An Analysis of the Candidate Teachers’ Beliefs Related to Knowledge, Learning and Teaching
Bibliographic record
Abstract
Candidate teachers have several beliefs related to their knowledge, learning and teaching. The purpose of this study is to analyze the beliefs of candidate teachers about knowledge, learning and teaching. Candidate teachers were assigned a scale and from the answers “belief points” were obtained based on their attitudes about these three dependent variables. It is investigated whether or not there is a significant difference in candidate teachers’ belief points about knowledge, learning and teaching. In addition, this research aims to show to what extent they have these beliefs and predictive among these belief dimensions regardless of variable identification. The relational descriptive method is used in this study. The study was conducted on the 297 primary school candidate teachers selected as subjects of the research in the last year of their education. It is found out that the belief of teaching needs to be constructivist and learning depends on process and efforts are indirectly predicted by the belief on the relativity of knowledge. Similarly, traditional beliefs on teaching are directly and indirectly predicted by the belief that learning depends on effort and ability and the belief in objective and ultimate knowledge. Consequently, it is determined that individuals’ beliefs on knowledge, learning and teaching are highly interdependent.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".