Development of Gamyflip-Pro Module and Determination of Its Content Validity
Bibliographic record
Abstract
In recent years, the integration of Flipped Classroom and gamification in teaching and learning process became popular in order to provide flexibility in teaching and learning process.The purpose of this study was to develop and validate the content of GamyFlip-Pro module in teaching and learning a topic Programming for pre-university students.This module was developed based on Sidek and Jamaludin's Model, Flipped Classroom Approach, and The Five Steps of Applying Gamification in Education by Huang and Soman.The development process involves two main process namely; development of module draft, experts' reviews and implementation of content validity for the modules.Since the development of module draft complete, five subject matter experts will review the module draft.Then, module modification was done based on suggestion and recommendations by subject matter experts followed by the process of calculation content validity of the module.The percentage of experts' agreement obtained for this module ranged from 73% to 90%.This finding shows that GamyFlip-Pro module has a good content validity.This finding reinforces the use of flipped classroom and gamification approach in the design of teaching and learning modules and establish the pedagogy of flipped learning and gamification in teaching programming.
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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 | 0.027 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".