Creative and Learning Processes using Game-Based Activities
Bibliographic record
Abstract
This paper gains a better understanding of how gameplay activities facilitate the creative learning process, reviews learning objects as game elements that influence creativity and learning outcomes, and focuses on learning activity in terms of a series of actions, namely goals, action and interaction, and interpretation. The series should be an element of object learning through game elements, that are adapted from a collection of content items, practice items, and assessment within game tasks. The findings illustrate the relationship between the series of actions within classroom activities, show the significant impact of learning outcomes and the series of actions' learning, and facilitate creative learning processes. The paper proposes a method for understanding the relationships between the learning processes that players undertake during gameplay, and the components of the game that allow these processes to occur. This method was synthesized from the focus group by considering the relationships of their learning outcomes and creative processes within game activities. Previous research studied the creative potential in puzzle game play in the process. The pilot participants in the study adapted the process that was observed during the game "Online Gunz 2: The Second Duel", and were recorded their learning outcomes. The anticipated outcomes represented how video games foster learning processes and creative processes. The results illustrate a significant conceptual method as interrelationships between meaningful learning and creative potential within a game-based activities.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".