EXPLORATION OF THE MULTIPLE

INTEGRATION MODE OF MODERN

INTELLECTUALISED MUSIC TEACHING AND

TRADITIONAL MUSIC CULTURE

Mingyang Zhang*

• Department of Music, Art College, Taishan University, Tai'an, Shandong, 271000,

China

•mingyang322@163.com

Reception: 21 February 2024 | Acceptance: 10 April 2024 | Publication: 15 May 2024

Suggested citation:

Zhang, M. (2024). Exploration of the multiple integration mode of modern

intellectualised music teaching and traditional music culture. 3C

Empresa. Investigación y pensamiento crítico. 13(1), 138-155. https://doi.org/

10.17993/3cemp.2024.130153.138-155

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138

ABSTRACT

In this paper, the elite TLBO algorithm is utilized to integrate modern vocal music

teaching with traditional music culture, and a feedback phase is introduced to improve

the algorithm's optimization accuracy and stability. An intelligent teaching framework is

constructed to integrate the integration of different forms and repertoire of traditional

music, as well as the integration of phonetics and phonological systems. Artificial

intelligence is integrated with modern sound teaching through traditional music

culture, and an experimental test and satisfaction survey is conducted in a university

as an example. The results show that the percentage of students' time spent on

independent learning outside the classroom rises rapidly from 51% to 77%, while on

the contrary the percentage of time invested in entertainment decreases from 26.6%

to 11.5%. Satisfaction surveys of teachers using the application as well as students

were conducted, with all six evaluation components scoring above 9.0. The study

shows that the teaching mode of integrating traditional music culture in modern

intelligent teaching can enhance the students' vocal skill ability, which in turn improves

the students' vocal art quality and improves the overall level of vocal music teaching.

KEYWORDS

Elite TLBO algorithm; feedback stage; phonological system; artificial intelligence;

intelligentized teaching

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INDEX

ABSTRACT ...................................................................................................................................2

KEYWORDS .................................................................................................................................2

1. INTRODUCTION .....................................................................................................................4

2. LITERATURE REVIEW ..........................................................................................................4

3. APPLICATION OF INTELLECTUALIZATION TECHNOLOGY IN MUSIC TEACHING ........6

3.1. Artificial intelligence empowers music teaching .................................................6

3.2. instructional Optimization Algorithm ...................................................................6

3.2.1. Elite Algorithm .............................................................................................6

3.2.2. Teacher phase .............................................................................................8

3.2.3. Student phase .............................................................................................9

3.2.4. Algorithm flow ..............................................................................................9

4. CONSTRUCTION OF A MODERN INTELLIGENTIZED MULTI-INTEGRATION MODEL ...11

4.1. Intelligent Teaching and Learning Framework ..................................................11

4.2. Integration of traditional music and cultural elements ......................................12

4.2.1. Traditional music forms and repertoire ......................................................12

4.2.2. Metrical and Phonetic Systems .................................................................13

5. ANALYSIS OF THE EFFECTIVENESS OF THE INTEGRATION OF MODERN

INTELLECTUALIZATION AND TRADITIONAL MUSIC CULTURE ....................................13

5.1. Comparison of learning outcomes ...................................................................13

5.2. Analysis of learning interests ...........................................................................14

5.3. Teacher and student experience of use ...........................................................15

6. CONCLUSION ......................................................................................................................16

REFERENCES ............................................................................................................................17

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1. INTRODUCTION

At this stage, has fully entered the information society, education and teaching,

should also be fully integrated with intelligent related technology to improve the quality

of music classroom teaching [1]. Therefore, music teachers should be aimed at the

characteristics of students who are full of curiosity about new things, actively develop

and utilize information technology, explore the integration strategy of music teaching

and intelligent technology, and then help students fall in love with music and develop

good learning habits [2]. Creating an intelligent teaching environment on the one hand

changes the teaching method of the teacher-led classroom and provides students with

rich database resources [3]. Students use network resources to understand the major

teaching platforms and learn the relevant knowledge of the music curriculum, and at

the same time, they can make personalized choices according to their own learning

characteristics in terms of teaching materials, content, exams, forms of expression,

etc., to achieve personalized learning [4-5]. On the other hand, to improve students'

learning conditions, students, with the support of diversified and modernized

technologies, can quickly stimulate learning motivation, enter the world of music,

experience the creator's emotions, and form music literacy in an image and vivid

environment [6-7].

