Data science has expanded at an exponential growth with the advancement of big data technology. The data analysis techniques need to delve deeper to find valuable information (Sarac 2017). The notion of edge computing is broadly acknowledged. Edge-enabled solutions provide computing, analysis, storage, and control nearer to the edge of the network, which support the efficient processing and decision-making. Machine learning has also attained significant attention in this context due to its flexibility and its ability to provide a variety of supervised, unsupervised, and semisupervised techniques. This research presents a specific model to evaluate the potential correlation of piano teaching using machine learning. The data analysis is performed at the edges of network for efficient results (Tan et al. 2017). The association rule mining technique of machine learning is utilized with the integration of improved
The emergence of Internet of Things (IoT) and the proliferation of machine learning have improved the way we act and think. Entering the new era of machine learning, an effective strategy for data analysis has become a challenging task. The existing models face challenges to have efficient data analysis and evaluation. Educational technologies (e.g., multimedia technology) improve the popularity of the modern educational system. Multimedia technology improves the quality of teaching in various applications. Among these applications, piano teaching is one such application that has benefitted from using multimedia technology. The use of modern educational technologies in piano teaching has been popularized in recent years. In fact, these technologies have promoted the application of modern education to a new horizon. Due to the continuous development of multimedia technology, various educational theories and teaching models have been put forward, so that multimedia piano teaching also faces some new problems. The intersection of these aspects is the need to scientifically evaluate and analyze the quality and effectiveness of multimedia piano teaching. At present, the existing evaluation system can realize the functions of data entry, query, and statistics but cannot discover the relationships and rules existing in the data and cannot predict the future development trend based on the existing data. Therefore, the big data mining technology based on machine learning is applied to the analysis of multimedia piano teaching evaluation data, which provides decision support for teaching managers, which is of great significance to the improvement of multimedia piano teaching quality [
The idea of edge computing is gaining prominence with the rise of complex data analysis. Edge-enabled solutions provide efficient processing and control nearer to the edge for scalability and latency management. Edge computing offloading technology uses computing-intensive that is hard to handle by mobile devices. The relocation of jobs to the servers nearby the edge device not only advances the processing capability of the devices. This paper proposes a multimedia-assisted piano teaching model based on big data machine learning. The data analysis is performed at the edges of network for efficient results. The association rule mining technique of machine learning is utilized with the integration of improved
The evaluation of multimedia-assisted piano teaching is not the purpose of evaluation activities. The ultimate goal of evaluation is to improve the work, scientific management, and research decision-making by evaluating the information obtained. This evaluation can be improved by using the edge-commuting. The edge-enabled analysis will improve the efficiency of the data analysis process. The evaluation information data do not spontaneously play a role. It is necessary to properly process the evaluation information along with its feedback. The feedback evaluation information exerts its comprehensive role as much as possible to make the evaluation activity effective. The data analysis factors are described in the coming sections.
The development of multimedia-assisted piano teaching is a complex systematic project, which is closely related to the corresponding theoretical research, technological progress, teaching practice, and so forth. Its teaching effect is influenced by many factors such as the information literacy of teachers and students, the quality of teaching courseware, and the teaching support platform. At present, multimedia-assisted piano teaching is still in the exploration stage in terms of teaching mode, teaching methods, and teaching strategies. Therefore, through the big data and machine learning technology to analyze the factors affecting the effect of multimedia teaching, it provides various links for improving multimedia-assisted piano teaching. Valuable information is of great significance to improve the quality of multimedia teaching.
In this information society, the efficiency of information transmission and the timeliness of feedback accelerate the process of social development. In the evaluation process, we must allow the advantage of using computer network technology in data processing and information transmission. The computer processing is made more efficient with the help of edge computation where the processing unit is provided with the network edges. The collected data are sorted and analyzed to obtain the distribution state of the data. The characteristics, rules, and relationship of the data is utilized at the edges and useful information is extracted for improving the quality of multimedia-assisted piano teaching and contributing to teaching management. According to the evaluation feedback information, solve various problems in the teaching in time, further optimize various aspects of multimedia-assisted piano teaching, give full play to the advantages of multicoal-assisted piano teaching, and lay the foundation for improving the quality of piano teaching.
