A Learning Preference Analysis Method Based on a Novel Developed Teaching Skill Training App for Mobile Learning

The emergence of mobile learning makes the learning no longer limited by time and space, and also gives a new way for teaching skill training of normal university students. In this paper, we proposed a learning preference analysis method to explore learning behavior characteristics from the training data based on a novel developed teaching skill training App. Four indexes including frequency, time, media and location preferences are selected to analyze the learning interest of users during mobile learning based skill training. By using visual analysis through Python and K-means clustering algorithm in data mining, the changes of users' learning preferences over time, the preference degree for different skills, and when and where users are more willing to learn knowledge are obtained. By using the learning preference analysis method, learners realize personalized learning, and teachers and developers can adaptively adjust training content and training route in time to improve the skill training efficiency.