Research On The Construction Of Primary School Science Learners Portrait In Smart Class

With more and more smart education,smart campus and smart class coming into people’s view,the construction of education informatization is in full swing in primary and secondary schools all over the country.How to better serve teachers and students with a large number of learner related data accumulated on the smart class learning platform and give full play to its due value has become a big problem to be solved.As a visual analysis tool of students’ learning performance,the function of learner portrait is to reveal students’ learning characteristics in a comprehensive way by visualizing learners’ personalized characteristic information,excavate the value hidden behind the data generated in the process of education and teaching,and describe the learning process in an all-round way,It can reduce the cognitive load of teachers and students caused by the influx of a large number of teaching data,provide a scientific basis for accurate teaching,and better understand and improve the learning and teaching process.First,this study defines the concepts of smart room,learner portrait and precise teaching by referring to relevant literature and books,and expounds the theoretical and technical basis of the construction of learner portrait.According to the data field of smart room learning platform and the characteristics of primary school science classroom,the paper designs a learner model for the primary and secondary school subjects in smart room.It determines the learner portrait based on the five characteristics of students’ basic attribute characteristics psychological attribute behavior attribute capability attribute result attribute feature.And based on the previous relevant relevant features The results show that there are five steps in the process of portrait of scientific learners in the primary school of smart room.Based on the above,the paper takes the data of 31 students in class 4,grade 5,Y School of C City,based on the smart room platform,and takes two primary school science and smart room in the sink and float unit of the teaching section as an example.The basic attribute dimension,learning style dimension,behavior attribute dimension,inquiry ability dimension and learning conclusion of the learners are realized The five characteristics of the result dimension are analyzed in depth,including the modeling of the learner portrait in the shallow layer.The data collected are analyzed simply.In order to get the deep portrait,the author makes deep mining on the learner behavior by clustering method,enriching the granularity of the learner portrait,and learning based on the behavior and ability attributes of the learners The author divides the types of learners into categories.Based on the basic attribute learner portrait model,psychological attribute learner portrait model,behavior attribute learner portrait model,ability attribute learner portrait model and result attribute learner portrait model,the paper constructs the primary school science learner portrait label in the form of tagging System.Finally,in the form of chart and tag cloud,it presents the personal digital portrait of students and the group portrait of students for teachers based on personalized tags.This study builds a portrait based on the data of the real smart class students in primary schools,visualizes the data,and constructs a picture label system for the learners in the primary schools of smart classroom by accurately mining the value behind the students’ behavior data.It completes the digital portrait for the learners and the group portrait for the teachers,and accurately positioning the students’ learning situation,And in the form of interview,the precise teaching decision is put forward based on the learner portrait.