Animation Augmented Reality Book Model (AAR Book Model) to Enhance Teamwork
Bibliographic record
Abstract
This study aims to synthesize an Animation Augmented Reality Book Model (AAR Book Model) to enhance teamwork and to assess the AAR Book Model to enhance teamwork. Samples are five specialists that consist of one animation specialist, two communication and information technology specialists, and two teaching model design specialists, selected by purposive sampling. The instrument used in the study was an evaluation form for the Book Model. Statistics used in the study were arithmetic mean and standard deviation. The result shows that: an AAR Book Model to enhance teamwork achieved contains four components. Firstly, requirement analysis to create animation augmented reality; including 1) Objective setting, 2) Content analysis, 3) Student analysis, 4) Environment Analysis, 5) Teacher analysis, and 6) Creating animation augmented reality as a teaching material to motivate students. Secondly, teaching method: 1) Using gamification to motivate learning and practice; 2) Assigning students to work in teams and make a presentation. Thirdly, evaluating teamwork, conducted via teachers’ observation and creating an integrated scoring rubric. Lastly, analysis of feedback: All five specialists agreed that the AAR Book Model to enhance teamwork developed through this study has a highest level of suitability (= 4.75, S.D. = 0.04).
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.014 | 0.004 |
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".