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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".