Production of Mobile English Language Teaching Application Based on Text Interface Using Deep Learning
Yunsik Cho, Jinmo Kim
Abstract
This paper proposes a novel text interface using deep learning in a mobile platform environment and presents the English language teaching applications created based on our interface. First, an interface for handwriting texts is designed with a simple structure based on a touch-based input method of mobile platform applications. This input method is easier and more convenient than the existing graphical user interface (GUI), in which menu items such as buttons are selected repeatedly or step by step. Next, an interaction that intuitively facilitates a behavior and decision making from the input text is proposed. We propose an interaction technique that recognizes a text handwritten on the text interface through the Extended Modified National Institute of Standards and Technology (EMNIST) dataset and a convolutional neural network (CNN) model and connects the text to a behavior. Finally, using the proposed interface, we create English language teaching applications that can effectively facilitate learning alphabet writing and words using handwriting. Then, the satisfaction regarding the interface during the educational process is analyzed and verified through a survey experiment with users.