Empowerment in Science Curriculum Development: A microdevelopmental approach
Bibliographic record
Abstract
This study characterizes how learning and teaching differs as the responsibility for choosing curriculum goals and the strategies to reach those goals shifts between teacher and the students. Three different pedagogical approaches were used with 125 seventh‐grade and eighth‐grade students. All three curricula focus on electromagnetism, and were taught by two teachers in different schools over a two‐week period. When students had control over the strategies employed to reach goals, their engagement stayed high. All three curricula advanced student understanding to some degree; however, large and significant gains were seen only for the pedagogy in which teachers set the specific learning goals and students had control over how to achieve them. Microdevelopment, a principle by which short‐term learning recapitulates the stages seen in long‐term developmental growth, is found to be a useful framework for curriculum development and for analyzing changes in student understanding. In general, initial “tinkering” activities are best followed by attempts at representing phenomena, only then to be followed by abstract conceptualization. On balance, we find that students benefit most from freedom to control the procedures that they generate in response to well‐structured goals presented by the teacher.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.000 | 0.001 |
| 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".