Social and Cognitive Predictors of Collaborative Learning in Music Ensembles

Abstract

There have been many attempts to find ways to make music education more relevant and useful for pupils. Learning theories, performance-based learning, contract-learning, discovery-learning, cooperative learning, daily clocking, stage practice, and music-focused required and elective courses are all part of the implementation of these methods. Since high vocational students tend to have lower GPAs, it is imperative that they discover strategies to enhance their academic performance. Reform, rather than relying on theoretical frameworks, should be grounded on practical, innovative human actions. Both instructors and pupils possess the capacity to comprehend what they have learnt, according to the humanistic perspective. This paper provides evidence that collaborative learning is beneficial for first-year music practice students in a popular music program at a Chinese institution. The students work in small, diverse groups. Data was collected and analyzed from students over the course of one academic year with grades 4-6.. Collaboration is a powerful tool that has many applications, including but not limited to popular music degree programs, which is implemented in this paper using machine learning techniques. It zeroed down on seven important characteristics, all of which had obvious applications in the educational process. Another online course could use the method to predict students' performance, including real-time tracking of their progress and risk of dropping out, after it has been adjusted to capture relevant features corresponding to different contexts. This method could also be applied to other management learning platforms.