Goal Setting and Instructional Design in Music Education Based on Bloom’s Cognitive Hierarchy Theory

Abstract

This paper first introduces Bloom’s cognitive hierarchy theory of music education goal classification and revises it to construct a new version of Bloom’s educational goal-setting system. After that, it adopts multi-objective hierarchical analysis (AHP) to calculate the weights of the music education objectives assessed by experts and optimizes the convergence of AHP based on Bergh’s improved particle swarm (PSO) optimization algorithm. New music education objectives and teaching contents were created based on the experimental results. The CR values of the six cognitive subprocesses of the second-level evaluation indexes were 0.0001, 0.0011, 0.0014, 0.0014, 0.0023, and 0.0029, respectively. The results of the consistency test of the first-level and second-level indexes were all CR<0.05, indicating that their consistency and trustworthiness were extremely high. The top three indicators in terms of the weights of the secondary indicators are inference, recall and differentiation, whose weights are 0.5723, 0.5276 and 0.5192, respectively; the three indicators with the lowest proportion are interpretation (0.2422), normalization (0.2661) and creation (0.3201). The purpose of this paper is to create a music course instructional design system with a 4-dimensional and 18-indicator system and evaluate the importance of each indicator. It fully embodies the basic spirit of quality education and reflects the common requirements of the new curriculum system of colleges and universities that combines epochal, basic, and selective. The teaching design system proposed in this paper is expected to serve as a reference for the current music teaching reform.

Keywords

• bloom’s cognitive hierarchy theory
• particle swarm algorithm
• hierarchical analysis
• music teaching design
• 97m80