Abstract
Abstract This paper establishes a model for teaching dance innovation in colleges and universities based on big data. Using a clustering algorithm to obtain the characteristics of dance teaching using affiliation assignment calculation to maximize the fuzzy indicators, the dance innovation teaching indicators are divided to improve the assessment accuracy. The optimization of dance teaching assessment indicators is normalized to complete the data processing and clustering in the teaching system and combined with the user preference to divide the student group based on similar characteristics. The results show that 45.00% of the student’s performance ability of physical balance is excellent, and the dance teaching classroom question score is 93. Big data technology can effectively integrate and categorize the existing dance teaching resources in colleges and universities, thus improving the quality of dance teaching.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
