Research On Learner Model In Online Learning Environment

With the rapid development of big data,learning analysis,computational science and other technologies,personalized learning,adaptive learning and other technologies are important research issues in the online learning environment.Among them,learner model research is the core and foundation for personalized learning.Aiming at the current situation that most of the studies will study the learner's learning style,cognitive level,and even the behavioral style,this thesis focuses on the further comprehensive study of learner characteristics in the online learning environment.The main work contains three aspects.Firstly,based on the analysis of online learning learner modeling research,a literature analysis method was used to propose an integrated model of learner characteristics in an online learning environment.The model described learner online learning features from five aspects:learner basic information,cognitive level,learning style,interactive behavior features,and online learning social features,so as to draw portraits of learners from different dimensions.Secondly,based on the proposed learner model,the learner feature analysis technology in online learning environment is studied.Among them,correlation analysis and regression analysis were used to analyze the cognitive level;K-Means algorithm was used to analyze the learning style;Analytic Hierarchy Process(AHP)was used to analyze the interactive behavior characteristics;and social network analysis was used to analyze online learning social characteristics.Finally,using the data provided by the actual application system,an empirical study was conducted on the proposed model and analysis techniques.Including the correlation analysis and regression analysis of cognitive level based on behavioral data,the analysis of learner information processing style based on Cluster,the analysis of learner loyalty and engagement based on behavioral data,the analysis of reputation and centrality based on social network.The actual data analysis shows the effectiveness of the proposed model and analysis method,which can provide theoretical and practical guidance for personalized services in an online learning environment.