Analysis of Critical Thinking Dispositions Regarding Teachers’ Schematic Representation of Resource Systems
Bibliographic record
Abstract
Studies in recent years have focused heavily on teacher practice and analyzing textbooks and their contents. The schematic representation of the resource system can be used to analyze the composition of teachers’ creation of the document. It is also thought to be an effective process for revealing their critical thinking dispositions. This study aims to determine whether teacher candidates reflect critical thinking dispositions to their schematic representation of the resource systems. The case study design, one of the qualitative research methods, was used in this study. The research was conducted with 26 third-year students from the mathematics department in the faculty of education. In this study, it has been revealed that teacher candidates reflect the resources and critical thinking dispositions they preferred in their schematic representations of resource systems. The five themes “truth-seeking”, “open-minded”, “analytic”, “systematic” and “self-confidence” were found in the schematic presentation of mathematics teacher candidates’ critical thinking dispositions. Also, it was noted that mathematics teacher candidates were more oriented towards digital resources, especially internet resources. As a result, this study showed the resources that affect the professional development of teacher candidates and the relationships between these resources and their critical thinking orientations by the schematic representation of the resource system.
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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 | 0.005 | 0.031 |
| 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 | 0.002 |
| Scholarly communication | 0.003 | 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".