Modernization of Sight-Singing and Ear Training Assessment Standards: From Traditional Scoring to Data-Driven Feedback Mechanisms

Abstract

As an important part of music education, solfeggio ear training has long relied on traditional scoring standards to evaluate students' learning results. The traditional scoring system is usually based on the subjective judgment of teachers, and emphasizes the scoring of students' pitch, rhythm, timbre and other aspects. Although this method has certain historical value, it has many limitations in practical application, especially in the objectivity, accuracy and personalized feedback of assessment, which is difficult to meet the needs of modern education. With the development of science and technology, especially the application of big data and artificial intelligence technology, the traditional scoring system is facing the opportunity of transformation. In recent years, data-driven feedback mechanism has gradually become a new evaluation standard, which can analyze students' learning status in real time and objectively, and provide personalized suggestions for improvement, thus playing an increasingly important role in solfeggio training. The purpose of this study is to explore the modernization of evaluation criteria for solfeggio ear training, focusing on the transition from traditional scoring system to data-driven feedback mechanism. First of all, this paper reviews the evolution of the traditional grading system and analyzes its existing problems and shortcomings, especially the neglect of students' differentiated needs in the training process. Secondly, this paper introduces how the data-driven technology, especially the feedback mechanism based on artificial intelligence and machine learning, can be effectively applied in solfeggio training. By comparing the effect of traditional scoring and modern data-driven system, this paper reveals the advantages of data-driven feedback mechanism in improving training effect, stimulating students' learning motivation and realizing personalized teaching. Finally, this paper discusses the future development trend of this modern assessment standard and its application potential in other fields of music education. The study found that data-driven assessment systems can not only improve the accuracy and objectivity of assessments, but also provide real-time feedback to each student to help them optimize their skills as they practice. In addition, learning analysis based on big data can comprehensively reflect students' learning progress, thus providing teachers with a more scientific basis for teaching decisions. Although the current data-driven feedback mechanism is still in the development stage and there are still technical application challenges, it has great potential in improving the effect of solfeggio ear training and promoting the personalized development of students. To sum up, through the comparative analysis of traditional scoring criteria and data-driven feedback mechanism, this paper puts forward the modernization path of evaluation standards for solfeggio ear training, hoping to provide theoretical support and practical guidance for the innovation of evaluation standards in the field of music education.