Evaluation of the Utilization of Augmented Reality Technology to Increase Interaction Satisfaction in Remote Assistance
Bibliographic record
Abstract
This research was to evaluate remote assistance applications based on AR technology in terms of user satisfaction.Because user satisfaction is an important factor that needs to be considered in AR applications, the aim is to find out whether the presence of AR technology in telecommunications applications can increase usability and satisfaction when interacting.There are factors tested in the research, namely first impressions, ease of use, integration with the real world, UI design, level of performance and responsiveness, usability and practicality, level of learning, personalization, level of satisfaction, and suggestions for improvisation.The research used 2 methods of data collection, user tests along with interviews plus questionnaires as complementary information to interviews and literature studies with similar research.From the literature study that has been carried out, there are factors that can still be considered when integrating AR technology into remote assistance applications, then also add the results of user tests carried out on one of the remote assistance applications that use AR technology and continue with interviews that add information.new impact of AR technology in remote assistance applications.Plus, by using the User Experience Questionnaire (UEQ).
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".