Design and Evaluation of a Learning Assistant System with Optical Head-Mounted Display (OHMD)
Bibliographic record
Abstract
The rapid increase in the use of wearable technologies, especially Optical Head-Mounted Display (OHMD) devices, suggests potentials for education and requires more scientific studies investigating such potentials.In particular, the issue of information access and delivery in classrooms can be of interest where multiple screens and objects of attention exist and can cause distraction, lack of focus and reduced efficiency.This study explores the usability of a single OHMD device, as an alternative to individual and big projected screens in a classroom situation.We developed OHMD-based prototypes that allowed presentation and practice of lesson material through three display and two control options.We conducted user studies to compare various feasible combinations using a series of evaluation criteria, including enjoyment, ability to focus, motivation, perceived efficiency, physical comfort, understandability, and relaxation.Our results did not strongly support a significant effect caused by the use of alternative displays.However, participants' feedback showed that they favoured OHMD as a single screen in classroom learning situations, while their main complaints about it were related to physical comfort and ease of control.Our studies also showed that participants were more pleased and motivated to learn when using OHMD.This suggests that improved OHMD technology will have the potential ability to be effective in classroom learning.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".