关键词: 学习风格, 机器学习, 门控循环单元（GRU）, 学习管理系统, 大规模在线开放课程（MOOCs）

Abstract: The emergence of Massive Open Online Courses（MOOCs） in 2012 has been boomed in recent years, learners’ learning behaviors can be recorded and analyzed by the system. According Felder-Silverman learning style model, this paper applies a hybrid Neural Networks（NN） which combines a Convolutional Neural Networks（CNN） and connects with a Gated Recurrent Unit（GRU） based Recurrent Neural Networks（RNN）, to detecte learning style dynamically. The model is applied in analyzing the learners’ learning behavior, including online-community interaction, learning content browsing log, click and drag the behavior, identifying learning style dynamically, and the using GRU based RNN to process and predict the possible learning styles and provides learning content and path for optimizing learning process efficiency, improving learning experience, supports large-scale, personalized and high quality education.

Key words: learning styles, machine learning, Gated Recurrent Unit（GRU）, learning management system, Massive Open Online Courses（MOOCs）