The Help-seeking from Social Capitals and Self-regulated Learning among Pre-service Teachers
Bibliographic record
Abstract
This study aims to investigate the help-seeking subjects for pre-service teachers in need of suggestions for practicum and to determine the prediction of pre-service teachers’ social capitals for help-seeking on their self-efficacy for help-seeking and self-regulated learning. A total of 223 pre-service teachers, from a teacher education university at the middle of Taiwan, were invited to fill in the validated questionnaire in October 2015. The analytical results of this study by Chi-square Test achieve significant differences in the five types of help-seeking subjects for the pre-service teachers in need of suggestions for practicum. Another finding of this study by multiple regression analysis indicates that the scores of seeking help from faculties in practicum school and peer interns in practicum school can jointly predict self-efficacy for help-seeking This study concludes that the pre-service teachers preferred seeking help from family members, faculties in practicum school, classmates at university and peer interns in practicum school to university professors. Moreover, pre-service teachers perceived self-efficacy for help-seeking and self-regulated learning when seeking help from faculties and peer interns in practicum schools. Interestingly, peer interns in practicum school are considered as bonding social capital but maybe play a role of suggestion-provider, similar to bridging social capital during the practicum.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".