Using assistive robots to promote inclusive education
Bibliographic record
Abstract
PURPOSE: This paper describes the development and test of physical and virtual integrated augmentative manipulation and communication assistive technologies (IAMCATs) that enable children with motor and speech impairments to manipulate educational items by controlling a robot with a gripper, while communicating through a speech generating device. METHOD: Nine children with disabilities, nine regular and nine special education teachers participated in the study. Teachers adapted academic activities so they could also be performed by the children with disabilities using the IAMCAT. An inductive content analysis of the teachers' interviews before and after the intervention was performed. RESULTS: Teachers considered the IAMCAT to be a useful resource that can be integrated into the regular class dynamics respecting their curricular planning. It had a positive impact on children with disabilities and on the educational community. However, teachers pointed out the difficulties in managing the class, even with another adult present, due to the extra time required by children with disabilities to complete the activities. CONCLUSIONS: The developed assistive technologies enable children with disabilities to participate in academic activities but full inclusion would require another adult in class and strategies to deal with the additional time required by children to complete the activities. Implications for Rehabilitation Integrated augmentative manipulation and communication assistive technologies are useful resources to promote the participation of children with motor and speech impairments in classroom activities. Virtual tools, running on a computer screen, may be easier to use but further research is needed in order to evaluate its effectiveness when compared to physical tools. Full participation of children with motor and speech impairments in academic activities using these technologies requires another adult in class and adequate strategies to manage the extra time the child with disabilities may require to complete the activities.
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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 | 0.003 |
| 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 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".