Empirical Research on Developing an Educational Augmented Reality Authoring Tool
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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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 | 0.037 | 0.163 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".