Analysis And Practical Research For Students' Behavior Data On The E-Learning Platform Of Senior High School

In the second session of the 13th National People's Congress on March 5^th,"Internet+education"was written into the government work report for the first time,which became an important starting point of the country's education.The report pointed out that"Internet+education"should be developed to promote the sharing of high-quality educational resources.As a form of"Internet+education",e-learning has been rising in various colleges and universities,which has set off an upsurge.However,it is rarely used in basic education.E-learning platform plays an important role in improving students'learning effect.Teachers can also obtain students'data through e-learning platform to further improve teaching efficiency.Based on the theory of behavioral science,this paper uses the online behavior of the first-year students in high school in the information technology teaching platform as the research object.The e-learning behavior model is divided into two types according to the related e-learning behavior model theory after eliminating the invalid behavior data.Pearson correlation coefficient was used to evaluate the collected effective behavior data,and three characteristic values of the model were determined:classroom practice time,classroom learning time and login times of the platform after class.The Pearson correlation coefficient was evaluated for the collected effective behavior data,and the three eigenvalues of the model were determined.Through the machine learning method,the classification algorithm is used to establish the test score prediction model and verify the accuracy of the model.The paper consists of five parts:The first chapter introduces the background,purpose and significance of the research,investigates the research status in China and abroad,determines the research methods,and introduces the sociological theory,behavioral science theory and the new constructivism theory.It also defines the concepts of e-learning,learning behavior and student behavior data,e-learning platform as well as e-learning behavior.The second part is a brief introduction to the online teaching platform as well as the online courses in this platform.Besides,it obtains student behavior data from the platform,and preprocess the data,which include platform login times after class,truancy times,the number of overdue works,the number of high-quality works,help students times,performance of class work,classroom practice and classroom learning time.The part also chooses eigenvalue for prediction model with Pearson correlation coefficient.The third chapter analyzes the student behavior data of the e-learning platform.According to the characteristics of each classification algorithm,K-nearest neighbor algorithm and decision tree algorithm are selected to establish the model and compare the correct rate of the first model.Then add the third eigenvalue to upgrade the model to improve the correct rate.The fourth chapter uses scatter plot to analyze the relationship between eigenvalues and learning effects.It proposed corresponding strategies for teachers,students in different levels from the changes of accuracy in different established models.These strategies were implemented to measure their impact to learning outcomes.The fifth chapter is the summary and expectation of this paper.The study used high school students'behavior data in the information technology teaching platform occur in the classroom,that is,track students'behavior by online platform in class as well as after class,and select the three behavioral characteristics that more impact with the performance.It proposed a series of teaching strategies from the teaching level,the learning level as well as the platform improvement level.Especially at the platform improvement level,the author builds a webpage with the functions required by the strategy and applies it to the actual teaching,which also improved the friendliness of the platform.