Action Research as Perceived by Student-Teachers in the Field Training Program at Hashemite University / Jordan
Bibliographic record
Abstract
Field Training plays a key role in narrowing the gap between theory and practice in the preparation of teachers. University professors and in-service teachers who work in cooperative schools coordinate their efforts to help student-teachers at the Hashemite University link teaching theories to practice.Student-teachers, sometimes, find themselves isolated from theories learned at the universities and feel the gap between theory and practice. As a result, they tend to use traditional teaching methods in the same way they were taught in the past.One of the important requirements of the Field Training Program is asking student-teachers to do action research to clarify issues they face during training. Student-teachers face different problems related to action research: selecting issues, preparing, modeling, implementing, and evaluating the results of the research. Therefore, this study attempts at investigating the student-teachers’ weakness in carrying out Action Research from the students’ own perspective.To achieve the study objectives, the researcher administered a test on (47) student-teachers, and interviewed (6) others.The majority of responses conveyed that student-teachers know how to design action research theoretically but they lack the ability and necessary skills to put this into practice. Based on these findings, this research concludes with some suggestions and recommendations to solve the problem.
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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 | 0.026 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".