Research Of Similar Learners Based On The Feature Vectors Of Network Education Learners

In the era of rapid development of education informatization,the teaching and learning methods are also undergoing diversified changes,and traditional education is difficult to meet the large number of learners in society,and network education has also emerged.Since this education model gives learners more autonomous learning space and authority,it is particularly necessary to provide learners with relevant support services and personalized and adaptive teaching.As the core element of the network teaching system-the learner is the object of providing high-quality learning services.Among them,the analysis of learner characteristics has become a key way for the online education system to fully understand the needs of learners and provide accurate learning services.On the one hand,learner characteristics reflect the learner's performance,status,and characteristics in learning activities;on the one hand,it can promote the guidance and service of the online education system to learners,so it is an important research direction of online education.However,in traditional distance education,the analysis of learners is not comprehensive enough and even ignores the characteristics of learners and the learning status reflected by them.It is easy to cause learners to have problems such as lost learning and reduced learning motivation,which will affect the learners' learning effectiveness.In addition,in the face of a large group of learners,separating them to provide personalized teaching and related services will inevitably consume a lot of educational resources and time costs,and also ignore the possible connections,commonality and laws among learners.Therefore,on the one hand,the thesis focuses on the comprehensiveness and dataability of online education learner features,and on the basis of this,the learner features are vectorized,in order to provide comprehensive data support for learner feature analysis;also pay attention to learning with similar features Learners,and similar learners are formed based on the learner feature vectors.Through the vectorization of learner features and similar learner analysis,it aims to provide some feasible methods for optimizing the learning effect,improving the learning process,and improving the quality of the new distance education model.This research is based on the above background.In order to directly and comprehensively apply learner features in online education to further optimize learning support services,the research work of this paper is as follows:(1)Through analysis of the existing literature,this paper sorts out the development status of online education and the opportunities and challenges faced by learners,and defines the concepts of online education learner characteristics,learner feature vectors and similar learners.Learner feature vectorization and similar learner formation lay the theoreticalfoundation.(2)This research is guided by humanistic learning theory and constructivist learning theory,based on the principles of data standardization and cluster analysis,and based on the learner system of Jiangnan University 's continuing education and online education system.Practical processing,through the second-level index data to obtain the first-level index feature value,and on this basis,the learner characteristics are vectorized.(3)According to the learner feature vector,first,randomly obtain 10,000 learner feature data of the Jiangnan University online education platform,and secondly,apply the cohesive hierarchical clustering method to further form similar learners,and use the contour coefficient method to select the best cluster As a result,according to the best clustering result,the category of similar learners is obtained,and the clustering result is analyzed,and then the application of similar learners is discussed.Finally,the results and shortcomings of this study are summarized,and the future research work is prospected.This study is a further in-depth application of the learner feature system,which can provide a basis for the practical application of learner features,and at the same time pay attention to the analysis and application of similar learners,and provide some reference for the study of application services for similar learners in network education.