Towards a Transforming Constructivism: Understanding Learners' Meanings and the Messages of Learning Environments
Bibliographic record
Abstract
This article is based on the ideas and material presented at the invitation to deliver the 17 u, Annual University of Calgary Faculty of Education Annual Distinguished Lecture. This lecture honors the work of a member of Faculty whose research contributions have been judged to contribute significant new insight in the field of Education. The article presents the foundations of research and thinking that have inspired work to deepen knowledge about the value and use of constructivist approaches in research, teaching, and learning. The article deepens the discussion by presenting additional ideas designed to produce a transforming constructivism. A transforming constructivism goes beyond taking into account learner conceptions or ideas in the teaching/learning dialogue. It incorporates thinking about the cultural and social messages presented within the environments of learning that also must be taken into account to build a more complete story of the ways that the learner constructs meaning. illustrative dialogue on children learning about the topic light is presented from my book, What Children Bring to Light: A Constructivist Perspective on Children's Learning in Science, (1994). The article presents findings and further work that has extended and deepened understanding by featuring the messages of science learning environments in the account of learner construction of ideas.
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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 | 0.019 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.005 | 0.065 |
| Scholarly communication | 0.021 | 0.030 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".