Determination of Cognitive Structures of Science Teacher Candidates in Ecology
Bibliographic record
Abstract
In particular, it is of great importance that teacher candidates are trained to develop awareness of ecology and toprotect ecological systems. Because they are the ones who will be educate future generations. Ecology is generally aconceptual field. In this study, it was aimed to determine the conceptual structures related to ecology of science teachercandidates at cognitive level. The study is a qualitative research carried out by the screening model. The study wascarried out with the participation of 127 candidates’ science teachers. In this study, a word association test (WAT)was used to determine the cognitive structures of science teacher candidates related to “ecology”. Content analysisand descriptive analysis methods were used in the analysis of data. In this data, the frequency table has been formed.Based on the frequency tables prepared according to teacher candidates' responses to WAT, concept networks relatedto ecology have been established. In the preparation of concept networks, cut point technique was used. When welook at the frequency table, it was observed that the key words that teacher candidates associate most with ecologyare living places, functional properties of ecology and biotic factors of ecosystems in ecology. The sentences ofteacher candidates related to ecology are selected and categorized according to the concepts they contain. When thesentences of teacher candidates related to ecology are examined, it is seen that correct descriptions are found but inmany of them there is incomplete information or concept confusion.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".