Evidence map›Paper›PMID 40874033›Full record

ArticleBiology methods & protocols2025

Constructing a norm for children's scientific drawing: Distribution features based on semantic similarity of large language models.

Yi Zhang, Fan Wei, Jingyi Li, Yan Wang, Yanyan Yu, Jianli Chen, Zipo Cai, Xinyu Liu, Wei Wang, Sensen Yao and 2 more

Abstract read
In one paragraph

Article in Biology methods & protocols, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Yi ZhangFirst Primary School, Beijing 100071, China.
Fan WeiFangzhuang Primary School, Beijing 100078, China.
Jingyi LiCollege of Elementary Education, Capital Normal University, Beijing 100048, China.
Yan WangFirst Primary School, Beijing 100071, China.
Yanyan YuFirst Primary School, Beijing 100071, China.
Jianli ChenFirst Primary School, Beijing 100071, China.
Zipo CaiFirst Primary School, Beijing 100071, China.
Xinyu LiuFirst Primary School, Beijing 100071, China.
Wei WangFirst Primary School, Beijing 100071, China.
Sensen YaoBeijing Fengtai Institute of Education, Beijing 100073, China.
Peng WangHepingli No.9 Primary School, Beijing 100013, China.
Zhong WangInstitute of Clinical Education, Beijing Doers Education Consulting Co., Ltd, Beijing 100027, China.ORCID https://orcid.org/0000-0003-2020-8450

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The use of children's drawings to examining their conceptual understanding has been proven to be an effective method, but there are two major problems with previous research: (i) The content of the drawings heavily relies on the task, and the ecological validity of the conclusions is low. (ii) The interpretation of drawings relies too much on the subjective feelings of the researchers. To address this issue, this study uses the Large Language Model (LLM) to identify 1420 children's scientific drawings (covering nine scientific themes/concepts) and uses the word2vec algorithm to calculate their semantic similarity. The study explores whether there are consistent drawing representations for children on the same theme and attempts to establish a norm for children's scientific drawings, providing a baseline reference for follow-up children's drawing research. The results show that the representation of most drawings has consistency, manifested as most semantic similarity >0.8. At the same time, it was found that the consistency of the representation is independent of the accuracy (of LLM's recognition), indicating the existence of consistency bias. In the subsequent exploration of influencing factors, we used Kendall rank correlation coefficient to investigate the effects of "sample size," "abstract degree," and "focus points" on drawings and used word frequency statistics to explore whether children represented abstract themes/concepts by reproducing what was taught in class. It was found that accuracy (of LLM's recognition) is the most sensitive indicator, and data such as sample size and semantic similarity are related to it. The consistency between classroom experiments and teaching purpose is also an important factor, many students focus more on the experiments themselves rather than what they explain. In addition, most children tend to use examples they have seen in class to represent more abstract themes/concepts, indicating that they may need concrete examples to understand abstract things.

Indexed as

children’s drawingsdistributionLLMnormsemantic similarityWord2vec

Identifiers

PMID40874033
PMCPMC12380450

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.