Research Of MOOC Learning Situation Early Warning Based On Learner Portrait

Online education learning is a very important learning method in the process of non-traditional education learning.With the continuous promotion and development of education informatization,the improvement of education quality and informatization education level in China has provided strong support for the development of online learning.Learners have carried out online learning across platforms,terminals and time and space,and have been in different online education platforms There are a lot of learning behavior data.MOOC is an important platform for online education.At present,MOOC has a huge amount of user data,which makes it difficult to grasp the learning situation of learners based on user data.Online learner portraits can help solve such problems.All kinds of online platform data and the study of learners' portrait and learning situation early warning are constantly updated,and the combination of portrait technology and learning situation early warning provides a new path for the study of learning situation early warning of MOOC platform.Based on the above background,this study takes all kinds of learners' data in online MOOC platform as the research object,through analyzing and using the MOOC data of 14 courses to construct learners' portraits and early warning of learning situation,through analyzing learning situation through portraits,and through learning situation analysis to early warning of learning situation,finally forming learning situation early warning strategy.First of all,through literature analysis,this study defines the concepts of learner portrait and learning situation early warning,expounds the theoretical and technical basis of portrait depiction and learning situation early warning,and determines the classification of three dimensions of online learner portrait.And according to the data field of online MOOC platform,construct the label system of the learner's portrait,provide the measurement index for the label system of the learner's portrait,and lay the foundation for the application of the portrait technology to realize thelearning situation early warning.Secondly,according to the data and basic information of learners' online learning behavior,online learning data analysis is carried out.By using data mining and analysis methods,groups of different types of learners are divided,and learners' portraits are output.Based on the five dimensions of the portraits,learning situation analysis is carried out to grasp learners' learning information.Finally,with the help of the results of five dimensions of academic situation analysis,four dimensions of academic situation early warning are carried out by using association rules and sequence analysis model and other technical means.According to the classification of the portraits,the early warning strategies of learning situation are provided.On this basis,the effectiveness of the early warning of academic situation based on portrait is tested by questionnaire.This research builds a picture based on real MOOC data,visualizes the data,uses a variety of research methods and technical means to carry out picture based learning situation early warning,helps teachers and learners master the situation of learners and provides learning situation early warning strategies,which provides a new idea for online MOOC platform teaching.