Development of the Learning Package for the Living of the Elderly in Kanchanaburi and Suphanburi Province
Bibliographic record
Abstract
The research aimed to 1) develop a learning package for the living of the elderly, 2) assess the quality of the developed learning package, 3) assess the technology acceptance of the developed learning package, and 4) find out the elderly's satisfaction towards the developed learning package. The learning package was developed following the 5-step ADDIE model. The learning package ran under the Glide Application on the YouTube platform and was accessed through mobile phones. The learning package consisted of seven modules which were analyzed and designed based on the study and analysis from the target group of 1,044 persons residing in provinces included in Kanchanaburi Rajabhat University’s service areas, which are Kanchanaburi and Suphanburi, Thailand. The results of the learning package development was composed of 3 sub-modules, which were learning module 2) media module and 3) assistance module. The evaluation result by specialists found that the developed learning package was of a very good quality. The result of a 12-week learning package trial among the target group of 60 persons found that the assessment result of technology acceptance according to the TAM concept in all four aspects was at a good level, and so did the satisfaction of the target group. This indicated that the developed of learning package could be used effectively. That is, it helped support a lifelong learning for the elderly, allowing them to live in modern society with value and maintain a good quality of life in the long run.
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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 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".