Sun Fei, Mendoza Laura, Wang Junju, Li Hongbin
School of Foreign Languages and Literature, Shandong University, Jinan 250100, China.
Language Centre, University of Helsinki, P.O. Box 4, 00014 Helsinki, Finland.
Behav Sci (Basel). 2025 Jul 20;15(7):983. doi: 10.3390/bs15070983.
Approaches to writing play an important role in both the writing processes and outcomes. However, little is known about whether L2 writers adopt different combinations of approaches in academic writing contexts and what factors predict such combinations. Hence, this study aimed to identify different profiles of approaches to writing in an L2 academic context and examine how they are predicted by writing self-efficacy and large language model (LLM) acceptance. To this end, a total of 578 Chinese graduate students were recruited to participate in the study. Latent profile analysis revealed three distinct writing profiles: unorganized (Profile 1), dissonant (Profile 2), and deep and organized (Profile 3), with the majority of students categorized under the dissonant profile. Additionally, multinomial logistic regression analysis revealed that writing self-efficacy positively predicted profile membership, with the strongest effect observed for Profile 3, followed by Profile 2 and then Profile 1. LLM acceptance also positively predicted profile membership, with the strongest effect for Profile 2, followed by Profile 3 and then Profile 1.
写作方法在写作过程和结果中都起着重要作用。然而，对于二语写作者在学术写作语境中是否采用不同的写作方法组合以及哪些因素能够预测这些组合，我们知之甚少。因此，本研究旨在识别二语学术语境下不同的写作方法概况，并考察写作自我效能感和大语言模型（LLM）接受度如何预测这些概况。为此，共招募了578名中国研究生参与本研究。潜在概况分析揭示了三种不同的写作概况：无组织型（概况1）、不一致型（概况2）和深入且有组织型（概况3），大多数学生被归类为不一致型概况。此外，多项逻辑回归分析表明，写作自我效能感对概况归属具有正向预测作用，对概况3的影响最强，其次是概况2，然后是概况1。LLM接受度也对概况归属具有正向预测作用，对概况2的影响最强，其次是概况3，然后是概况1。
