Xi'an Conservatory of Music

The purpose of this study is to explore how to improve the learning effect of university front-end development courses through effective strategies and teaching practices. In recent years, with the rapid development of Internet technology, front-end development skills have become the core content of computer science education. However, the challenges college students face in front-end development courses include the complexity of knowledge, the practical application of projects, and the maintenance of learning motivation. Based on the theoretical framework of behaviorism and constructivism, this paper analyzes how different types of students benefit from a variety of teaching methods such as reinforcement feedback, project-driven learning, and collaborative coding. The main objective of this study is to optimize teaching methods and improve students' learning efficiency and effectiveness in front-end development courses. The study uses a qualitative approach, utilizing interviews and classroom data analysis to assess the impact of these teaching strategies on different student groups. The findings show that enhanced feedback and timely correction of errors, through progressively more complex task design and project-based learning, significantly improve student engagement and skill mastery. In the discussion part, the practical application effect of these strategies and the insights for future teaching are analyzed. This paper puts forward some suggestions for the improvement of future research and teaching practice. The novelty lies in the innovative teaching strategies proposed by combining the reinforcement mechanism of behaviorism with the knowledge construction method of constructivism.

Abstract University music and art teaching is an important link to improve students ‘literary literacy, enhance students’ cultural confidence, and improve students’ comprehensive ability. And for the university music traditional education mode exists a low degree of feedback, teacher-student interaction is difficult, teachers are difficult to accurately and quickly grasp the students’ movements and other problems, which is not conducive to the improvement of teaching quality. Personalised teaching mode can be a good solution to the above problems, but due to its limitations, it has not been used on a large scale in the past. With the rapid implementation of informatisation and digitalisation in the information age, it provides technical support for the large-scale popularisation of personalised education. The purpose of this paper is to explore the optimisation method of the machine algorithm model for personalised teaching mode of university music classroom in information technology environment. We introduced deep learning technology and constructed a personalised teaching feedback model based on dynamic grouping, automatic information collection system and graph convolutional neural network fusion. And we designed a personalised teaching model for university piano personalised teaching supported by personalised recommendation system. Finally, the performance and accuracy of the constructed model are verified by comparison. The results show that the personalised teaching model constructed in this paper has achieved good application results in piano teaching, with students’ satisfaction reaching nearly 100% in a semester’s learning time, while the unique weight allocation table further ensures that the model is always consistent with the goals of university music education during the optimisation process. The constant feedback and dynamic grouping during the optimisation of the learning algorithms described above allows for a rapidity and subjectivity that is not possible in a static model, which is in line with the educational requirements for student and teacher motivation and autonomy in curriculum design. The model can be used not only in the personalised classroom design of music teaching, but also to optimise the traditional teaching model, which also provides a reference value for educators to implement personalised teaching.

Cultural dance fosters social cohesion, emotional well-being, creative thinking, and cultural identity by conveying cultural values and social meanings. However, systematic comparative research on cross-cultural and indigenous cultural dance education remains limited. This study, adopting a global perspective, examines the current applications, core issues, and educational strategies in this field. Through a systematic literature review and grounded theory approach, the research scrutinizes studies conducted from 2010 to 2024. The results show that intercultural research primarily focuses on multicultural exchange, cultural integration of immigrant groups, and cultural acceptance among socioeconomically disadvantaged populations, highlighting the influence of intercultural adaptability and globalization. In contrast, indigenous cultural research emphasizes local culture and educational systems, with a particular focus on local cultural identity and educational reform. While both types of research reach a consensus on the importance of cultural transmission, diversity in teaching strategies, and the critical role of educators, significant differences remain in cultural identity and the design of educational content. Future research could concentrate on virtual dance education, innovation in indigenous cultures, the exploration of dance education's role in mental health, and the deep integration of artificial intelligence into dance pedagogy. Furthermore, consideration of the potential impact of globalization and technological advancement on cultural identity and educational models will foster theoretical innovation and practical development in dance education.

The current noise reduction method only uses the filter to deal with a certain kind of special noise to achieve the purpose of noise reduction. When applied to electronic music noise reduction, the noise reduction effect is poor, low efficiency, and affect the human ear sensory experience of music. Aiming at the above defects, a method of electronic music signal denoising based on Naive Bayes is studied. After digital preprocessing of electronic music signal, spectrum characteristics of electronic music are analyzed from the angle of human ear. After node filtering of wavelet packet decomposition tree by Naive Bayes model, the electronic music signal is decomposed and reconstructed by the optimal wavelet packet decomposition tree to complete noise reduction. In the noise reduction experiment, the signal-to-noise ratio of signal processed by naive Bayesian method is reduced by about 2∼5dB, and the noise reduction efficiency is improved by about 32.87%, and the noise reduction effect is better.

Music is a kind of language expressing emotion, different structures and content contain different emotions. For the music emotion classification problem, a novel music emotion recognition strategy is proposed using audio features and machine learning. The developed scheme includes four stages: preprocessing, feature extraction, feature selection, and classification. As a first step, the signals in the data set are stably continuous by framing and windowing in processing. Secondly, a 1x140 size feature is derived from signal to profile the detailed music emotion. In the third step, the extracted features are selected and pre-weighted based on the feature weighting algorithms (ReliefF), max-relevance and min-redundancy (mRMR). In the final step, the data are led to the convolutional neural network and the long short-term memory (CNN-LSTM) combination model to recognize music emotions. The experimental results approve that the proposed CNN-LSTM based on ReliefF & mRMR (RM-CNN-LSTM) feature processing method could obtain higher identification accuracy, and all indicators of the proposed model improve significantly.

