Watch Those Studs! How Prior Domain Knowledge and Extraneous Details on LEGOâ Bricks Influence Children’s Fraction Division
Bibliographic record
Abstract
The primary aim of the present study was to examine whether extraneous details on LEGOâ bricks prompted inappropriate counting strategies and impacted performance accuracy when solving fraction division problems. The secondary aim was to investigate whether prior domain knowledge of fractions concepts influenced the extent to which the extraneous details on the bricks affected children’s problem-solving performance. Thirty-eight fifth- and sixth-grade students (<i>N</i> = 38) participated in the study. A fractions test was used to classify students into low (<i>n</i> = 19) and high prior knowledge (<i>n</i> = 19) groups. Then, all participants watched an instructional lesson that showed them how to represent fractions with LEGO bricks and how to solve fraction division problems using measurement division with the bricks. The participants then completed a learning task and a second task designed to assess whether extraneous details on LEGO bricks influenced their problem-solving performance. The results revealed that the extraneous details on LEGO bricks prompted some students to use inappropriate counting strategies, but prior knowledge did not explain the attention to extraneous details. In contrast, prior knowledge accounted for the variance in performance accuracy and the types of errors committed. Specifically, children with low prior knowledge made more errors choosing the correct bricks to represent the dividend fractions, which resulted in a larger number of inaccurate solutions compared to children with high prior knowledge.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".