The Development of Media and Special Event through Cooperative Learning to Raise Funds for Students with Financial Difficulties at Faculty of Industrial Education and Technology
Bibliographic record
Abstract
This research was aimed (a) to develop media and special event through cooperative learning to raise funds for students with financial difficulties at Faculty of Industrial Education and Technology, (b) to evaluate the quality of such media and special event, (c) to measure the learning achievements and the satisfaction of students towards cooperative learning, and (d) to measure the satisfaction of participants in such event. This research was both quantitative and qualitative. There were two sampling groups in this study. The first group consisted of 34 students who enrolled in the ETM 361 Presentation Skills II course in the second semester of the academic year 2017. The second group consisted of 297 participants in the event. The statistical analysis included means score, standard deviation and dependent t-test for research hypothesis testing. The results from the quantitative analysis showed that the quality of the contents was at a very good level (=4.90, S.D.=0.17) and the quality of the media and special event was at a very good level (=4.71, S.D.=0.43). The cooperative learning consisted of 6 stages as in 1) examining problems, 2) planning, 3) implementing, 4) reviewing and follow-up, 5) practicing and presentation, and 6) reflection and evaluation. The learning achievements of students through cooperative learning showed that their average post-test score was higher than their average pre-test score with a statistically significant difference at the.05 level. The cooperative learning increased the teamwork among the students. The students expressed the highest level of satisfaction towards the cooperative learning (=4.76, S.D.=0.42). The participants expressed a high level of satisfaction towards the event (=4.43, S.D.=0.63). The most common problem during the event was lack of communications among students and it could be resolved by the use of social network or closed group on Facebook to report activities and follow-up. The benefit from this event was that the students spent more time working for others and therefore their service mind increased. It could be concluded that the media and special event through cooperative learning could be used on future occasions.
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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 | 0.006 |
| 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 | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".