Developing Handmade Teaching Material Innovation on Thailand Maps to Enhance Geography Concepts of Students with Visual Impairments to Creating an Equitable Ecology in Education for Sustainable Development
Bibliographic record
Abstract
The objectives of the research were to study and develop handmade teaching materials innovation on Thailand maps to enhance geography concepts of students with visual impairments and 2) to study Thai geography concepts of students with visual impairments. The research was action research whereby there were samples in the research, namely (1) 5 experts in developing teaching materials for students with visual impairments (2) 5 experts in social studies learning management, and (3) 10 students with visual impairments. Simple random sampling was used. From the research studies, it was found that: 1) Handmade teaching materials on Thailand maps to enhance geography concepts for students with visual impairments have been developed by using the content of Thailand maps of secondary education students to enhance handmade teaching materials on Thailand maps accounting for 15 charts based on the design of CADDIE model of Mangkhang (2017). Assessment results had completeness and appropriate qualities at a high level. This was used together with our plan of learning management on Thailand maps for 4 plans accounting for 10 hours. The assessment results of the learning management plan came out with having completeness, correctness, and qualities suitable at a high level; 2) Building the assessment form of geography concepts of students with visual impairments had completeness and qualities suitable at a high level and students had geography concepts at the highest level.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".