Multimedia-assisted piano teaching combines the advantages of multimedia teaching and traditional teaching and plays a big role in improving teaching quality. However, at the same time, it also puts forward higher requirements for the quality and ability of teachers engaged in multimedia piano teaching. In the evaluation process, we must strive to provide teachers with useful information through the orientation, diagnosis, and timely feedback of the evaluation system with the help of edge computing. Through the analysis of the evaluation data, we find out the problems and deficiencies of teachers in all aspects of multimedia piano teaching and help teachers improve their multimedia information, emergency technology, and classroom integration ability to promote the self-improvement and improvement of piano teachers. The evaluation index of multimedia-assisted piano teaching model is shown in Figure
Evaluation index of multimedia-assisted piano teaching model.
Evaluation is a subjective activity, and the evaluator’s attitude is direct or not, which has a direct impact on the evaluation results. Scientific processing of the obtained evaluation data is important and an important issue to ensure the quality of the evaluation. Since the evaluation indicators are derived from the same target, there must be an intrinsic link between various indicators, which also leads to some correlation between the evaluation data of different evaluation indicators. We can find out that through the association rules. The correlation between the evaluation data is used, and it is checked whether the obtained evaluation data are reliable based on the correlation.
In this section, the machine learning-based association rule metrics are designed. The overall process is applied to the multimedia-aided piano teaching techniques considered as the edge of the networks. Initially, the standard is discussed in the context of association rules. A detailed description is provided in the subsection.
In the past few years, there have been many controversies about the problems of traditional support and credibility models, and many improvements have been proposed. Various new rule evaluation criteria have been proposed and added to the mining algorithm. The generation of association rules is restricted and constrained to obtain more novel and effective association rules. There are broadly two different categories. The first category is to try to find alternatives to the measure of credibility and to improve the objective evaluation method of extending the support threshold limit. The second category is to try to increase the subjective measure. Multimedia-assisted piano teaching data mining process diagram is shown in Figure
Data mining process workflow.
Data mining of multimedia-assisted piano teaching data is first performed. The data mining process can be divided into the following steps: Data cleansing: eliminating noise and data not related to the mining theme. Data integration: integrating data from multiple data sources. Data selection: selecting data related to mining topics. Data transformation: using data such as normalization to transform data into a form suitable for data mining. Data mining: the core steps to mine knowledge using methods such as classification, fusion, and association rules. Mode evaluation: evaluation of the effect of the model, the commonly used indicators have accuracy, recall rate, and so on. Knowledge representation: the model represented by a technically understandable model and the knowledge obtained by the mining presented to the user.
Relativity [
The interest of the basis difference is defined as follows:
According to the definition of interest, there are three possible scenarios for the metric of interest If the degree of interest is > 0, then If the degree of interest is <0, then If the degree of interest = 0, then
In fact, there are many definitions of the interestingness of the rules. The interestingness is defined in the literature [
This definition makes the interest level between −1 and 1, and the interestingness of a rule is greater than 0, indicating that the more interested in the rule (the greater its practical value), the more unreasonable the rule, and the more interested people are in the negative rule of the rule (that is, the greater the actual use value of the reverse rule). Among the factors influencing the quality of rules, in addition to the three factors that affect the degree of interest, such as coverage, implementation, and confidence, there are also rule complexity factors. In addition, Alex also analyzed several other factors that may affect the quality and interestingness of the rules, including the size of the rule front, the imbalance of class distribution, the influence of rule attributes, the cost of misclassification, and the classification rules.
The literature [ If If If
Introducing a threshold of relative confidence −1< relative confidence
According to the value of relative confidence, strong association rules can be divided into three categories: positive association rules, invalid association rules, and negative association rules [
The concept of validity is introduced in [
The intuitive meaning of the above formula is as follows: validity = (probability of simultaneous occurrence of
It can be seen from the definition of validity that if the validity of
Considering the possibility of
For example, If match >0, then If match = 0, then If match <0, then If match = 0, then
As can be seen from the formula, the definition of matching not only includes the relevance factor but also includes the factor of
The matrices can be improved in two different ways that are (1) insufficient measures of some association rules and
In the pattern of generating association rules, the classic support and confidence framework are generally used to generate association rules. However, when the framework is used to generate rules, a large number of redundant and irrelevant rules are generated, which affects the user’s choice of rules and even misleads the judgment [
A set of transaction data.
