Examining how children’s use of math elicitations supports their own math learning
Bibliographic record
Abstract
Math abilities are related to outcomes including better health, greater chance of full-time employment, and higher income (Agarwal & Mazumder, 2013; Currie & Thomas, 2001; Reyna & Brainerd, 2007), with individual differences in math skills present as early as the beginning of kindergarten (Jordan et al., 2006). Previous work has found that parents’ encouragement of math conversation supports children’s math learning (Levine et al., 2010; Elliott et al., 2017). However, no work has looked at how children spontaneously discuss math, which may be an information-seeking technique used to shape their own learning. We examined children's math elicitations (questions or prompts used to encourage a response from the other person) during free play in both lab and home settings in parent-child dyads (RQ1: n = 113, 51% boys, M age = 3.9 years; RQ2: n = 84, 50% boys, M age = 3.9 years) in cases where parents were not previously discussing math, but children elicited math relevant to the conversation. RQ1: Children who used more of these spontaneous math elicitations had larger gains in math skills over 6 months, even controlling for a variety of covariates including children’s overall elicitations and baseline math performance, β=0.318, p=.004, suggesting that children who take what their parents are discussing and make it math-related may promote their own math learning. Given the robust association between children’s spontaneous math elicitations and their math performance, we were interested to explore predictors of children’s use of spontaneous math elicitations. RQ2: We assessed children’s spontaneous focusing on number (SFON) tendency (children’s tendency to focus their attention on the number of objects in a set on their own, without any outside guidance or prompting) and found that children’s SFON did not significantly predict their later use of spontaneous math elicitations, β=.088, p=.403. This work stresses the importance of considering how children may directly shape the home environment and their opportunities to learn in addition to thinking about parents' influences on children's math learning. Future work should continue exploring factors that influence children’s tendency to spontaneously seek out math information.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".