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Article type: Research Article
Authors: Li, Yajiea | Cai, Xiruib; *
Affiliations: [a] Faculty of Arts, Huashang College, Guangzhou, Guangdong, China | [b] Department of Foreign Languages, Kunming Medical University, Kunming, Yunnan, China
Correspondence: [*] Corresponding author. Xirui Cai, Department of Foreign Languages, Kunming Medical University, Kunming, Yunnan 650500, China. E-mail: tsai_hsijui@163.com.
Abstract: In recent years, significant progress has been made in the study of Chinese vocabulary acquisition, and the research content and scope have gradually expanded. Chinese vocabulary is the foundation for understanding and using language, and any language has developed on the basis of Chinese vocabulary. Many studies have shown that there are many factors that affect the acquisition of Chinese vocabulary, and the impact on Chinese vocabulary acquisition is also different. Among them, the influencing factors of Chinese vocabulary acquisition have gradually become a research hotspot, and a large number of related empirical studies have emerged. Based on the big data technology and the random effect model, this paper comprehensively analyzes whether the influencing factors of Chinese vocabulary acquisition as a second language are significant and investigates the effects of internal and external influencing factors, delayed post-test, specific factors and experimental interval time on the learning effect. External factors significantly impact Chinese vocabulary acquisition, including learning style, input factors and input methods. Through the subgroup analysis of the experimental interval, we find that the effect of Chinese vocabulary acquisition decreases with the extension of the experimental interval. Therefore, this paper holds an inverse relationship between the experimental interval and the development of Chinese vocabulary acquisition.
Keywords: Chinese, second language, Chinese vocabulary, big data technology, analysis of influencing factors
DOI: 10.3233/JIFS-235515
Journal: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 3545-3556, 2024
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