TID | Items |
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1 | |
2 | |
3 | |
4 | |
5 | |
6 | |
7 | |
8 | |
9 | |
10 |
From the table, it can be said that
The idea of the
Let the overall
But now
It can be known from the sampling distribution theorem that if
The first case: when the two population variances are unknown and equal,
The second case: when the two population variances are unknown and unequal, that is,
Thus, the test statistic of the two population mean difference test is
The paired sample
Among them
If the assumption is true, according to the sampling distribution theorem, the statistic
The evaluation of multimedia-assisted piano teaching is not the purpose of evaluation activities. The ultimate goal of evaluation is to improve the work, scientific management, and research decision-making by evaluating the information obtained. The evaluation information data do not spontaneously play a role, and it is necessary to appropriately process the evaluation information, give feedback on the evaluation information, and exert its comprehensive role as much as possible to make the evaluation activity effective. This is also the purpose of multimedia-assisted piano teaching evaluation data analysis. This chapter uses the LIBSVM toolkit and modifies it accordingly and then uses MATLAB to perform multimedia-assisted piano teaching modeling on real datasets.
The model mainly uses the association rule algorithm in data mining to sort and analyze the data and related data in the teaching evaluation database to obtain the distribution state of the data, the characteristics of the data, the change law of the data, and the model between the data. Provide useful information to improve the quality of teaching and provide decision support to managers. The model function diagram is shown in Figure
Model function diagram.
The model adopts an association rule mining (ARM) algorithm. ARM is a rule-based technique for identifying fascinating relations among variables. The relationship is discovered in the huge dataset. It is envisioned to recognize robust rules. The model adopts Apriori algorithm as the ARM algorithm of the system and adds the function of attribute selection based on Apriori algorithm. Then, in the association rule generation algorithm, the influence measure based on
Produce Get_ValidRuls (min_effect) Begin ValidRules = null; For each rule in begin effecti=(confidence_rule = if effect > min_effect then add rule to ValidRules return ValidRules; end End
This section provides the detail about results of the proposed model. The data collected at the edges of the network are analyzed using the machine learning approach. The mining algorithm of the above association rules is applied to the evaluation system of MAPT in a university. According to the existing evaluation result data of the school, some association rules can be obtained. Taking all the data of the multimedia-assisted piano teaching effect evaluation of the university from 2005 to 2007 as an example, the association rules of the teacher’s title, the teacher's highest age, the teacher's age, and the teacher’s multimedia-assisted piano teaching evaluation results are used. Association rule analysis result 1 is shown in Table Minimum support: 0.3 Number of iterations: 9 The generated large item set: Size of the big data item The size of the big data item The size of the big data item The best rules have been found.
Association rule analysis result 1.
Association rules | Confidence | Influence degree |
---|---|---|
Professor, doctor ⟶ excellent | 0.88 | 4.87 |
Professor ⟶ excellent | 0.83 | 4.32 |
Elderly ⟶ professor, excellent | 0.67 | 9.98 |
Doctor ⟶ professor, excellent | 0.52 | 2.16 |
lecturer ⟶ master | 0.69 | 6.87 |
Doctor, excellent ⟶ professor | 0.56 | 3.57 |
If we set the minimum confidence level to 80% and the minimum impact degree to 4, we can get the association rules “professor, doctoral
The data of the piano teaching effect from 2005 to 2007 are considered as a case study. The evaluation of the multimedia auxiliary piano teaching effect is considered in the case study. The first-level indicators of the multimedia classroom teaching evaluation standard in the multimedia-assisted piano teaching effect evaluation system are adapted. The major attributes are teacher quality, teaching design, and teaching process. Then the association rules between the teaching environment and the teacher's multimedia-assisted piano teaching evaluation results are extracted. Before the analysis of association rules, we quantified the evaluation scores of teacher quality, teaching design, teaching process, teaching courseware, and teaching environment. There are four grades of excellent, good, medium, and poor. Association rule analysis result 2 is shown in Table Minimum support: 0.3 Number of iterations: 7 The generated large item set: Size of the big data item The size of the big data item The size of the big data item
Association rule analysis result 2.
