Analysis of the Construction and Course Effectiveness of the Online Course Platform for College Piano in the Background of the Internet

Abstract

Abstract Internet technology continues to develop and deepen, course learning methods present diversity, and piano courses are an important way for colleges and universities to carry out students’ musical quality training. In this paper, we first propose a behavioral sequence analysis method for predicting students’ online course performance in the context of the Internet for college piano online courses. The sequence-based prediction task is divided into two stages in total. In the first stage, the sequence characteristics of students’ online course learning behaviors are predicted by mining the potential expressions of the implicit layer through deep RNN. In the second stage, the support vector machine is used to create an optimal segmentation hyperplane. The SPC two-stage classifier is able to predict student course grades in order to identify students with learning crises in time. Then a layered MVC model is used to design the university piano online course platform system. Finally, the performance of the platform and the effectiveness of the course were tested. When the number of concurrent users reached 150, the success rate of the server in processing transactions remained at about 96%, and the pre-test and post-test of students’ piano scores showed that the mean value of score-reading skills was 34.32 in the pre-test and 45.63 in the post-test, with a mean value improvement of 11.31. The study showed that the online course client had high operational performance and the online course could effectively the study shows that the online lesson client has high performance and the online lessons is effective in improving the students’ piano performance.