In this paper, artificial intelligence is firstly used to enhance learning efficiency by

personalized learning experience and intelligent content recommendation. Secondly,

elite algorithms are used to optimize teaching plans and content to ensure more

efficient and goal-oriented teaching activities. In the teacher phase, it helps teachers

to adjust teaching methods and materials based on students' progress and feedback.

In addition, an intelligent teaching framework was established to combine traditional

music and cultural elements with modern teaching techniques. Integration of different

forms and repertoire of traditional music, as well as the integration of tonal and

phonetic systems, thus utilizing the advantages of modern teaching methods while

maintaining the authenticity and depth of traditional music education. Through these

methods, not only can traditional music be taught and passed on more effectively, but

intelligent technology can also be utilized to improve the teaching effect and create a

modern music teaching environment that is multifaceted and integrated.

2. LITERATURE REVIEW

Jiandong Cai used neural networks for audio time domain and frequency domain

feature extraction to construct a music pattern library, a synthesis algorithm to

generate a music training model, and a GRU model for music training and model

prediction. The experimental results show the conclusion that adopting the teaching

mode of traditional music and culture integration can improve students' music skills

and artistic literacy, which in turn improves the accomplishment of students' learning

objectives and improves the level of music teaching [8]. Yan Bai constructed a variety

of music integration teaching methods by analyzing the development characteristics of

music teaching, and used adaptive sampling and BP network-based Markov chain

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Monte Carlo methods to conduct teaching evaluation. The prediction accuracy of the

model constructed in this thesis reaches more than 94%, and the relative error is

controlled within 1.5%. It shows the feasibility of teaching the integration of modern

popular music and traditional music culture through the BP neural network model and

provides a meaningful teaching quality evaluation system [9]. Gegen bilige used the

association rule algorithm to establish a computational model for the integration of

university music teaching and traditional music culture, and used the indexes of the

integration degree, the value, and the acceptance degree to the degree of integration

and the effect of integration are analyzed. The results of the study show that the

combination of college music teaching and traditional music culture is a feasible and

effective teaching strategy with popularization value [10]. Yingxue Zhang et al.

develop a recursive neural network music-based automatic synthesis technology for

melody teaching. First, a strategy for extracting acoustic features from musical

melodies was proposed. Secondly, a sequence model was used to synthesize general

music melodies. After that, a synthesized musical RNN melody is set up to combine

with a singing melody, e.g., to find a suitable singing clip for a musical melody in a

teaching scenario. The RNN can synthesize a musical melody with a short delay

based on static acoustic features only, thus eliminating the need for dynamic features.

Experiments have proved the effectiveness of the model [11]. Jun Hao analyzed the

degree of information diffusion of traditional music culture in music teaching in

colleges and universities by combining the information diffusion model, and the results

show that the integration of different types of traditional music cultures has different

impacts on music teaching; traditional music is mainly integrated into music teaching

through musical emotions and tunes. Therefore, integrating the emotion of traditional

music culture into music teaching can enhance students' understanding of music and

improve their perception of music emotion to a certain extent [12]. Jun Hao believes

that the co-development of music teaching in colleges and universities and the

inheritance of national music culture is an important topic. In music teaching in

colleges and universities, it is necessary to strengthen the attention to and inheritance

of national music culture, so that students can understand and experience the

essence of national music culture while learning music [13]. Li Sun puts forward

positive and effective specific paths for the integration of traditional music culture into

the teaching of vocal music in universities to strengthen the traditional music culture

literacy of vocal music teachers, further innovate the teaching methods of vocal music,

utilize the advanced teaching technology for teaching, enriching students' emotional

experience in the teaching process, as well as choosing suitable music works to

cultivate students' perceptual ability [14]. Yuan,Y. proposed that multimedia can

improve students' thinking ability and cultivate students' thinking quality, which,

combined with the vivid function of Cal courses, greatly stimulates students' interest in

learning, and put forward that computer-assisted teaching will be the direction of the

future reform of education [15]. He,J. used multimedia digital technology to build a rich

and independent online learning learning environment. Teachers can present music

teaching content through Internet teaching resources, and students learn music

through online platforms to realize interactive learning [16].

