Research On Learner Characteristic Model With Emotional Factors In Smart Classroom Environment

With the rapid development of information technology such as big data and learning analysis,the product of the deep integration of technology and education,smart classroom,emerges at the right moment.Smart classroom is the main carrier of smart learning environment,and smart learning environment is the important environmental support and technical support for personalized learning,in which the learner’s emotional state is an important part of personalized learning.Moreover,the research of personalized learning is based on learner model construction.Based on the above research focuses,this paper will carry out the research on the learner characteristic model integrating emotion factors in combination with the intelligent classroom teaching environment,in order to solve the problem of insufficient research on the characteristics of learners’ emotion factors in the current learner model research and analysis.Firstly,on the basis of analyzing the research status at home and abroad and sorting out related concepts and theories,a learner characteristic model integrating emotional factors is proposed.Learner characteristics are divided into three categories: learner basic information,behavior factor characteristics and emotion factor characteristics.The characteristics of learners are divided into three categories: basic characteristics of learners,behavioral factors and emotional factors.The research focuses on the characteristics of emotion factors.The study deeply analyzes the workflow of acquisition,recognition and classification of learners’ emotion characteristics in the smart classroom environment.From the above perspective,learning characteristics are described and the core status of emotional factors is highlighted.Meanwhile,learners are described in a more comprehensive way.Secondly,on the basis of the proposed learner model,the learner feature analysis technique is studied,focusing on the learner’s emotion feature analysis technique.The concepts of tree structure and decision tree in the field of computer science are introduced,and then random forest and hierarchical random forest model are introduced to explain the key techniques of facial expression recognition and classification.This paper briefly introduces the quantitative analysis methods of cognitive level,interactive behavior characteristics and social network characteristics.Finally,the online MOOC and offline synchronous classroom ““Internet +”era of teacher information teaching literacy ”course as an example of teaching case analysis.According to the analysis indexes and methods in chapter 3 and chapter 4,the validity of the learner feature model integrating affective factors is verified from four aspects: cognitive level feature,interactive behavior feature,social network feature and affective feature.Based on the data analysis of practical teaching cases,it can be concluded that learners’ emotional state changes in the intelligent classroom environment can be judged by the learner’s characteristic model and relevant analysis methods.And provide targeted intervention measures in combination with the characteristics of learners’ behavioral factors.Thus,it also provides a new way of thinking for the research of smart classroom teaching.