CONSTRUCTIVISM AND EDUCATION: MISUNDERSTANDINGS AND PEDAGOGICAL IMPLICATIONS
Bibliographic record
Abstract
Constructivism is a popular concept in contemporary teacher education programs. However, a genuine concern arises with the concept's application because many teachers and teacher educators claim that knowledge is constructed, without appreciating the epistemological and pedagogical implications such a claim entails. This article employs Phillips' (1995) Phillips, D. C. 1995. The good, the bad, and the ugly: The many faces of constructivism. Educational Researcher, 24(7): 5–12. [Crossref] , [Google Scholar] analytic framework that divides the pedagogical applications of constructivism into three distinct categories: the good, the bad, and the ugly. Reviewing the constructivist epistemologies of Dewey and Vygotsky also enables the exploration of how constructivism might inform both our understanding of the impediments students confront when learning new knowledge and our understanding of general constructivist pedagogical practices. The primary objective in this article is to provide teacher educators and teachers with a richer understanding of constructivism—its limitations and its strengths—while offering concrete pedagogical strategies for its classroom application.
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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 | 0.121 | 0.138 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.008 | 0.138 |
| Scholarly communication | 0.023 | 0.041 |
| Open science | 0.006 | 0.014 |
| Research integrity | 0.008 | 0.027 |
| Insufficient payload (model declined to judge) | 0.002 | 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".