Access to hands-on mathematics measurement activities using robots controlled via speech generating devices: three case studies
Bibliographic record
Abstract
PURPOSE: To examine how using a robot controlled via a speech generating device (SGD) influences the ways students with physical and communication limitations can demonstrate their knowledge in math measurement activities. METHOD: Three children with severe physical disabilities and complex communication needs used the robot and SGD system to perform four math measurement lessons in comparing, sorting and ordering objects. The performance of the participants was measured and the process of using the system was described in terms of manipulation and communication events. Stakeholder opinions were solicited regarding robot use. RESULTS: Robot use revealed some gaps in the procedural knowledge of the participants. Access to both the robot and SGD was shown to provide several benefits. Stakeholders thought the intervention was important and feasible for a classroom environment. CONCLUSIONS: The participants were able to participate actively in the hands-on and communicative measurement activities and thus meet the demands of current math instruction methods. IMPLICATIONS FOR REHABILITATION: Current mathematics pedagogy encourages doing hands-on activities while communicating about concepts. Adapted Lego robots enabled children with severe physical disabilities to perform hands-on length measurement activities. Controlling the robots from speech generating devices (SGD) enabled the children, who also had complex communication needs, to reflect and report on results during the activities. By using the robots combined with SGDs, children both exhibited their knowledge of and experienced the concepts of mathematical measurements.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".