Social Constructivism—Jerome Bruner
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
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?
ChatGPT's output details the benefits of inquiry-based, student-centred models of science education, stating that this type of teaching has been shown to be more engaging and can lead to better retention.
“The output also details the benefits of inquiry-based, student-centred models of science education (Rannikmäe et al., 2020), stating that this type of teaching has been shown to be more engaging for students and can lead to better retention of the material.”
From Examining Science Education in ChatGPT: An Exploratory Study of Generative Artificial Intelligence (2023) · cites this paper as Rannikmäe et al., 2020
The citation lists haven’t been fetched yet
References 23 · Cited by 105
- Authors
- Rannikmäe, M., Holbrook, J., & Soobard, R.
- Affiliations
- University of Tartu (EE)
- Corresponding author
- Holbrook, Jack
- Venue
- Springer Texts in Education
- Publisher
- Springer International Publishing
- Published
- 2020
- Pages
- 259-275
- ISSN
- 2366-7672 (ISSN-L)
- 2366-7680
- OpenAlex
- W3085432084
- Cited by
- 105
- References
- 23
- Citations by year
- 2026: 13 · 2025: 25 · 2024: 28 · 2023: 21 · 2022: 14 · 2021: 4
- FWCI
- 57.41
- Type
- book chapter
- Language
- en
- Open access
- closed
- Locations
- Springer texts in education (publishedVersion)
- Topics
- Social Sciences › Education › Science Education and Pedagogy
- Social Sciences › Sociology and Political Science › Educational Theory and Curriculum Studies
- Social Sciences › Psychology › Developmental and Educational Psychology › Innovative Teaching and Learning Methods
- Keywords
- social constructivism, constructivism, Bruner, cooperative learning, problem solving, curriculum change, contextualization, science for all, student participation in curriculum design, teacher role, science education
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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