Theoretical perspectives on learning in an informal setting
Bibliographic record
Abstract
Abstract Research into learning in informal settings such as museums has been in a formative state during the past decade, and much of that research has been descriptive and lacking a theory base. In this article, it is proposed that the human constructivist view of learning can guide research and assist the interpretation of research data because it recognizes an individual's prior knowledge and active involvement in knowledge construction during a museum visit. This proposal is supported by reference to the findings of a previously reported interpretive case study, which included concept mapping and semistructured interviews, of the knowledge transformations of three Year 7 students who had participated in a class visit to a science museum and associated postvisit activities. The findings from that study are shown in this report to be consistent with the human constructivist view of learning in that for all three students, learning was found to be at times incremental and at other times to involve substantial restructuring of knowledge. Thus, we regard that the human constructivist view of learning has much merit and utility for researchers investigating the development of knowledge and understanding emergent from experiences in informal settings. The theoretical and practical implications of these findings for teachers and staff of museums and similar institutions are also discussed. © 2003 Wiley Periodicals, Inc. J Res Sci Teach 40: 177–199, 2003
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.005 | 0.038 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".