Cited by 88Teaching and Learning ProgrammingEducational Games and GamificationEducational Robotics and Engineering

Fun in Making: Understanding the experience of fun and learning through curriculum-based Making in the elementary school classroom

Open access
Chu, Sharon Lynn, Angello, Genna, Saenz, Michael, Quek, Francis·2017·Entertainment Computing

Integrity signals

Registry

Crossref · 10.1016/j.entcom.2016.08.007

Cites

35 of 35 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?

Cites

Flagged first, then by citations

Cited by

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Authors
Chu, S. L., Angello, G., Saenz, M., & Quek, F.
Affiliations
Texas A&M University (US)
Corresponding author
Chu, Sharon Lynn
Venue
Entertainment Computing
Publisher
Elsevier BV
Published
2017-01
Volume
18
Pages
31-40
ISSN
1875-9521 (ISSN-L)
OpenAlex
W2517462758
Cited by
84
References
35
Citations by year
2026: 2 · 2025: 6 · 2024: 8 · 2023: 8 · 2022: 11 · 2021: 20 · 2020: 14 · 2019: 10 · 2018: 2 · 2017: 2 · 2016: 1
FWCI
6.67
Type
journal article
Language
en
Open access
Locations
Entertainment Computing (acceptedVersion)
Funders
NSF (CNS-1439614, DRL-1433770)
Topics
  • Physical Sciences › Computer Science › Computer Science Applications › Teaching and Learning Programming
  • Social Sciences › Psychology › Developmental and Educational Psychology › Educational Games and Gamification
  • Physical Sciences › Computer Science › Artificial Intelligence › Educational Robotics and Engineering
Keywords
making, fun, learning, elementary school, science education, children's experiences

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 ›

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