Usability of context-aware mobile educational game
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Ubiquitous learning is an innovative approach that combines mobile learning and context-awareness, can be seen as kind of location-based services, first detects user’s location, knows surrounding context, and gets learning profile, and then provides the user learning materials accordingly. Game-based learning have become an emerging research topic and been proved that can increase users’ motivations and interests. The aim of our research is to present a context-awareness multi-agent-based mobile educational game that can generate a series of learning activities for users doing On-the-Job training and make users interact with specific objects in their working environment. We reveal multi-agent architecture (MAA) into the mobile educational game design to achieve the goals of developing a lightweight, flexible, and scalable game on the platform with limited resources such as mobile phones. A scenario with several workplaces, research space, meeting rooms, and a variety of items and devices in 11th floor of a university’s building is used to demonstrate the idea and mechanism proposed by this research. At the end, a questionnaire is used to examine the usability of the proposed game. 37 freshmen participate in this pilot study and the results show that they are interested in using the game and the game does help them getting familiar with the new environment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it