Teaching Practicums at the Postgraduate Level: Problems, Solutions, and Recommendations
Bibliographic record
Abstract
In any educational study program, teaching practicums are necessary to be implemented. It often serves as an apprenticeship to give pre-service teachers practical experience regarding the real world of teaching. It is commonly implemented at the undergraduate level. At the postgraduate level, however, this implementation remains controversial. This study was conducted to reveal postgraduate students’ problems and solutions during their teaching practicums. Besides that, this study also aimed to examine and give critical insights into the implementation of teaching practicums at the postgraduate level for educational study program students. Data was retrieved from open-ended questionnaires to 8 postgraduate students about their problems and solutions in doing their teaching practicums. Data was also taken from 7 other respondents. They were practitioners like lecturers, teachers, and alumni from educational departments. The findings revealed that in doing their teaching practicums, the master’s degree students dealt with various problems similar to those faced by their undergraduate counterparts. Another finding disclosed the practitioners’ opinions that for the postgraduate level, the focus of teaching practicums can be altered and implemented in a form other than teaching. It can be research, curriculum development, language teaching technology development, and material development. At the postgraduate level, students are expected to focus on research, not just the practice of teaching.
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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 | 0.013 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".