Evaluation of Music Education in Universities and Colleges: Implementing Decision Tree Algorithm

Abstract

The ensuring and improvement of value in advanced education is a perpetual cycle for the structure and development of college. Nature of music training guidance is remembered for school educating quality. The utilization of showing quality assessment instruments is vital because of music schooling’s one-of-a-kind concentration. This paper fosters a choice tree calculation-based assessment file framework for school MUSIC training instructing quality. Master meeting and scientific pecking order process are utilized to decide the assessment record and weight of framework for evaluating the nature of music training. To rearticulate it in an unexpected way, this paper centers around choice tree calculation stream and designs in the event that we examine information mining hypothesis overall. In order to show the value and quality evaluation framework, we established the “selection tree model” and adjusted the C4.5 calculation. In order to create a choice “Decision Tree Classifier,” we mainly establish a data mining algorithm. The model’s accuracy and materialness were checked by applying it to homeroom showing assessment information from 125 instructors. In the last investigation, key dynamic ascribes that affect how well music instruction educators are assessed and are featured, and in light of these discoveries, a sound exhortation is given.