Future instructional design in game-based learning and gamified learning: the construction and incorporation of player typology family tree in the educational context / Darren Lim Yie, Dr. Mageswaran Sanmugam and Prof. Dr. Wan Ahmad Jaafar Wan Yahaya
Bibliographic record
Abstract
Games are not merely for entertainment purposes but rather a powerful tool to be used in the education sector, its use in teaching can be traced back all the way to ancient civilizations. On the other hand, the concept of gamification that is gaining prominence over the past decade holds the idea of harvesting the core of games by the extraction of its elements and applying it in everyday mundane activities. With the rising usage of games and gamification in the educational context, the familiarity of educators and instructional designers to comprehend player types is more crucial than ever before. Thus, the authors of this study conducted a systematic review with the collection of player typology works from 62 authors and restructured it into a family tree with the hope to shed light and bring new insight to fellow educators. The authors successfully identified four main branches of player typology namely the equipment branch, synthesis branch, psychoanalysis branch and demeanor branch which could then be incorporated within an educational context via instructional design.
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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 | 0.016 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".