Minding the gap: Children’s difficulty conceptualizing spatial intervals as linear measurement units.
Bibliographic record
Abstract
Understanding measurement units is critical to mathematics and science learning, but it is a topic that American students find difficult. In 3 studies, we investigated the challenges underlying this difficulty in kindergarten and second grade by comparing performance on different versions of a linear measurement task. Children measured crayons that were either aligned or shifted relative to the left edge of either a continuous ruler or a row of discrete units. The alignment (aligned, shifted) and the measuring tool (ruler, discrete units) were crossed to form 4 types of problems. Study 1 showed good performance in both grades on both types of aligned problems as well as on the shifted problems with discrete units. In contrast, performance was at chance on the shifted ruler problems. Study 2 showed that performance on shifted discrete unit problems declined when numbers were placed on the units, particularly for kindergarteners, suggesting that on the shifted ruler problems, the presence of numbers may have contributed to children's difficulty. However, Study 3 showed that the difficulty on the shifted ruler problems persisted even when the numbers were removed from the ruler. Taken together, these findings suggest that there are multiple challenges to understanding measurement, but that a key challenge is conceptualizing the ruler as a set of countable spatial interval units.
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".