Performing mathematics activities with non-standard units of measurement using robots controlled via speech-generating devices: three case studies
Bibliographic record
Abstract
Purpose To examine how using a Lego robot controlled via a speech-generating device (SGD) can contribute to how students with physical and communication impairments perform hands-on and communicative mathematics measurement activities. This study was a follow-up to a previous study. Method Three students with cerebral palsy used the robot to measure objects using non-standard units, such as straws, and then compared and ordered the objects using the resulting measurement. Their performance was assessed, and the manipulation and communication events were observed. Teachers and education assistants were interviewed regarding robot use. Results Similar benefits to the previous study were found in this study. Gaps in student procedural knowledge were identified such as knowing to place measurement units tip-to-tip, and students' reporting revealed gaps in conceptual understanding. However, performance improved with repeated practice. Stakeholders identified that some robot tasks took too long or were too difficult to perform. Conclusions Having access to both their SGD and a robot gave the students multiple ways to show their understanding of the measurement concepts. Though they could participate actively in the new mathematics activities, robot use is most appropriate in short tasks requiring reasonable operational skill. Implications for Rehabilitation Lego robots controlled via speech-generating devices (SGDs) can help students to engage in the mathematics pedagogy of performing hands-on activities while communicating about concepts. Students can "show what they know" using the Lego robots, and report and reflect on concepts using the SGD. Level 1 and Level 2 mathematics measurement activities have been adapted to be accomplished by the Lego robot. Other activities can likely be accomplished with similar robot adaptations (e.g., gripper, pen). It is not recommended to use the robot to measure items that are long, or perform measurements that require high operational competence in order to be successful.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".