Using an ANN-based computational model to simulate and evaluate Chinese students’ individualized cognitive abilities important in their English acquisition

Abstract

From a psycholinguistic perspective of view, there are many cognitive differences that matter to individuals’ second-language acquisition (SLA). Although many computer-assisted tools have been developed to capture and narrow the differences among learners, the use of these strategies may be highly risky because changing the environments or the participants may lead to failure. In this paper, we propose an artificial neural network (ANN)-based computational model to simulate the environment to which students are exposed. The ANN computational model equips English teachers with the ability to quickly find the predicting factors to learners’ overall English competences and also provides teachers with the ability to find abnormal students, based on reviewing their individualized ANN trajectories. Finally, by observing the compound effects of cognitive factors using the same evaluation scale, new hypotheses about the mutual relationships among the phonological awareness, phonological short-term memory, and long-term memory abilities of their students can be generated. Our experimental ANNs suggested three detailed corresponding conclusions for the participants’ English teachers. These results provide teachers with guidance in designing and applying cognitive ability-related intervention strategies in their L2 pedagogical activities.

Keywords:

• Cognitive ability
• short-term memory
• long-term memory
• phonological awareness
• artificial neural network
• computational model

Acknowledgements

Last, we would like to thank the principal of Zongbei Experimental Middle School, Mr. Jiaming Zhang for providing many helps to implement the experiments.

Additional information

Funding

This research is supported by the National Natural Science Foundation of China (61402309), UK ESRC grant RES-062-23-2721, Natural Science Foundation of Sichuan (2018JY0312), Sichuan NDSMS grant 201606, and Sichuan Educational Research grant 17ZA0322.