Technology and early mathematics skills: Effectiveness of I Love Math with Robots
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Technological tools facilitate mathematical learning and make children love mathematics, thanks to their structures and ways of working. In this context, educational robots appear as a very attractive alternative. Studies show that the use of these devices provides positive cognitive outcomes. This research aimed to investigate the effect of using robotic devices on the early math skills of preschool children. Participants consisted of 24 children aged between 50-68 months. In the study, quasi-experimental model was used. Children in the experimental group attended the 8-week “I Love Math with Robots” designed by the researchers whereas those in the control group engaged in activities without technologic robots including the same objectives. Early math skills of children in both groups were assessed individually before and after the intervention. The results indicated that changes in math scores of children in the experimental group were significantly different from those in the control group.
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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 | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it