Transformative an Area-Based Pedagogy of Social Studies Teachers for New Normal Thaischooling
Bibliographic record
Abstract
This paper presents social studies digital pedagogy innovation for social studies teachers for new normal Thaischooling. The purpose of this study is to enable social studies teachers to change teaching methods from traditional social studies teaching to social studies digital teaching. It also expands the area in the context of social studies more widely. Social studies pedagogy is transformative teaching for educating learners to understand human living as an individual and cohabitation in societies. Learners should be equipped with abilities to adapt themselves according to the environment, manage limited resources, understand changing development according to eras and various factors, understand themselves and other people, be patient and accept differences, have morals, and apply knowledge in living and development of digital citizenship. Social studies teachers are expected to apply such concepts suitably for the contexts and environment of new normal schooling. Teaching innovation is distinctive in the teaching and learning process with the focus on the learner as a maker. In this approach, digital technology is integrated with the teaching and learning process by selecting teaching methods, media, activities, and evaluation suitable for the contents. The methods and activities enhance learners to achieve objectives of teaching and learning as well to use students’ learning performance for evaluating and developing their competencies.
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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 | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".