Abstract In psychology, emotions such as anxiety, tension, anger, depression, sadness, and pain are collectively referred to as negative emotions. Such emotional experiences are not positive, and the body will feel uncomfortable, and even affect work and life. The probability of college students suffering from negative emotions is higher than that of other groups, which is a group that needs special attention. Emotion and cognition are closely related. Studies have shown that negative emotions can cause problems such as decreased attention and memory. We used Self-Rating Anxiety Scale (SAS), Self-Rating Depression Scale (SDS), and Positive and Negative Affect Scale (PANAS) to evaluate negative emotions, and screened out those with SAS and SDS scores greater than 35 points and PANAS with moderate negative emotions. 50 Yanshan University student subjects with emotional score greater than 20 points. There were 20 subjects for music stimulation test, 20 subjects for neurofeedback training and 10 subjects for control group. The trial lasted for two weeks, 5 days a week, 5 minutes a day. Before the test on the first day and after the test on the last day, neuropsychological scales and attention tests were completed. Before training and after training every day, the EEG signals of the subjects in the resting state were collected. We analyzed the effects of the two intervention methods on the characteristic parameters of brain function, and then analyzed their effects on mood and cognition improvement. In particular, we analyzed differences in laterality of the brain by relative power changes, interhemispheric EEG coherence. The results of neuropsychological scale test and attention evaluation showed that both negative emotion and attention were improved with statistical significance. The change in the control group was not statistically significant. It is noted that the trail has been prospectively registered in ChiCTR on Oct 25, 2022. The trail registration number (TRN) is ChiCTR2101245362.

The present article presents an innovative and versatile approach to formative assessment that allows educators to assess students’ aptitude for online learning and their mastery of Popping dance. Popping dance, a form of breakdance, is increasingly being taught through distance learning programs. Despite the growing interest in Popping dance culture across different regions worldwide, few studies have focused on developing a comprehensive formative assessment model that combines Popping dance abilities and online learning. Therefore, we have developed a novel formative assessment framework for Popping dance students, which utilizes various online formats and offers a diversified assessment model. The article outlines five distinct assessment methods with practical examples that can enable students in heavily impacted areas to improve their Popping dance technique at home. These assessment methods cover software operation, theory testing, and technical testing, all of which are suitable for online use. While this model is still in its early stages, it provides a fundamental methodological framework for assessing the Popping dance proficiency of students.

In today's highly competitive society, student management in universities not only focuses on students' academic performance, but also focuses on cultivating their comprehensive qualities and positive attitudes. Positive psychology, as an important branch of psychology, can help college students establish a correct outlook on life and values, enhance their confidence and adaptability by studying and applying positive emotions, positive thinking, and positive behavior. This article will explore the application of positive psychology in student management in universities and discuss its positive impact on student development.

Online learning experience is an important research direction of online learning in recent years.It is important to explore the key factors of online learning experience and their structural relations to improve learners' online learning performance.Aiming at the special group of conservatory students, it is of great significance to understand the key factors affecting the online learning experience of conservatory students to promote the online teaching practice and improve the learning results of conservatory students.Firstly.thisstudy identified the influencing factors of online learning experience of conservatory students through literature research and interviews.which are divided into 14 indicators from 5 dimensions: learners, platform function, course content, teachers, and interaction support.Then, a questionnaire was developed for investigation.Through SPSS data analysis and AMOS structural equation modeling, the structural relationship model of influencing factors of online learning experience of conservatory students was constructed.The results showed that the key influences on online learning experience of conservatory students are as follows: teachers' professional knowledge literacy, teachers' (teaching assistant team) support and service, content features, learning harvest, teacher-student interaction, self-learning ability and teaching strategies.Finally, some suggestions were put forward to improve the online learning experience of conservatory students.

Under the background of the in-depth development of Internet + education, the deep integration of virtual reality technology and dance teaching is a new way to innovate the training mode of dance talents and satisfy students' all-round development and lifelong learning. This article introduces the pain points of the current dance teaching, and the breakthrough point of integrating virtual reality technology into dance professional teaching: using virtual reality technology to build immersive dance teaching environment, building dance online resource library, conducting virtual choreography and rehearsal, and simulation training. Finally, the paper shows the case of the application of virtual reality technology in dance teaching through the lesson of "Dunhuang Dance", designs the six links of the whole process of teaching implementation, introduces the process of making Dunhuang Dance courseware with VR content making tool Nibiru Creator, and constructs the dance course mixed teaching mode based on virtual reality technology, On this basis, experimental and interview methods were adopted to study the influence of immersive VR micro-lessons on students' learning, with the presentation of micro-lessons as independent variables and the positive emotions and knowledge acquisition scores of choreography major students as dependent variables. Through experimental research, it is found that immersive VR micro-class has extremely high application value in students' emotional guidance and knowledge understanding. It can bring students immersive emotional experience and interactive experience exploration of VR courses in dance teaching, and guide students to think positively and deepen their understanding of learning content. After that, this paper analyzes and discusses the experimental results, analyzes the limitations of the experiment, and makes some assumptions and prospects for dance VR teaching, hoping to provide some enlightenment for practice.