Study On Academic Situation Analysis Based On Online Learning Behavior Data

In the teaching process,adjusting teaching contents and methods based on the learning situation of students is an important task for teachers.Compared with offline teaching,teachers lack the corresponding means to follow up the learning status of students in online teaching.At present,few studies on academic situation analysis are carried out based on online learning behavior data,and most of these studies focus on teaching video viewing data,while little attention is paid to other learning behaviors.In this case,the effectiveness and accuracy of academic situation analysis cannot be ensured.The study in this article is based on a variety of learning behavior data generated during the learning process of teachers and students on the Ziyue platform.Aiming to obtain a more comprehensive and accurate academic analysis result,and to better serve the teaching work of teachers.Firstly,a student behavior data set is constructed,which includes online study notes data and viewing behavior data.Secondly,according to the characteristics of the note text,the quality of the study notes in the student behavior data set was evaluated from five indicators of sentence completeness,readability,similarity,cohesion,and gregariousness,and a note with the characteristics of the automatic control principle course was constructed.The quality evaluation model,through which the comprehensive quality Q of the notes is calculated.Then based on the viewing behavior data in the student behavior data,the online learning concentration evaluation of the students is carried out.According to the characteristics of the data,the learning concentration is measured from the three indicators of viewing,looking back,and concentration.And a learning concentration evaluation model is constructed.Calculate the student’s online learning concentration F through this model.Then combined with the student’s note quality Q,study concentration F,and classroom test scores,the online education analysis of students is carried out from the three dimensions of notes,videos,and tests.Finally,combining the above two evaluation models,a student performance prediction model based on the neural network is built,and student performance is predicted through this model.The analysis results show that the quality of student notes is significantly positively correlated with learning concentration;it is positively correlated with classroom test scores;learning concentration is positively correlated with classroom test scores;learning evaluation results based on student behavior data are positively correlated with classroom test scores,and both have statistics academic significance(p<0.05).The result error of the performance prediction model on the test set is 3.137%,and the error is within the acceptable range,indicating that the model has a certain feasibility.