Shifting Paradigms in Secondary Science Classrooms: Teaching and Learning from an Ecological Perspective
Bibliographic record
Abstract
This paper explores ecological learning theory and how it disrupts the present understanding of knowledge, intelligence, and the individual. In the education system, intelligence is seen as the basic capacity for competence; reasoning, as the activity that generates competence (St. Julien, 2000, p. 254). The common language here points to the Cartesian idea that knowledge is something that is outside the individual and that intelligence is an attribute within the individual that allows them to make use of this knowledge. Educational practices are built upon these assumptions that something must be done to the student to help them acquire and apply this knowledge. Ecological learning theory's fundamental understanding of intelligence breaks away from this paradigm and offers a very different understanding of cognition and intelligence. Moving the focus of education from studying about the world towards being part of the world means that a completely different way is needed to understand knowledge and learning (Davis, Sumara and Luce-Kapler, 2000). This means the definition of what learning is has burst open to incorporate many experiences and interactions compared to the traditional narrow definition of learning. From this frame, we will explore the implications for teachers and students in a secondary science classroom.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.020 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".