Adaptive Classification Algorithm Design for Online Teaching Resources of Opera Singing

Abstract

In the context of the integration of each professional course’s online education platform and teaching knowledge system, teaching resources are experiencing explosive growth, and classroom-based instruction is gradually being replaced by resource-based instruction. In the modern era, the optimization, integration, and efficient use of online teaching resources have become central concerns in education. In particular, the categorization and integration of resources have become the central focus of teaching work. The cognitive stratification theory classifies and orders the educational objectives in the cognitive domain in a scientific manner, which provides teachers with important ideas and foundations for implementing online and offline integrated teaching designs centered on the students’ skill acquisition. Adaptive classification algorithm is introduced to subdivide the objectives of online singing teaching resources, and on this basis, the online resources are classified and integrated according to the characteristics of different teaching sessions, thereby achieving the efficient use of resources, which is an innovative path of singing resources classification and integration in the new stage of online resources teaching.