Kerangka kesedaran kognitif bagi reka bentuk antaramuka pengajaran berasaskan gamifikasi dalam kalangan pelajar kejuruteraan
Bibliographic record
Abstract
Gamification is one of the most popular methods of teaching today. The usage of gamification appropriately applied to students will contribute to their learning. In the global world of education, the use of gamification affects the quality of their learning. This study discuses the elements of gamification design that contribute to the awareness of cognitive learners. A total of 400 UTHM students comprising of engineering students answer the questionnaire was developed to determine the criteria involved in cognitive awareness in developing gamification learning. The findings of the survey were analyzed using statistical descriptive. In addition, data found in survey questions are used for literature review to analyse the criteria for gamification to enhance cognitive awareness through content analysis and the targeted literatures are journals, conferences and books available in the database from 2015 to 2018. Both survey and literature review analys were used to indentify cognitive domain awareness criteria in gamification in learning design. In order to verify and validate the criteria, a checklist was used involving three experts in the field of gamification. This data is analyzed through heuristic analysis. Next, the data was verified and validated by the expert. The data was analysed using an interpretation of cohen kappa result. The overall findings determined the criteria of cognitive awareness in gamification learning for engineering students. It is hoped that the criteria for cognitive awareness will help to improve the decision-making process and the ability to evaluate students performance.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.005 |
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".