Empirical Research on Developing an Educational Augmented Reality Authoring Tool
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 thesis identifies a lack of research on the efficiency of using general-purpose Augmented Reality (AR) authoring tools for educational purposes and investigates its difficulties and drawbacks. While traditional education methods have proven their efficiency, academics constantly explore new ways to benefit from technology in education. Notwithstanding, elementary school teachers are tempted by the well-reputed success of incorporating AR in classrooms to enhance lessons, motivate students, keeping them focused, and so forth. They face, along with students, many challenges trying to adopt this technology to the curriculum. We scrutinized the literature review to sort and analyze some of the difficulties of using general-purpose authoring tools in education and deduct heuristic and reflect on how to counter those difficulties to develop an education AR authoring tool. We have developed and evaluated a prototype of an AR authoring tool made for education called CUAR (Carleton University Augmented Reality).
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 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