Discourse Analysis of Science Teachers Talk as a Self-reflective Tool for Promoting Effective NOS Teaching
Bibliographic record
Abstract
Teaching the Nature of Science (NOS) in elementary schools and in higher education institutions has become thefocus of the science education research community because there is a need to redefine teaching methods andpractices. Studies have shown that elementary teachers’ views and attitudes towards teaching the NOS are not alwayscompatible with the current acceptable theoretical framework identified by the research community. Researchers andscholars have proposed a variety of approaches in pre-service and in-service teacher training so that teachers changetheir beliefs, views and attitudes towards teaching the NOS. Amongst these there are two major trends, the implicitand explicit training approaches. Literature review has shown that the later is relatively more effective. Our proposalconcerns the use of an explicit strategy to enable teachers analyse their own discourses in cooperation withresearchers and reflect on their own views on aspects concerning teaching the NOS. The proposal is based on theassumption that the critical reflection on the ways people construct meanings may enable the exploration ofalternative ways to communicate actions during science lessons. An empirical example of this discourse analysisoriented in explicit approach is presented and a proposal of possible series of discourse analysis oriented in actionsfor promoting more effective NOS teaching is discussed.
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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 | 0.023 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.003 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| 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".