Using the divergent association task to measure divergent thinking in Chinese elementary school students

被引:1
作者
Ding, Guozhu [1 ]
He, Yiwei [1 ]
Yi, Kaixu [1 ]
Li, Shan [2 ]
机构
[1] Guangzhou Univ, Teachers Coll, Guangzhou Higher Educ Mega Ctr, Sch Educ, 230 Wai Huan Xi Rd, Guangzhou 510006, Peoples R China
[2] Lehigh Univ, Coll Educ, Coll Hlth, HST Bldg 132,124 E Morton St, Bethlehem, PA 18105 USA
关键词
Divergent thinking; Semantic distance; Creativity; Natural language processing; Elementary school student; CREATIVITY; TESTS; MODEL;
D O I
10.1016/j.tsc.2024.101503
中图分类号
G40 [教育学];
学科分类号
040101 ; 120403 ;
摘要
The Divergent Association Task (DAT), published in July 2021, is a psychological test designed to measure an individual 's divergent thinking. The test requires participants to name ten nouns that exhibit maximum dissimilarity from each other. The semantic distance between these nouns is then calculated to indicate the person 's level of divergent thinking. In this study, we explored the applicability of the DAT for elementary school students in Chinese contexts, given that it was not initially designed for this specific population and was available only in English. We recruited a total of 348 students who were asked to complete three creativity tasks: the DAT, the Alternative Uses Task (AUT), and the Bridge-the-Associative-Gap Task (BAG). We examined the associations between DAT and the scores of the AUT and BAG tests. Moreover, we tested the accuracy of the DAT using varying numbers of nouns and different natural language processing models to calculate the semantic distance between nouns. Our findings supported the suitability of using the DAT to measure divergent thinking in elementary school students within Chinese contexts. We also found that using only eight nouns, instead of ten, could achieve a relatively high accuracy in measuring divergent thinking based on the DAT method. The language model of Word2Vec performed better than the BERT (Bidirectional Encoder Representations from Transformers) and GPT (Generative Pre-trained Transformer) models when calculating semantic distances between nouns. This study has methodological and practical implications.
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页数:11
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