Abstract
Intelligent Guided Learning System (ITS) is a computer system that uses computers to imitate the experience and methods of teaching experts to assist in teaching; ITS provides learners with personalized learning resources and adaptive teaching methods, thus reducing students’ dependence on teachers and realizing independent learning. After years of research and development, and with the help of artificial intelligence technology, ITS has basically formed a stable structure and unified implementation specifications and has given birth to many excellent products in disciplines such as basic computer, language, medicine, and mathematics. Based on the above background, this paper takes the system structure of universal ITS as the basis, combines the characteristics of music sight-singing subject in teaching contents and teaching methods, researches the intelligent guidance system model of music sight-singing, and completes the design of the overall system architecture. The specific strategies and implementation methods of the teaching methods, resource recommendation, and ability assessment components of the teaching model are studied. The research includes the definition of the difficulty characteristics of the score, the design of the score recommendation algorithm, the design of the sight-singing scoring algorithm, and the experimental analysis of each algorithm. It is proposed that the difficulty feature-based score recommendation algorithm is the core component of resource recommendation in the teacher model, and the sight-singing scoring algorithm is the basis for ability assessment and learner model update.
Highlights
Personalized teaching is the key to improve teaching and cultivate innovative talents, but in the traditional teaching environment, one-to-one personalized teaching for each student is difficult to achieve due to the limited teaching resources and teachers’ strength [1]
Providing learners with learning resources and learning environment cannot truly realize personalized teaching and requires the learning platform to actively interact with learners, understand their individual characteristics, and provide targeted teaching services for different learners by imitating the teaching style of teachers. e intelligent tutoring system uses artificial intelligence technology to simulate and learn the teaching style of human teachers, provide personalized learning paths and learning suggestions for different learners according to their needs, and understand learners’ learning habits and preferences through verbal communication, behavioral analysis, and simulation during the learning process to dynamically guide learners to complete the mastery of knowledge [3]
Artificial intelligence technology has made significant progress in deep learning, intelligent control, data mining, and artificial neural networks, which has greatly promoted the development of intelligent guidance systems, formed a standardized structural framework, and given birth to all many excellent products in disciplines
Summary
Personalized teaching is the key to improve teaching and cultivate innovative talents, but in the traditional teaching environment, one-to-one personalized teaching for each student is difficult to achieve due to the limited teaching resources and teachers’ strength [1]. Is study takes the existing research results of intelligent guidance system as the theoretical basis, combines the disciplinary characteristics of music sight-singing, and deeply investigates the model and its design method in the intelligent guidance system of music sight-singing, accumulating valuable theoretical basis for the implementation of the intelligent guidance system; at the same time, the algorithm proposed in this paper provides a feasible solution to the problems of personalized teaching resources pushing and ability assessment in music sight-singing. Models of melodic complexity were first proposed by Eugene Narmour, who suggested that the implicit realization model performed well in the description of melodic complexity of original folk songs but lacked consideration of the musical and cultural context of the listeners For this reason, Eerola et al proposed an expectation-based model (EBM) to estimate complexity, and they found that tonality (modified by rhythmic position and pitch duration), the interval principle of range direction and range variation, rhythmic principle slicing, and rhythmic variability showed significant ability in predicting listeners’ complexity judgments. Different studies select different types of features or combine different types of features according to their scoring purposes and focus, and the differences in scoring effects all depend on the selected features
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