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3. APPLICATION OF INTELLECTUALIZATION

TECHNOLOGY IN MUSIC TEACHING

3.1. ARTIFICIAL INTELLIGENCE EMPOWERS MUSIC TEACHING

The value of technology in education is not determined by technology, but by

people. Therefore, AI-enabled teacher education should first follow the law of human

understanding of things [17]. At the same time, it should also make scientific decisions

about the main contradiction and the main contradictory aspects of AI-enabled

teaching and teaching research, that is, how to accurately empower for different

contents and different periods of time, so as to construct an intelligent and precise

boosting matrix [18]. Based on the above ideas, the realization path of AI-enabled

teacher education is constructed as shown in Figure 1. First, it should follow the law of

scientific understanding, and the law of teachers' and students' understanding of

things is the foundation and premise of AI-enabled teacher education. For example, in

the case of practical training on music lesson preparation ability, teachers or teacher

trainees should first have practical experience through studying excellent teaching

design, trying to prepare lessons for a specific music course, etc., to form a perceptual

understanding of lesson preparation. Secondly, it should be facilitated based on the

precision matrix, and the use of the precision teaching matrix can make teaching more

precise, and promote students' efficient learning by constructing precise teaching

strategies that match the music teaching stage and different lesson types [19].

Figure 1. Ai-enabled teacher education path

3.2. INSTRUCTIONAL OPTIMIZATION ALGORITHM

3.2.1. ELITE ALGORITHM

Suppose there are two different teachers and , teaching the same music

subject in different classes, and the students in both classes have the same initial

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level [20]. Figure 2 shows the teacher and student distribution curves, Figure 2(a)

shows the distribution of student scores in the two classes under the teaching of the

teachers, Curve 1 and Curve 2 are the distributions of scores under the teaching of

Teachers and , respectively, and and

are the mean values of Curve 1 and

Curve 2, respectively. Assuming that the scores follow a normal distribution, curve 2

has a higher mean than curve 1, and it can be said that teacher

is better in teaching

than

. Besides the help of the teacher, the students improve their scores by

communicating with each other. Figure 2(b) shows the curve of students' obtained

scores and curve

represents the model of distribution of scores obtained by

students in a class. Teachers are the most knowledgeable people in the society, so

the students who get the highest scores act as teachers, and teachers

impart

knowledge to the students, which increases the average score of the whole class.

Teacher endeavors to bring the class mean score from

closer to the new mean

score

by teaching the students knowledge, so that the students in turn need new

teachers with more knowledge than the students, i.e., new teachers

on the new

curve .

Figure 2. Distribution curve of teachers and students

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(1)

where is the variance, is the mean, and is a normally distributed random

variable. Like other algorithms inspired by natural phenomena, the TLBO algorithm is

a population-based algorithm. In TLBO algorithm, the number of students is the

population number of the algorithm, the music learned by the students is the

independent variable, the result of the students' learning is the fitness value, and the

teacher is the current best solution. The TLBO algorithm is divided into a teacher

phase and a student phase, the teacher phase is for the students to learn the

knowledge from the teacher, the student phase is for the students to learn the

knowledge by communicating with each other, and the outputs of the teacher phase

are used as inputs for the student phase.

3.2.2. TEACHER PHASE

At any number of iterations , is the mean, is the teacher, and the teacher

tries his best to keep the mean close to his level so that the new mean is

close to . The difference between the current mean and the new mean is given by

equation (2):

(2)

where is a random number from 0 to 1, is the teaching factor, which

determines the extent to which the mean value is changed, and is randomly

determined to be 1 or 2 by equation (3), i.e:

(3)

The teacher phase updates the current solution according to equation (4):

(4)

Accept if is better than .

