Cited by 89Teaching and Learning ProgrammingCareer Development and DiversityGender and Technology in Education

Making in the classroom: Longitudinal evidence of increases in self-efficacy and STEM possible selves over time

Schlegel, Rebecca J., Chu, Sharon L., Chen, Kaiyuan, Deuermeyer, Elizabeth, Christy, Andrew G., Quek, Francis·2019·Computers & Education

Integrity signals

Registry

Crossref · 10.1016/j.compedu.2019.103637

Cites

7 of 7 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

Sorted by citations

Authors
Schlegel, R. J., Chu, S. L., Chen, K., Deuermeyer, E., Christy, A. G., & Quek, F.
Affiliations
  • Texas A&M University (US)
  • University of Florida (US)
Corresponding author
Schlegel, Rebecca J.
Venue
Computers & Education
Publisher
Elsevier BV
Published
2019-12
Volume
142
Pages
103637
ISSN
0360-1315 (ISSN-L)
OpenAlex
W2963220664
Cited by
89
References
7
Citations by year
2026: 7 · 2025: 16 · 2024: 14 · 2023: 21 · 2022: 14 · 2021: 9 · 2020: 8
FWCI
8.40
Type
journal article
Language
en
Open access
closed
Locations
Computers & Education (publishedVersion)
Funders
National Science Foundation
Topics
  • Physical Sciences › Computer Science › Computer Science Applications › Teaching and Learning Programming
  • Social Sciences › Safety Research › Career Development and Diversity
  • Social Sciences › Gender Studies › Gender and Technology in Education
Keywords
making, maker culture, makerspaces, self-efficacy, science identity, science interest, STEM career interest, underrepresented groups in STEM, elementary school students, hands-on learning, curriculum integration, longitudinal effects

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