Preservice Teachers’ Views Regarding Out-of-Class Teaching Processes: A Case Study
Bibliographic record
Abstract
Out-of-class learning environments are important learning environments because they improve students’ mental and physical health as well as providing them with cognitive, affective, and psychomotor skills. However, it is necessary to make a plan, implement and evaluate the teaching processes appropriately to efficient benefit from out-of-class teaching environments. The present study aimed to determine preservice teachers’ views regarding out-of-class teaching processes. The study utilized the case study design, a qualitative research approach, to make an in-depth analysis of preservice teachers’ views. 58 preservice teachers from the educational faculty of a state university in Turkey were the participants of the study. Data were collected using a semi-structured interview form developed by the researcher of the present study. For the analysis of data obtained, content analysis was carried out using NVivo9 software, and themes and codes were determined. Findings were presented with frequencies, percentages, excerpts of preservice teachers’ views, and models that indicate the relationship between themes and codes. Findings revealed six different themes for the preservice teachers’ views: out-of-class learning places; advantages of out-of-class teaching; limitations of out-of-class teaching; planning of out-of-class teaching; implementation of out-of-class teaching; and assessment of out-of-class teaching. The study findings were discussed in line with the related literature and suggestions were made regarding the findings.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| 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".