Determine the average level of the student population for any number of iterations.

The goal of the teacher, as an expert or best practitioner in the field, is to bring the

average level of the student population as close as possible to his or her own level.

This approach is similar to mentorship in modern teaching, in which the teacher helps

students progressively reach higher skill levels by providing expert guidance and

feedback. This is achieved through a specific mathematical formula. Here, a decisive

role is played by the teaching factor, which controls the extent to which the mean

value changes, and this factor itself is determined by the rule of randomly determining

it as 1 or 2. This element of randomization increases the flexibility and adaptability of

the teaching process, allowing the teaching method to be adjusted to different

f(x) =

2πσ

exp

(

−

(x−μ)2

2σ2

)

σ2

Mnew

DifferenceMean i=ri(Mnew −TFMi)

TF= round[1 + rand(0, 1)]

xnew ,i=xold ,i+ Difference Mean i

xnew

xold

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situations and student needs. Further, another task of the teacher phase is to update

the current pedagogical solution according to a specific update formula. This updating

is based on comparing the effects of the current solution with the new one, thus

ensuring that the teaching process is always moving towards the optimal outcome. If

the new solution is superior to the current one, then it will be accepted and applied in

the teaching practice.

3.2.3. STUDENT PHASE

The student phase is for students to randomly interact with each other, where

students are able to acquire new knowledge from students who have more knowledge

than they do. Is the independent variable of the optimization problem and

is the

objective function of the optimization problem. After the teacher phase, two students

and are randomly selected, where . Firstly, the values of the objective

function corresponding to the two students are compared, and if

, it

means that student is better than student , then is closer to

, as shown in

equation (5):

(5)

Conversely, student is superior to student , then moves closer to

shown in equation (6):

(6)

After the student stage process, compare new solution

with current solution

and accept if is better than .

3.2.4. ALGORITHM FLOW

In the elite TLBO algorithm, students improve their scores only through teachers'

teaching or communication with students, which is a single learning method [21-22].

However, in the actual student learning process, students often also with the teacher

active and purposeful feedback exchanges, through the feedback for their own

learning knowledge to check the gaps and fill in the gaps can get more knowledge,

which can further improve the students' scores. Therefore, this paper introduces a

feedback phase based on the elite TLBO algorithm to improve the algorithm's

optimization accuracy and stability.

The feedback phase is added after the student phase so that students improve

their scores not only through the teacher's teaching and students' communication with

each other, but also through students' direct feedback communication with the

teacher. After the student stage, two students and are randomly selected, where

. Compares the corresponding objective function values of the two students, and

f(x)

i≠h

f(xi)<f(xh)

xnew

xnew ,i=xold ,i+ randi(xi−xh),f(xi)<f(xh)

xnew

xnew ,i=xold ,i+ randi(xh−xi),f(xh)<f(xi)

xnew

xold

xnew

xold

i≠d

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if , it means that student is better than , then student is selected to

have a feedback exchange with the teacher, as shown in equation (7):

(7)

Instead, students were selected for a feedback exchange with the teacher, as

shown in equation (8):

(8)

After the feedback stage process, compare the new solution

with the current

solution and accept if is better than Student satisfaction.

The addition of the above feedback process increases the learning mode of

students, ensures the diversity of students and improves the global search

performance of the algorithm. At the same time, the feedback stage enables poorer

students to quickly approach the current optimal individual teacher, and the search

range is quickly converged to the vicinity of the optimal solution, and in the algorithm's

termination conditions iterative number of generations must be certain, the algorithm

carries out the local fine search in the later stage of the number of generations of the

relative increase, so that the algorithm's optimization search accuracy and stability will

be improved.

Feedback elite teaching algorithm for optimization problems, the algorithm flow is

shown in Figure 3, the steps are as follows:

Define the optimization problem and initialize the parameters of the

optimization problem, initialize the number of group members, the number of

iteration generations, the number of independent variables and the constraints

of the optimization problem.

