This study aimed to analyze the relations between class competition and primary school students’ academic achievement, considering the possible mediating roles of learning anxiety and learning engagement. Participants were 1,479 primary school students from four primary schools in Zhejiang, China. We analyzed participants’ scores for class competition, learning anxiety, and learning engagement and their last two final exam scores. Class competition did not directly predict academic achievement, but indirectly affected academic achievement through learning anxiety and learning engagement. There were three (...) effect paths: class competition negatively predicted academic achievement by increasing learning anxiety; class competition positively predicted academic achievement by promoting learning engagement; and class competition affected academic achievement through multiple mediating effects of learning anxiety and learning engagement. This study highlights the important roles of learning anxiety and learning engagement in class competition and academic achievement, which have theoretical and practical significance. (shrink)

This paper presents a theoretical framework of the circular shift network coding system through the study of nonmultiple clustered interval music performance style conversion and the analysis of music conversion by using circular shift topology, and a series of basic research results of circular shift network coding is obtained under this framework. It reveals the essential connection between scalar network coding based on finite domain and cyclic shift network coding, designs a solution construction algorithm for cyclic shift network coding under (...) multicast network, and portrays the multicast capacity of cyclic shift network coding. It overcomes the problem that the piano roll-curtain representation cannot distinguish between a single long note and multiple consecutive notes of the same pitch, describes musical information more comprehensively, extracts musical implicit style from the note matrix based on autoencoder, and better eliminates the potential influence of musical content on musical performance style. A two-way recurrent neural network based on the gated recurrent unit is used to extract a sequence of note feature vectors of different styles, and a one-dimensional convolutional neural network is used to predict the intensity of the extracted note feature vector sequence for a specific style, which better learns the intensity variation of different styles of MIDI music. (shrink)