The Development of Media Activities by Undergraduate Students in Order to Promote Agricultural Tourism Community Enterprise According to the Principles of Social Service Learning and Community-Based Leaning
Bibliographic record
Abstract
This research was aimed to develop media activities by undergraduate students to promote agricultural tourism community enterprise according to the principles of social service learning and community-based learning, 2) to evaluate the quality of such media activities, 3) to measure the income of the community after the development of media activities, and 4) to evaluate the satisfaction and the learning achievements of the sampling group. This research was conducted in the second semester of the academic year 2016. There were 5 important phases as in 1) Survey on the concepts and the community’s demands, 2) Development of media activities according to ADDIE Model, 3) Evaluation of the quality by 9 experts and the results showed that the contents were of good quality (=4.33, SD=0.63) and that the presentation was of good quality (=4.48, SD=0.54), 4) Measurement of the income of 34 persons in the community and it showed an average income of 6000 to 12000 Baht, or 15% increase, and the satisfaction of the local shops was at the highest level (=4.66, SD=0.54), and 5) Evaluation of the satisfaction and the learning achievements of 79 undergraduate students showed that the students expressed the highest level of satisfaction (=4.70, SD=0.26). After Action Review (AAR) showed that the learning achievements were good because students gained practical knowledge and used their experience in every step of their activities.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".