According to the number of group members and the number of independent

variables, randomly generate the initial population.

3. Evaluate the population and retain the elite solution.

4. Teacher stage, teaching process in teacher stage according to equation (4).

Student phase, according to Eqs. (5) and (6) students are randomized to

communicate with each other to improve their performance.

Feedback phase, according to Eqs. (7) and (8) students engage in feedback

exchanges with the teacher to improve student performance.

7. Elite solutions replace poorer solutions.

8. Randomize the variation operation on the elite solution.

9. Repeat steps 3 to 8 until the end condition is satisfied.

f(xi)<f(xd)

xnew ,i=xold ,i+ randi(Mnew −xd),f(xi)<f(xd)

xnew ,i=xold ,i+ randi(Mnew −xi),f(xd)<f(xi)

xnew

xold

xnew

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Figure 3. Algorithm flow

4. CONSTRUCTION OF A MODERN INTELLIGENTIZED

MULTI-INTEGRATION MODEL

4.1. INTELLIGENT TEACHING AND LEARNING FRAMEWORK

Intelligent teaching is therefore intelligent because the agent has a thinking state,

which is generally categorized into three parts: belief-knowledge, desire and intention

for education, which in turn can be integrated with the Agent's perception and

reasoning as well as planning and action [23]. The intelligent teaching conceptual

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model is shown in Figure 4, where the agent's belief-awareness state is used to

portray the user's learning needs and cognitive abilities in the teaching conceptual

model. The agent's information is stored in the knowledge base, the learning module

accepts the user's feedback and the KQML messages transmitted by other teaching

agents, and then converts these feedbacks and messages into the agent's information

about the user's learning needs and cognitive abilities according to certain inference

rules, and then utilizes this information to update the agent's information stored in the

knowledge base. Based on the agent's information, the matching module filters out

the learning resources from the resource base that are suitable for the user's needs

and abilities.

Figure 4. Intelligent teaching model

Suppose that in the teaching activity, the agent adopts a certain behavior , and the

representation of the credence on which behavior is based is

. The agent

updates the credence based on the following formula:

(9)

where is the feedback signal resulting from the agent's behavior

, and

denotes the learning efficiency, i.e., the extent to which the new credence

replaces the current credence.

4.2. INTEGRATION OF TRADITIONAL MUSIC AND CULTURAL

ELEMENTS

4.2.1. TRADITIONAL MUSIC FORMS AND REPERTOIRE

In modern intelligentized music teaching, the integration of traditional music forms

and repertoire is a key part of realizing multicultural inheritance. First of all, it is

necessary to clarify the traditional music forms to be fused, which include, but are not

limited to, classical music, ethnic music and so on [24]. These forms represent the

music culture of different historical periods and regions, and are the vivid expression

of traditional music. When choosing traditional repertoire, emphasis should be placed

S(a)

S(a)←S(a)+λ(r−S(a))

λ(0 < λ< 1)

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on representativeness to cover different styles and regions. This not only helps

students to fully understand the diversity of traditional music, but also provides them

with a broader learning horizon. By selecting representative repertoire, students can

deeply feel the essence of traditional music, experience the musical language of

different cultures, and promote their understanding and love of traditional music.

4.2.2. METRICAL AND PHONETIC SYSTEMS

Traditional rhythms often carry unique cultural connotations and have certain

differences from modern rhythms. In the process of integration, these differences

need to be studied in depth and a suitable way of integration determined. Adjustments

involving tonality, scales, intervals, etc., are involved to ensure that the integrated

music can inherit the traditional tonal characteristics while conforming to the context of

modern music. Focusing on the preservation of the unique tonal system of traditional

music is an important means of maintaining cultural heritage, including the protection

and inheritance of traditional timbres, performance techniques, and sonorities [25]. In

the integration mode, the traditional phonological system is integrated into modern

music teaching through the use of traditional instruments and the preservation of

traditional performance methods. Such integration not only allows students to feel the

uniqueness of traditional music, but also provides them with a more comprehensive

musical experience.

