Developing Learning Innovation of Digital Open Badge in Social Studies to Enhance Citizenship Characteristics of Secondary School Students
Bibliographic record
Abstract
This study aims to synthesize theories and concepts related to educational management, personality traits, and citizenship characteristics evident in the education systems of the United Kingdom, Canada, the United States, Singapore, and Thailand; to develop an innovative model of open-badge digital social education to enhance the cultivation of citizenship characteristics among high school students in Chiang Mai; and to study the results of developing digital badge innovations to enhance the development of students’ personality traits and citizenship characteristics in the educational innovation area of Chiang Mai province. A mixed-method approach is employed in order to synthesize the universal core values that ensure societal peace within diverse communities from the curricula of the five countries. When comparing the results with the Thai context, it becomes apparent that the Thai curriculum still lacks precise definitions of “universal core values” and activities that instill these core values. Through purposive sampling, this study extracts the necessary values from the opinions of involved personnel and pilot schools in the Education Sandbox program in Chiang Mai. The results suggest that ten core values need to be addressed firmly in the Digital Open Badge learning innovation and guidelines for both in-class and out-of-class activities. The citizenship characteristics platform and guidelines in social studies are designed based on the 5Ts Action Learning model with 40 hours of observation. The outcomes demonstrate that overall satisfaction with the curriculum is high. Notable changes from interviews and observations indicate that responsibility and cooperation are the most significant changes in students’ character traits. These traits are expressed mostly at the individual level (35.02%), followed by the family level (29.39%), the community level (22.62%), and lastly, the global level (12.97%).
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".