Association rules | Confidence | Influence degree |
---|---|---|
Teacher quality (excellent), teaching process (excellent) ⟶ excellent | 0.78 | 9.87 |
Teacher quality (excellent) ⟶ excellent | 0.63 | 6.32 |
Instructional design (excellent) ⟶ teaching courseware (excellent) | 0.57 | 5.98 |
Teacher quality (excellent) ⟶ teaching process (excellent) | 0.42 | 3.16 |
If we set the minimum confidence level to 70% and the minimum impact degree to 4, we can get the association rules “teacher quality (excellent), teaching process (excellent)
The exhaustive and comprehensive experiments are carried out. The dataset of the year 2005 to 2007 of the local university is considered. Experiments were conducted on the validity of the impact of the multimedia teaching evaluation. It is verified whether the use of influence can reduce the generation of redundant rules. The experiment was carried out in Lenovo computer 8g memory, 3G frequency, Win XP, MATLAB environment. The degree of influence is used to filter the association rules generated under the traditional support and confidence framework. When the minimum confidence is set to 0.5, the total number of association rules generated before and after filtering will be tested with the support threshold. The support degree verification diagram is shown in Figure
Support degree verification diagram.
Scatter plot of support degree and confidence association rules.
The scatter diagram of the confidence association rules.
After filtering from the above figure, it can be seen that the number of effective association rules measured by the degree of influence to the association rules generated under the support and confidence framework has been significantly reduced. This shows that the use of influence can effectively negate irrelevant rules and filter out redundant rules. In order to reflect the superiority of the model, the more popular model based on traditional correlation and effectiveness is selected. In order to increase the contrast, the same set of experimental data is selected. Comparison of traditional relevance validity models is shown in Table
Comparison of traditional relevance validity models.
Influence degree | Detected influence | Correct number | False detection | Precision rate |
---|---|---|---|---|
8.23 | 8.29 | 8.09 | 0.2 | 0.982989064 |
8.28 | 8.34 | 8.14 | 0.2 | 0.983091787 |
8.33 | 8.39 | 8.19 | 0.2 | 0.983193277 |
8.38 | 8.44 | 8.24 | 0.2 | 0.983293556 |
8.43 | 8.49 | 8.29 | 0.2 | 0.983392645 |
8.48 | 8.54 | 8.34 | 0.2 | 0.983490566 |
8.53 | 8.59 | 8.52 | 0.07 | 0.998827667 |
8.58 | 8.88 | 8.81 | 0.07 | 1.026806527 |
8.63 | 8.93 | 8.86 | 0.07 | 1.026651217 |
8.68 | 8.98 | 8.91 | 0.07 | 1.026497696 |
8.98 | 9.28 | 9.21 | 0.07 | 1.025612472 |
9.28 | 8.78 | 8.71 | 0.07 | 0.938577586 |
9.58 | 9.08 | 9 | 0.08 | 0.939457203 |
9.88 | 9.38 | 9.3 | 0.08 | 0.941295547 |
9.8 | 9.3 | 9.22 | 0.08 | 0.940816327 |
9.72 | 9.22 | 9.14 | 0.08 | 0.940329218 |
9.64 | 9.14 | 9.06 | 0.08 | 0.939834025 |
9.56 | 9.06 | 8.98 | 0.08 | 0.939330544 |
9.48 | 8.98 | 8.9 | 0.08 | 0.938818565 |
The experimental results show that the use of influence can not only obtain effective association rules but also divide the obtained rules into strong association rules and weak association rules and overcome some deficiencies of relevance and validity, making the rules more objective, reasonable, and convenient for users to choose. The verification results show that it is feasible and valuable to find the potential relevance.
This paper studies and analyzes the traditional support and confidence framework based on edge-enabled data analysis. The metrics such as relevance and validity indicate the shortcomings. On this basis, the
The data used to support the findings of this study are available from the corresponding author upon request.
The authors declare that they have no conflicts of interest.
This study was supported by 2019 Key Projects of Art and Science Planning in Heilongjiang (2019A003).