5. ANALYSIS OF THE EFFECTIVENESS OF THE

INTEGRATION OF MODERN INTELLECTUALIZATION

AND TRADITIONAL MUSIC CULTURE

5.1. COMPARISON OF LEARNING OUTCOMES

For the structural change of students' music assignment participation in the two

teaching contexts before and after the smart application, students' time investment

indicators are usually used to carry out observational analyses, and the difference in

students' time investment in learning leads to significant differences in their

perceptions of the effectiveness of teaching and learning before and after the smart

application. For this reason, this paper first utilizes the time investment of students'

participation and their time allocation structure to explore the changes in students'

academic participation paradigms in different teaching contexts.

Table 1 shows the study and recreation time before and after the smart application,

and the study shows that the total total time investment in students' weekly activities

increased during the online learning period, and that there was a slight increase in the

time spent on independent learning outside the classroom. However, it is of great

concern that the total weekly time investment of students' recreational time during

post-application teaching amounted to 23 hours, which is 1.8 times of the time

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investment in recreational time in traditional offline teaching contexts. Meanwhile, this

paper's investigation also found that students in online teaching situations spent an

average of 11 hours per week utilizing online social networking platforms such as

WeChat, QQ, and Weibo. When the students' learning field is changed from traditional

offline classroom to intelligent multiple integration mode, the proportion of

independent learning time outside the classroom rises rapidly from 51% to 77%, while

the proportion of entertainment investment time decreases from 26.6% to 11.5%.

Table 1. Study and recreation time before and after intelligent application

5.2. ANALYSIS OF LEARNING INTERESTS

The data obtained from the overall sample of subjects in the pre-test and post-test

were applied to the t-test to see the differences in the interest in music learning before

and after the integration of the multiple modes and their significance, Table 2 shows

the analysis of the results of the t-test of the samples, which shows a significance at

the 0.01 level of significance between the pre-music affective experience and the

post-music affective experience with t=-6.7 and p=0.000, and comparing the

differences it can be concluded that the pre-music affective experience mean of 2.6

would be significantly lower than the mean of 3.1 for the post-Musical Emotional

Experience. pre-Musical Perceived Focusing Ability and post-Musical Perceived

Focusing Ability showed a significance at the 0.01 level of t=-8.1, p=0.000, and a

comparative difference can be made to conclude that the mean of 2.7 for the pre-

Musical Perceived Focusing Ability would be significantly lower than the mean of 3.1

for the post-Musical Perceived Focusing Ability. music Novelty Associations pre and

Music Novelty Associations post show a significance at the 0.01 level t=-3.9, p=0.000,

and a comparative difference can be made to conclude that the mean of Music

Novelty Associations pre, 2.9, would be significantly lower than the mean of Music

Novelty Associations post, 3.1. Music Cognitive Explorations pre and Music Cognitive

Explorations post show a significance at the 0.01 level t=-9.6, p=0.000, and a

comparative difference can be made to conclude that Music Perceived Concentration

ability pre mean 2.7, would be significantly lower than the mean of Music Perceived

Concentration ability post, 3.1. 0.000, and a comparative difference can be made to

conclude that a mean of 2.8 for the pre-musical cognitive inquiry would be significantly

lower than a mean of 3.44 for the post-musical cognitive inquiry. The pre-musical

Before intelligent application After intelligent application

After-school study time 11.3 10.5

Fun time 23.0 12.2

Class time 18.6 23.9

Proportion of study time 51 77

Percentage of recreation time 26.6 11.5

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creativity challenge level and the post-musical creativity challenge level show a

significance at the 0.01 level, t=-9.2, p=0.000, and a comparative difference can be

made to conclude that a mean of 2.89, which is significantly lower than the mean

value of 3.3 after the music creation challenge level. 0.01 level of significance was

found between the pre-feeling of the music classroom as a whole and the post-feeling

of the music classroom as a whole, t=-10.1, p=0.000, and the difference in

comparison can be concluded that the mean value of 2.75 for the pre-feeling of the

music classroom as a whole is significantly lower than the mean value of 3.3 for the

post-feeling of the music classroom as a whole. Reflecting the modern wisdom of

music teaching and traditional music culture fusion, music teaching enhances the

students' learning interest.

