Additive Word-Problem Solving in Children With Language Difficulties: A Descriptive Analysis of Strategies and Errors
Bibliographic record
Abstract
Children with Developmental Language Disorder (DLD), a neurodevelopmental disorder affecting linguistic abilities, can experience difficulties throughout their schooling, such as in mathematics. Solving word problems, a language-dependent task, requires children to understand the text, and identify the semantic relationships between the problem’s quantities to solve them. Therefore, gaining insights on the effects of DLD on word-problem solving can help support the learning of children with DLD. The present study compares the word-problem solving abilities of typically-developing (TD) children (n = 28) and children with DLD (n = 16). Children were recruited in schools in Montreal, Quebec City, and Sherbrooke, or in private speech-language pathology’ clinics. During two videorecorded sessions, students were invited to solve additive word problems created by the research team. The groups were compared on accuracy, the appropriateness of their strategies, and error types. Also, a strategy profile was assigned to each child based on the most frequent strategy used to explore potential differences among the groups. The findings of this study highlight significant differences between the groups on accuracy, strategy appropriateness, and the frequency and types of errors produced. DLD appears to affect the way children understand the text, identify relevant information, abstract the problem structure, use a strategy aligned with the problem structure, and compute answers. In contrast, the distribution of the strategy profiles is similar in each group: They tend to use the standard algorithm (e.g., formal procedure taught in class) even if they make mistakes. Moreover, they still rely on manipulatives to solve word problems.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".