An early-warning model for online learners based on user portrait

Abstract

In the age of the Internet, online learning is an important learning strategy. At present, a large number of data on learning behavior have been generated on various online education platforms. It is difficult to grasp the learning situation of the numerous learners of these platforms according to the massive data. User portrait offers a possible solution to the problem. This paper firstly classifies the portrait of online learners into three dimensions, and constructs the tag system of learner portrait based on the data fields of online learning platform. Then, the learning behavior data of online learners were analyzed in details. Online learners were divided into multiple groups through data mining, and the learner portrait was generated. From the five dimensions of learner portrait, the learning situation was analyzed to master the learning information of learners. Based on the analysis results, the four-dimensional early-warning of learning situation was realized through sequence analysis and association rule mining. The research results provide a good reference for the improvement of online learning.