Research on associative learning mechanisms of L2 learners based on complex network theory

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.

Keywords:

• Associative learning
• English vocabulary learning
• learning mechanism
• language learning
• complex network theory

Disclosure statement

There are no conflicts of interest to declare.

Additional information

Funding

This article is supported by the Science and Technology Fund of Xi'an University of Architecture and Technology (QN1641) and the Fundamental Research Funds for the Central University of China (xjj2014108).

Notes on contributors

Juan Li

Juan Li is a teaching assistant at School of language, Literature and Law, Xi'an University of Architecture and Technology. Her main research field includes Empirical analysis of language learning mechanisms, and English language teaching methodology.

Hongquan Jiang

Hongquan Jiang, Ph.D., is a doctoral Supervisor and the vice director of the Institute of Manufacturing Systems and Quality Engineering, Xi'an Jiaotong University; he is also working at the Laboratory for Manufacturing and Productivity, Massachusetts Institute of Technology. His main research interests include complex system modeling and analysis, big data analysis and application technology.

Aihua Shang

Aihua Shang is working at School of language, Literature and Law, Xi'an University of Architecture and Technology. Her research orientation is Language teaching strategies and linguistic semantics analysis.

Jingli Chen

Jingli Chen is a lecturer at School of language, Literature and Law, Xi'an University of Architecture and Technology. Her research orientation is the Cross-Cultural Communication, and Language teaching methodology.