Perspectives on Flexible Learning of Science Teacher Education Program: Seven Thai Universities Report
Bibliographic record
Abstract
Recently, there has been a widespread implementation of online learning in both traditional classrooms and online classrooms. This has led to a shift in the educational landscape, moving away from face-to-face interactions towards flexible learning by using various kinds of digital platforms. This study employed survey method to investigate perspective of flexible learning of science teacher education from 7 universities, Thailand by selected through purposive sampling and analyzed using a phenomenological design, the researchers conducted interviews to understand the experiences and strategies of these teachers in implementing flexible learning. The questionnaire and interviewing form were employed, and data were collected online. Qualitative data and content analysis were used for showing the result. The themes that emerged from the analysis of the critical components, including learning resources, delivery mode, technology, pedagogy, and student perspective, focused on constraints, flexibility, the use of technology and online resources, and instructional techniques. The participants highlighted internet connectivity and the availability of learning materials as challenges. Instructors employed various techniques and innovations to engage students and encourage them to think creatively. However, the study requires more implementation to convince that flexible learning is suitable for science teacher education as well.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.015 | 0.004 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".