Assessing Young Children’s Language and Nonverbal Communication in Oral Personal Narratives
Bibliographic record
Abstract
In this article, we describe tasks and an assessment framework, collaboratively designed with kindergarten teachers in a northern rural Canadian school district, to assess young children’s language and nonverbal communication. Our analysis of 44 five-year old children’s language samples showed that children usually provided information about the name or role of at least one character in their narrative, although a few children referred to characters only using pronouns and a few provided information about multiple features of characters. The events and ideas in most children’s narratives were loosely connected, although some children used conjunctions to connect them and even explained causal relationships between them. To enhance meaning, many children communicated multimodally, most frequently by using gestures or a combination of gesture, intonation, and sound effect. They also used a question or invitation to hook their audience. The use of open-ended tasks allows children to draw on their funds of knowledge resulting in greater relevance and potential value across classroom 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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".