Observation and analysis of a classroom teaching and learning practice based on augmented reality and serious games on mobile platforms
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
This qualitative research is part of a learning effort to better understand how serious games are exploited in a science education context. The research team examined this issue by focusing on augmented reality as a technological innovation imbedded on a tablet. Given the current state of knowledge related to serious games and augmented reality, and given the fact that its use in the context of teaching/learning is not extended, this paper focuses on an initial exploration of how a new teaching practice involving a serious game based on an interactive augmented reality solution would impact on students in a physics class. A Design Based Research methodology was applied in a real‑world context within a college‑level physics class. Two conceptual tests containing ten questions on spatial notions regarding electromagnetic fields were administered to two control groups and two groups using the proposed serious game. The latter groups were administrated a game evaluation questionnaire as well. Thematic interpretation of students written responses to the evaluation questionnaire as well as the lessons and observations we derived from the in-class experimentation are provided and discussed in the paper.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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