Context-Aware Mobile Role Playing Game for Learning--A Case of Canada and Taiwan.
Bibliographic record
Abstract
The research presented in this paper is part of a 5-year renewable national research program in Canada, namely the NSERC/iCORE/Xerox/Markin research chair program that aims to explore possibilities of adaptive mobile learning and to provide learners with a learning environment which facilitates personalized learning at any time and any place. One of the sub-projects of this 5-year national research program is to design and develop context- aware mobile learning services. The research team of the sub-project applied narrative theory to design a location based Context-Aware Mobile Role Playing Game (CAM-RPG) in order to give students feeling of living in the game world and role playing, exploring the game world, completing the quests, and learning things. A pilot study was then conducted to see how the two game features - context-awareness and story generation - influence students' attitude towards the use of the mobile educational game. The research findings suggest that the story generated in CAM-RPG positively influences users' attitude towards game use and increases users' perceived game usefulness. With the research findings, other components and outcomes of sub-projects, such as natural language processing, location-awareness, multiple input forms, social networking, and student modeling, can then be put together as one piece to provide students effective and efficient mobile learning experiences.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".