Why Aren't They Getting This? Working through the regressive myths of constructivist pedagogy
Bibliographic record
Abstract
Through several collaborative inquiries with teachers in elementary and middle schools, we have noticed a troublesome trend: teachers have become familiar with many of the key terms and catchphrases of various constructivist discourses, yet they tend to be relatively unfamiliar with the developments in epistemology that have driven the rapid emergence of these vocabularies. In consequence, our efforts to invite teachers into current discussions of cognition have often been frustrated and frustrating. We argue that this situation is in large part due to two circumstances. First, the vocabularies chosen by constructivists are often too readily aligned with commonsense understandings of personal knowing and collective knowledge. Second, and closely related, educational theorists and researchers have not always been sufficiently attentive to the contexts of their work. As such, rather than prompting a break from deeply entrenched habits of thinking, constructivist discourses have often been co-opted to support renewed and regressive embraces of Platonic and Cartesian assumptions. Somewhat ironically, then, the work of many educational theorists and researchers appears to be carried out in ignorance of the tentative and participatory dynamics that are argued to be at the root of cognitive processes.
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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 | 0.089 | 0.108 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.011 | 0.142 |
| Scholarly communication | 0.026 | 0.040 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.006 | 0.030 |
| 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".