Touch Together: Open-Ended Design of TouchCounts Shapes Parent―Child Affective Engagement in Family Mathematics
Bibliographic record
Abstract
This qualitative study investigates how the open-ended design of TouchCounts (Sinclair & Jackiw, 2014), a multi-touch application for early numeracy, shapes affective dynamics during parent―child interactions in family math activities. Drawing on inclusive materialism (de Freitas & Sinclair, 2014) and affect theory in mathematics education (de Freitas et al., 2019), the study conceptualizes affect as a relational, provisional force that circulates among parent, child, and technology, and is entangled with their interaction. It focuses on how TouchCounts elicits distinct affective dynamics through its open-ended and multimodal features (e.g., without prescribed or level-driven tasks, gesture-responsive multi-touch screen, full-screen math-object generation space, and support for explorative individual and collaborative use). Through micro-scale analysis of two excerpts from a parent-child dyad's interaction video-recordings, selected from five participating pairs in Canada, the study captures affective dynamics—expressed through gestures, embodied actions, and verbal output—as they emerge and unfold differently in each sub-environment (i.e., Enumerating World and Operating World). Findings show that the distinct affordances of each World give rise to nuanced, affect-rich interactions that embody diverse ways of mathematical thinking and communication. This research underscores the potential of open-ended digital technologies to shape the affective dimensions of early math learning in family contexts.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".