DOI: 10.14704/nq.2018.16.5.1416

The Application of Brain Neural Computational Model in English Learning

Yong Liu


To apply the brain neural computational model to language cognition, this paper takes the application of the model in English learning as the study object. According the recent research, this study combines with the artificial brain neural network (ANN), use the psychological computational model and associated hypothesis models, on the basis of the theoretical results of domestic and foreign scholars' research and finally find the reasons for learning disabilities and verify the phonological awareness of learners. Assumptions related to memory ability are put forward, and English learning intervention suggestions based on association hypothesis model of learners’ phonological awareness and memory ability were proposed. The result shows that the ANN can simulate the learner’s learning path and provide a basis for predicting the learner’s learning ability and the learning barrier analysis. At the same time, it can provide more accurate clues and directions for subject teachers’ individualized teaching.


Brain Neural Computational Model, ANN Simulation, English Learning, Associated Hypothesis

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Brown WM, Gedeon TD, Groves DI. Artificial brain neural networks: A new method for mineral prospectivity mapping. Journal of the Geological Society of Australia 2015; 47(4): 757-70.

Byrnes JP, Vu LT. Educational neuroscience: definitional, methodological, and interpretive issues. Wiley Interdisciplinary Reviews: Cognitive Science 2015; 6(3):221-34.

Gabrieli JD. The promise of educational neuroscience: Comment on Bowers. Psychological Review 2016; 123(5): 613-19.

Gong Y, Zhang Y, Lan S. A Comparative Study of Artificial brain neural Networks, Support Vector Machines and Adaptive Neuro Fuzzy Inference System for Forecasting Groundwater Levels near Lake Okeechobee, Florida. Water Resources Management 2016; 30(1): 375-91.

Grover M, Drossman DA, Oxentenko AS. Integration of artificial brain neural network and geographical information system for intelligent assessment of land suitability for the cultivation of a selected crop. Brain Neural Computing & Applications 2015; 26(6): 1311-20.

Khorasani AM, Yazdi MRS. Development of a dynamic surface roughness monitoring system based on artificial brain neural networks (ANN) in milling operation. International Journal of Advanced Manufacturing Technology 2017; 93(1-4): 1-11.

Liu T, Xie J, Yan W. Finger-vein recognition with modified binary tree model. Brain Neural Computing & Applications 2015; 26(4): 969-77.

Miller R D. Contextualizing Instruction for English Language Learners with Learning Disabilities. Teaching Exceptional Children 2016; 49(1): 58-65.

Pillars W. The Ultimate Top 10 Teaching Tips to Make Your ELLs Soar!: Get 10 foolproof strategies for reaching the English language learners in your classroom. Educational Horizons 2015; 93(4): 30-30.

Pollack C. No brain left behind: consequences of neuroscience discourse for education. Learning Media & Technology 2015; 40(2): 168-86.

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