Using learning analytics technology to mine online learning features can optimize the teaching process. On the basis of collecting students' online learning information, the similarity between features is firstly analyzed; secondly, machine learning algorithm is used to construct a student's learning performance prediction model, and the accuracy and K value of the model are analyzed; the teacher's teaching process reform puts forward the corresponding direction.
This paper analyzes 48923 examinee's achievement data in a high school academic level examination in a city, and comprehensively evaluates the overall distribution of the examination from the aspects of average score and distribution of examination results. Pearson correlation coefficient is used to study the correlation between courses, cluster analysis is carried out for the courses with high correlation coefficient, and the courses with high correlation with the total score are also analyzed. Improving the score of the course can more effectively improve the total score. Finally, the LSTM method is used to predict and analyze students' performance, and compared with the results of SVM and logistic regression to verify the relationship between courses with high correlation, which provides a scientific reference for teaching management, teachers' teaching according to their aptitude and students' learning.
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