A Multi-Mode Learning Behavior Real-time Data Acquisition Method Based on Data Quality

With the rapid development of new technologies such as artificial intelligence, big data, and the Internet of Things, many researchers have probed into the study of learning analysis, trying to solve the problems of teaching by analyzing the learning behavior data from learning process. And in many learning behavior research, the sensor network usually consists of a host of mutually independent data sources, which can be used to monitor measured objects from multiple dimensions thereby obtaining the multi-source multi-modal sensory data. However, there still exist false negative readings, false positive readings and environmental interference, etc. Therefore, we propose a multi-source multimode sensory data acquisition method based on Date Quality(DQ). We first define the data quality in terms of four aspects-accuracy, integrity, consistency and instantaneity. Then, by the modeling there aspects respectively, we propose metrics to estimate the comprehensive data quality method of multi-source multi-mode sensory data. Finally, a data acquisition method is presented based on data quality, which selects a part of data sources for data transmission according to the given precision. This method aims at reducing the consumption of the sensory network on the premise of the data quality guarantee. An extensive experimental evaluation demonstrates the efficiency and effectiveness of the algorithm.