Research on associative learning mechanisms of L2 learners based on complex network theory. Issue 5 (26th July 2021)

• Record Type: Journal Article
• Authors: Li, Juan; Jiang, Hongquan; Shang, Aihua; Chen, Jingli
• Is Part Of: Computer assisted language learning. Volume 34:Issue 5/6(2021)
• Page Start: 637
• Page End: 662
• Publication Date: 2021-07-26
• Subjects: Associative learning -- English vocabulary learning -- learning mechanism -- language learning -- complex network theory; Language and languages -- Computer-assisted instruction -- Periodicals; 418.00285
• DOI: 10.1080/09588221.2019.1633356
• Languages: English
• ISSNs: 0958-8221

Abstract

Abstract: Associative learning strategy (ALS) is an important means of acquiring vocabulary—especially in L2 learning. This study proposes a method to identify the associative learning mechanism based on complex network theory. First, a distributed association strategy (DAS) for the associative learning of L2 learners based on distributed language learning is analyzed, and a dynamic vocabulary network model based on DAS is constructed. Second, by analyzing the topological parameters, the external form and overall characteristics of the vocabulary network and three questions regarding its impact on vocabulary acquisition are discussed. A case study involving the foreign language learning and teaching of undergraduate students is used to illustrate this method. The results show that the proposed method can effectively reflect the associative learning process of acquiring vocabulary. Results also reveal the dynamic and evolutionary laws governing vocabulary networks when using ALS, as well as small world and scale-free characteristics.