Developing Teachers to Enhance Students' Effective Teamwork Skills
Bibliographic record
Abstract
This research was conducted in order to develop teachers to enhance the effective teamwork skills of their students in secondary schools. This was a part of a research project, which was based on advances in digital technology and the knowledge-based society of the 21st Century. Various useful perspectives on developing effective teamwork skills were collected from the internet and applied by utilizing Research and Development methodology. The aim was to achieve an educational innovation called an "Online Self-Training Program to Develop Teachers to Enhance Their Students' Effective Teamwork Skills". This program was intended to empower teachers with knowledge and skills that could be applied to classrooms and could ultimately enhance the learning outcomes of students. From the research resulted in such educational innovations that have been verified by teachers who are interested in implementing this educational innovation and through experimental research in the field, found that it is effective according to the specified criteria, namely 1 ) The teacher learning outcome on the posttest score met the standard criteria of 90/90 and the posttest score was significantly higher than the pretest score, and 2) The posttest score from the students' effective teamwork skills assessment was significantly higher than the pretest score. Therefore, it showed that this educational innovation can be disseminated for the development of teachers and students in secondary schools nationwide.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".