Table 2. Analysis of sample T-test results

5.3. TEACHER AND STUDENT EXPERIENCE OF USE

To further validate the effectiveness of the Modern Intelligent Multi-Music

Integration Model, a scale of 1-10 was used to indicate the satisfaction of teachers

and students, with higher scores indicating higher levels of satisfaction. The results of

the teachers' and students' experience of using the program are shown in Table 3,

with all six evaluation components scoring above 9.0. Among them, the integrated

traditional music form was highly recognized by teachers and students, with a teacher

satisfaction score of 9.1 and a student satisfaction score of 9, providing strong support

Pair

number

item Mean

value

Standard

deviation

Mean

difference

t p

Music before emotional

experience 2.6 0.6

-0.3 -6.7 0.000**

Music after emotional

experience 3.1 0.7

Music perception before

focus 2.7 0.6

-0.3 -8.1 0.000**

Music perception after

focus 3.1 0.6

Music before new

associations

2.9 0.5

-0.2 -3.9 0.000**

Music after new

associations

3.1 0.7

Before music cognitive

inquiry

2.8 0.6

-0.5 -9.6 0.000**

After music cognitive

inquiry

3.44 0.5

Before the music

creation challenge

2.89 0.6

-0.4 -9.2 0.000**

After the music creation

challenge

3.3 0.5

Music class before the

overall feeling

2.7 0.5

-0.6 -10.1 0.000**

Music class after the

overall feeling

3.3 0.6

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for teaching. For the question of whether the integrated music retained the

characteristics of traditional meters, the teacher satisfaction was 9.2 and the student

satisfaction was 9.5, indicating that the integrated music performed well in retaining

the characteristics of traditional meters, and successfully inherited the unique charm

of traditional music.

Table 3. Results of teachers' and students' experience

6. CONCLUSION

For the long-term development of music teaching in colleges and universities, it is

necessary to integrate traditional music culture into music teaching. This study utilizes

the elite TLBO algorithm to integrate traditional music culture with modern music,

introduces a feedback phase to improve the algorithm's optimization accuracy and

stability, and gives a strategy for the integration of modern music teaching and

traditional music culture information. The classroom application is analyzed with the

students and teachers of a university as the research object. In terms of study and

entertainment time before and after the intelligent application, after the students'

learning field was changed from the traditional offline classroom to the intelligent

multivariate fusion mode, the percentage of independent study time outside the

classroom increased rapidly from 51% to 77%, and on the contrary, the percentage of

time invested in entertainment decreased from 26.6% to 11.5%. T-algorithm was used

to test the data to see the difference in music learning interest and its significance

before and after the fusion of multiple modes, and the results showed that music

teaching enhanced students' learning interest after the fusion of modern intelligentized

music teaching and traditional music culture. To further validate the effectiveness of

the modern intelligentized multivariate music integration model, a satisfaction survey

was conducted on the teachers who used the application as well as the students, and

the six evaluation components were all above 9.0 points. This indicates that the

integrated music performed well in retaining the characteristics of traditional sound

and successfully inherited the unique charm of traditional music. The above data

Evaluation item Teacher

satisfaction Student satisfaction

Whether the integrated traditional music forms meet the

teaching needs 9.1 9.2

Representativeness and coverage of traditional

repertoire 9.2 9.4

The rationality of integrating traditional music elements

into teaching content 9.0 9.0

Whether the integrated model can stimulate students'

interest in traditional music 9.5 9.4

Whether the integrated music retains the characteristics

of the traditional rhythm 9.2 9.5

Ease of use of the teaching platform using intelligent

technology 9.4 9.6

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indicate that students recognized the strategy of informational integration of music

teaching and traditional music culture proposed in this study, and believed that the

integration of traditional music culture into the modern music teaching mode could

create a good learning atmosphere and improve learning efficiency. It can develop

students' musical skill ability and increase their interest in traditional music culture.

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