Digital Learning Game Scenario - A Pedagogical Pattern Applied to Serious Game Design
Bibliographic record
Abstract
The design of educational Serious Games (SG) remains a difficult operation which requires a tight weave between practices in instructional design and game design to be effective. Despite excellent works in the domain, the balance problem increases more significantly in the mobile learning system development such as Kids Smart Mobile School (KSMS) as a SG. KSMS is a school that aims to provide learning from K to 12 in Math and English as a Second Language to children without access to school in developing countries. This paper proposes a solution by designing a pedagogical pattern of a Learning Game Scenario, based on the educational Montessori approach mixed up with instructional engineering technique. This pattern is applicable to the various learning phases, making up the structure cognitive and pedagogical of KSMS. Moreover, this paper indicates how this pedagogical pattern makes easier the communication between members of an interdisciplinary team in different phases of design and development.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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; both teacher heads agree on what is shown here.
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".