Student Vlog for Community Communication Through Social Lab
Bibliographic record
Abstract
This research was aimed to develop student vlog for community communication through social lab under the ECT Vlog project. In this study, a social lab consists of 3 communities near King Mongkut’s University of Technology Thonburi. Other objectives of this study were to determine the quality of the media and activities in this research, to evaluate the perception and the satisfaction of the sampling group towards the student vlog under the ECT Vlog project which was held in the second semester of the academic year 2019. The tools in this study consisted of questionnaires for the quality and the media presentation, the perception assessment form, and the satisfaction questionnaire. The sampling group in this study consisted of 100 followers of the ECT Vlog facebook page who had watched 12 student vlog video clips and were willing to participate in this research. They were chosen using purposive sampling method. The research results have shown that the researchers created 12 Student Vlog video clips for community communication through social lab with 3 communities near King Mongkut’s University of Technology Thonburi. The team of creators consisted of the researchers and undergraduate students from the ETM 358 Marketing Communication course. The team analyzed, designed, developed, implemented and evaluated the video clips according to the ADDIE Model which consists of 5 steps. The team analyzed the data, designed the contents and developed 12 student vlog clips and then asked 3 experts in contents and 3 experts in media presentation to evaluate the quality of the video clips. It was found that the quality of contents was at a very good level ( \hat{x} = 4.61, S.D = 0.38) and that the quality of media presentation was at a very good level ( \hat{x}= 4.62, S.D = 0.43). Afterwards, the researchers distributed the student vlog clips on the ECT Vlog facebook page and assessed the perception of the sampling group. It was found that their perception was at a high level ( \hat{x} = 4.50, S.D = 0.58). The sampling group expressed a high level of satisfaction towards the student vlog (\hat{x} = 4.43, S.D = 0.67), confirming the research hypotheses. It can be concluded that the development of student vlog for community communication through social lab was of good quality and that it could be used in other contexts.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".