Using virtual robot-mediated play activities to assess cognitive skills
Bibliographic record
Abstract
PURPOSE: To evaluate the feasibility of using virtual robot-mediated play activities to assess cognitive skills. METHOD: Children with and without disabilities utilized both a physical robot and a matching virtual robot to perform the same play activities. The activities were designed such that successfully performing them is an indication of understanding of the underlying cognitive skills. RESULTS: Participants' performance with both robots was similar when evaluated by the success rates in each of the activities. Session video analysis encompassing participants' behavioral, interaction and communication aspects revealed differences in sustained attention, visuospatial and temporal perception, and self-regulation, favoring the virtual robot. CONCLUSIONS: The study shows that virtual robots are a viable alternative to the use of physical robots for assessing children's cognitive skills, with the potential of overcoming limitations of physical robots such as cost, reliability and the need for on-site technical support. IMPLICATIONS FOR REHABILITATION: Virtual robots can provide a vehicle for children to demonstrate cognitive understanding. Virtual and physical robots can be used as augmentative manipulation tools allowing children with disabilities to actively participate in play, educational and therapeutic activities. Virtual robots have the potential of overcoming limitations of physical robots such as cost, reliability and the need for on-site technical support.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".