS, S. A., & STASKO, J. T.
Affiliations
  • University of Hawaiʻi at Mānoa (US)
  • University of Oregon (US)
  • Georgia Institute of Technology (US)
Venue
Journal of Visual Languages & Computing
Publisher
Elsevier BV
Published
2002-06
Volume
13
Issue
3
Pages
259-290
ISSN
1045-926X (ISSN-L)
OpenAlex
W2028868392
Cited by
719
References
1
Citations by year
2026: 21 · 2025: 21 · 2024: 23 · 2023: 16 · 2022: 18 · 2021: 29 · 2020: 24 · 2019: 60 · 2018: 22 · 2017: 18 · 2016: 33 · 2015: 43 · 2014: 27 · 2013: 32 · 2012: 17
FWCI
60.60
Type
journal article
Language
en
Open access
closed
Locations
Journal of Visual Languages & Computing (publishedVersion)
Topics
  • Physical Sciences › Computer Science › Computer Science Applications › Teaching and Learning Programming
  • Social Sciences › Psychology › Experimental and Cognitive Psychology › Visual and Cognitive Learning Processes
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Data Visualization and Analytics
Keywords
algorithm visualization, computer science education, educational effectiveness, learning theory, measurement of learning outcomes

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