Using robots in “Hands-on” academic activities: a case study examining speech-generating device use and required skills
Bibliographic record
Abstract
PURPOSE: A 12-year-old girl, Emily, with complex communication needs and severe physical limitations, controlled a Lego robot from a speech-generating device (SGD) to do various "hands-on" academic activities. Emily's teacher and assistive technology (AT) team thought that controlling a robot would motivate Emily to "use her SGD more". METHOD: A descriptive case study was used because the integration of communication and manipulation technologies is not yet understood. Target activities and goals were chosen by Emily's teacher and AT team. Emily performed several manipulative math activities and engaged in an "acting" activity aimed at increasing her message length. The competency skills needed to control a robot from the SGD were examined, as well as stakeholder satisfaction with the robot system. RESULTS: Emily generated up to 0.4 communication events and 7 robot commands per minute in the activities. Her length of utterance was usually one-word long, but she generated two- and three-word utterances during some activities. Observations of Emily informed a framework to describe the competency skills needed to use SGDs to control robots. Emily and her teacher expressed satisfaction with robot use. CONCLUSION: Robot use could motivate students to build SGD operational skills and learn educational concepts. Implications for Rehabilitation Controlling a robot from a speech-generating device (SGD) could increase students' motivation, engagement and understanding in learning educational concepts, because of the hands-on enactive approach. The robot and SGD system was acceptable to the participant and teacher and elicited positive comments from classmates. Thus, it may provide a way for children with disabilities to link with the curriculum and with other students in the classroom. Controlling a robot via SGD presents opportunities to improve augmentative and alternative communication operational, linguistic, social and strategic skills. Careful choice of activities will ensure that the activity requirements focus on the desired target skill, e.g. drawing or playing board games could be helpful to build operational skills and acting out stories could be helpful for building linguistic skills.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".