Interdisciplinary Community Based Learning to Enhance Competence of Digital Citizenship of Social Studies Pre-Service Teacher’s in Thai Context: Pedagogical Approaches Perspective
Bibliographic record
Abstract
The objectives of this research were to 1) synthesize competency of digital citizenship and concepts of interdisciplinary community-based learning and 2) propose guidelines of interdisciplinary community-based learning management for promoting digital citizenship of pre-service teachers in the Thai context. The research was qualitative research conducted by using content analysis according to the grounded theory to obtain study results according to the objectives. The study results were as follows: 1. The results of synthesizing competency of digital citizenship and concepts of interdisciplinary community-based learning revealed that digital competencies which led to digital citizenship were as follows: Digital Access, Digital Literacy, Digital Commerce, Digital Safety and Resilience, Digital Participation and Agency, Digital Emotional Intelligence, Digital Creativity and Innovation, Digital Communication, Digital Ethics, Digital Health, and well-being. For concepts of interdisciplinary community based learning through pedagogical approaches, they consisted of Academically Based community Service / Civic learning / Environmental Education / Placed-based Education / Service Learning / Work-based Learning/Tech Pre /Ethical Case Studies/Social Engagement/Discipline-Based Model Problem-based Learning / Capstone course (a short course emphasizing self-study from actual and complicated problems) /Service Internship / Youth Apprenticeship / Community-Based Action Research Model /Experience-Based Career Education Volunteerism / Pre-Service Teacher /Field Education / Independent Study Modal and 2. Guidelines of managing interdisciplinary community based learning for promoting digital citizenship of pre-service teachers in the Thai context revealed that important issues leading to interdisciplinary community based learning management consisted of Contextaul Knowlegde / Academic study /Citizenship/ Community-based Learning/ Interdisciplinary approach/ Environmental Education/ Experiential Learning / Lifelong Learning.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".