Evaluating the Learning Management and Assessment Abilities of Preservice Teachers in Mathematics Education Program
Bibliographic record
Abstract
The objective of this research was to evaluate the learning management and learning assessment abilities of preservice teachers in the Mathematics field. Additionally, it aimed to provide guidelines for the development of these abilities. The study group consisted of 34 practicum teacher students, 34 mentor teachers, and 14 experts. Data collection tools included evaluation forms and interviews. Descriptive statistics, such as means and standard deviations, were employed for quantitative data analysis, while content analysis was utilized for qualitative data analysis. The research findings indicated that the preservice teachers possessed a high level of learning management ability, with an average self-assessment score of 4.02 and an average mentor assessment score of 3.98. However, their learning assessment ability is relatively lower, with an average self-assessment score of 3.96 and an average mentor assessment score of 3.50. Guidelines for development include promoting inspiration and adjusting mindsets about mathematics learning management. The study also recommended intensive practical training in both simulated situations and intensely real situations. In terms of learning assessment development, diversifying assessment methods for improvement and providing creative constructive feedback is suggested.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".