The Guideline of Community Based Historical Learning to Enhance Digital Citizenship of Secondary school students in Education Sandbox
Bibliographic record
Abstract
This study of Participatory Action Research aimed to 1) study the guideline of community based historical learning management to enhance digital citizenship of secondary school students in the Education Sandbox and 2) propose a guideline of community based historical learning management to enhance digital citizenship of secondary school students in the Education Sandbox. The research sample was 1) a team of ten faculty members and social studies teachers; and 2) a team of 5 learning management experts. Purposive sampling was used to pick 15 persons, and the study tools were 1) a document analysis form 2) an unstructured interview template 3) an Assessment form for an appropriate learning management approach. The qualitative data were content evaluated and presented in descriptive analysis. The study discovered that: 1. The study the guideline of community based historical learning management to enhance digital citizenship of secondary school students in the Education Sandbox discovered that management of community-based historical learning is a concept of history learning management together with Community-Based Learning: CBL by connecting students' understanding as local members with a sense of history in their area with the learning process by employing local history content as a learning point that leads to the development of digital-based learning innovations to promote tourism in the community, in which teachers must manage learning to engage students in designing, planning as well as choosing study areas that interest them and in the education sandbox area, some several measurements and assessments respond to education management. And 2. The propose a guideline of community based historical learning management to enhance digital citizenship of secondary school students in the Education Sandbox, it was discovered that the researcher developed a community-based historical learning management approach known as the Guideline of Community Based Historical Learning: CBHL or 3P Model, which consists of 1. Prepare 2. Process and 3) Present, with the results of the highest degree of suitability assessment for learning management.
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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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".