Student grouping by neural network based on affective factors in learning English

Differences between teaching perspectives and students characteristics may impact negatively on students´ learning effectiveness. A new approach to bridge such a gap needs establishing. The capabilities of artificial neural networks to approximate extremely complex problems encourage us to develop a grouping model of students´ English ability. The model was trained using back propagation algorithm and tested using 154 samples from college students. The model grouping rate on students´ English abilities demonstrated fairly low errors for both general grouping and each ability grouping for Listening, Reading, Speaking, and Reading, respectively.