Student Interest Analysis And Prediction Based On Sequential Convolutional Network

For college students,there are other spare time activities besides study.Some students have rich interests and hobbies,and some students lack interests and hobbies.In psychology,interest,as a kind of inner strength of people,play a major role in personal future development.Through the survey,it is found that most current studies on students' interests are based on social networks or single-dimensional characteristics,There are few studies on students' interests from the perspective of multiple characteristics,Based on educational data,this thesis studies students' interest from the perspective of multiple characteristics.Firstly,this thesis analyzes student data,hoping to mine effective student interest information from massive data sets.In the information age,everyone generates a lot of data every day.This thesis uses data collection technology to extract a lot of education data from the online system of a domestic university,and then this thesis also extracts features that can express students' characteristics from the complex learning environment from multiple dimensions.The characteristics are tagged.According to the timeliness,all the attributes of students are divided into static tag attributes and dynamic tag attributes.Student portraits are obtained by tagging the attributes of students.Through the correlation analysis method,the attributes with high correlation with students' interest are selected,and these attributes jointly represent students' interest.Finally,this thesis also takes student achievement as an important feature of student interest prediction.In this thesis,an N-HOT algorithm is proposed,which can effectively achieve the embedding of performance features,thus realizing feature conversion,effectively saving space cost and reducing computational complexity.In this thesis,we redefine the student interest prediction problem as a sequential event prediction problem,and propose a new deep learning-based student interest prediction model IN-TCN,which is built on Temporal Convolutional Networks(TCN).This thesis has done experiments based on education data sets,and the results of student interest prediction show that this model is not only always better than standard logistic regression-based methods,but also better than some deep learning models.