A sensor and user behavior data analysis based method of mobile learning situation perception

As the popularity of the smart phones and mobile technologies, more and more people begin to use smartphones to learn and get new knowledge. Mobile learning has played a critical role in the field of education for a few years. The effectiveness of mobile learning reflects in the ability of perceiving different learning contexts and then provides matched learning resource. Context awareness has become a research hotspot, but the most important is learning situation perception. We can provide proper learning resources according to the specific learning situation. Because of the mobility and complexity of mobile learning, it's difficult to perceive learning situation. The thesis proposes a method to perceive learning situations by combining sensor data and learning operation data and conducts some experiments. It chooses and calculates some sensor data eigenvalues and learning operation index eigenvalues as the inputs of the classification algorithms, the learning situations that students provide as training set data. The result shows that combining sensor data and learning operation data to perceive learning situations can improve the accuracy of the learning situation perception, which proves the feasibility and effectiveness of learning situation perception based on sensor data and learning operations.