Haptic-Assisted Soldering Training Protocol in Virtual Reality: The Impact of Scaffolded Guidance
Bibliographic record
Abstract
In this paper, we present a virtual training platform for soldering based on immersive visual feedback (i.e., a Virtual Reality (VR) headset) and scaffolded guidance (i.e., disappearing throughout the training) provided through a haptic device (Phantom Omni). We conducted a between-subject user study experiment with four conditions (2D monitor with no guidance, VR with no guidance, VR with constant, active guidance, and VR with scaffolded guidance) to evaluate their performance in terms of procedural memory, motor skills in VR, and skill transfer to real life. Our results showed that the scaffolded guidance offers the most effective transitioning from the virtual training to the real-life task - even though the VR with no guidance group has the best performance during the virtual training. These findings are critical for the industry and academy looking for safer and more effective training techniques, leading to better learning outcomes in real-life implementations. Furthermore, this work offers new insights into further haptic research in skill transfer and learning approaches while offering information on the possibilities of haptic-assisted VR training for complex skills, such as welding and medical stitching.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".