Advancing Transformative Learning to Develop Competency in Teaching Social Studies Online of Pre-service Teacher Students in Chiang Mai Education Sandbox
Bibliographic record
Abstract
The objectives of the research at this time were to 1) study and construct the transformative learning innovation to develop competency in teaching social studies online and 2) study the results of transformative learning to develop competency in teaching social studies online of pre-service teacher students in Chiang Mai education sandbox. For research methodology, participatory action research (PAR) was used. The samples in the research consisted of 1) Staff of teachers teaching social studies (9 people); 2) Experts of learning management (5 people), and 3) Students taking the course of the social studies teaching methodology for Semester 1 of the 2021 academic year (43 people). Purposive sampling was used to get a total of 57 people. The instruments used in the research were 1) unstructured interview forms, 2) assessment forms of the suitability of the approach of organizing innovative transformative learning to develop competency in teaching social studies online, and 3) questions reflecting learning. Qualitative data were analyzed by using content analysis. The presentation was conducted in the form of descriptive analysis. Quantitative data were analyzed by using the statistical package to find the mean and standard deviation. The study results revealed that: 1. Regarding innovative transformative learning to develop competency in social studies online teaching, arrangements should be made for students to learn the methodology of social studies pedagogy in the form of hybrid learning. This will help students have teaching competencies in real classrooms(onsite) and virtual reality classrooms (online) efficiently. The approach to organizing learning innovation called area-based pedagogy had efficiency at the highest level and 2. The developed learning innovation helped develop teaching competencies of pre-service teacher students to be consistent with the Thai Qualifications Framework for Higher Education of digital competencies efficiently.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".