Innovative design of an art teaching quality evaluation system based on big data and an association rule algorithm from the perspective of sustainable development

doi:10.21203/rs.3.rs-2474249/v1

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Innovative design of an art teaching quality evaluation system based on big data and an association rule algorithm from the perspective of sustainable development

https://doi.org/10.21203/rs.3.rs-2474249/v1

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published 27 Mar, 2023

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Online art courses still suffer from poor course production, limited sustainable use and development, and a significant degree of course similarity today. It is really important to figure out how to create an efficient system for evaluating art instruction. The current institutions of higher art education are how to apply data mining approaches to the teaching quality evaluation system is studied in this article. Data mining effectively dig out the indicators of the institutions of higher art education teaching quality evaluation system using association rule algorithm to solve the current teaching quality evaluation of rationality and subjectivity, and a teaching quality evaluation algorithm based on association were some of the solutions used. The widely used development tools, SQL Server and ASP.NET were used to build and implement the teaching quality assessment system through a variety of indications of teaching quality evaluation in a school. The administrators of higher art education institutions will receive analytical materials for making decisions from this study that are based on science and objectivity.

Data mining

association rule algorithm

teaching quality evaluation system

innovative design

sustainable development

Online art teaching has become an increasingly popular way of teaching. However, online art courses still have the problems of imperfect course production, hindered sustainable use and development, and high course similarity on, hindered sustainable use and development, and high course similarity. How to construct an effective art teaching evaluation system to promote the sustainable use and development of online art courses is of great significance.

For teaching quality evaluation methods, many scholars have made an important discussion; in the context of the current study, our country's colleges and universities and research institutions for the teaching management system using data mining technology are very few; therefore, the combination of data mining technology with the teaching quality evaluation system of in-depth information digging and obtaining objective and fair evaluation results has become the focus of research ^[1]. Represented by Zhejiang University, the management of talent information of university teachers adopts the method of data mining to explore the favorable factors related to the construction of teacher team in teaching management. The teaching management department of Xiamen University has carried out a study on students' teaching methods by using linear regression equations and association rules to study the internal relationship between students' academic performance and teachers' teaching methods^[2].Nowadays, the informationization of teaching evaluation in higher vocational and technical education has been vigorously promoted in China, and online teaching evaluation by students or experts has become an important direction of research^[3]. Some domestic scholars have made some research achievements in this field: Wang et al from Shandong University introduced data mining in the process of teaching evaluation. They obtained relevant data of teaching management such as students' grades, course arrangements and teachers' information from the educational administration management system of the college to form a data warehouse. Then, they established a data mining model using an algorithm based on association rules. Through this model, it can be found that there is a certain correlation between the degree of education of teachers and the course performance of students, so as to guide the college to strengthen the construction of teaching staff and introduce higher level talents ^[4–6]. Gao^[7]introduces the research and implementation methods of constructing the quantitative evaluation index system of university teachers' teaching quality and its quantitative processing method as well as the intelligent quantitative evaluation system of teaching quality and explores the technical ways to discover the effectiveness of measurable factors on the evaluation of teaching quality, and the obtained results have important practical significance for the scientific evaluation of teaching quality. Jang Lan et al^[8]developed an internal teaching quality evaluation system based on B/S architecture, and used frequent itemset algorithm Apriori of association rules and classification decision tree algorithm C4.5 of machine learning algorithm in the teaching quality evaluation of the system.Ma W and Yang Z^[9]construct a system model based on quantum particle swarm optimization algorithm to evaluate teaching quality. The system includes management, evaluation and other functions, which can allow each college to successfully complete the teaching quality assessment.

With the development and maturity of data mining technology, it has become very common to apply it to the teaching quality evaluation of disciplines. More and more colleges and universities associate the teaching quality evaluation with the teacher talent database of colleges and universities^[10]. This research on teaching quality through big data can not only combine the theoretical results already obtained, but also build a teaching quality evaluation system suitable for design art colleges from the actual situation of art colleges, so as to obtain the factors affecting the teaching quality of art colleges more objectively and scientifically, and provide a basis for teaching reform.

This paper proposes an art teaching quality evaluation system based on association rules algorithm. The art teaching quality evaluation system is a data mining system based on SQL Server 2016 database. Through data mining, the indicators of the teaching quality evaluation system of higher art education institutions are dug out effectively. Besides, the association rule algorithm is applied to solve the problems of rationality and subjectivity of current teaching quality evaluation. By obtaining the results of the mining analysis and statistics, the effectiveness of the proposed model is verified.

2.1 Association Rule Mining

Data mining is a hot spot in the research field of data mining technology and artificial intelligence technology, the so-called data mining is to reveal the previously unknown and potentially valuable hidden information from the data. Data mining is the most cutting-edge discipline that spans a variety of technologies, from simple query of data to the application of data analysis and statistics, from which useful information can be extracted to help planners change market strategies, reduce losses and make wise judgments.

Data mining tasks can generally be divided into: description and prediction^[11].Descriptive data mining: express data based on a meaningful and concise overview of the nature of existence. Predictive data mining: analyze the model or a set of data obtained by providing settings to apply special data, and the model is new data used to predict the nature of the future.

Association rule mining is one of the most active data mining methods^[12]. Association rule mining is the process of discovering frequently associated data items from a database. The role of association rule mining is to discover the relationship between items from the data set so as to guide decision-making. The mining process of association rules mainly includes two stages: the first stage must first be from the high-frequency item sets in the data, and the second stage must generate association rules from a group of high-frequency items.The first stage of association rule mining must be set from the original data^[13], discover all frequent itemsets. The second stage of association rule mining is to generate association rules. The threshold conditions under the minimum confidence are set to use the high-frequency k-item generation rules in the previous step. If the reliability of the rules meets the minimum confidence expression, they are called association rules. Then set the frequency set found in the first step to generate ideal rules, and the result can only contain the set of all rule items, with only one right part of each rule. Once a defined rule is generated, users with a minimum confidence greater than the specified unique rule will be excluded.

2.2 Related algorithms of association rule mining

Apriori algorithm is an algorithm for mining frequent itemsets based on Boolean association rules. Frequent item sets refer to the existence of some identical elements in a series of sets, and the elements that appear frequently in the sets at the same time form a subset, which satisfies a certain threshold condition. The core of Apriori algorithm is the idea of realizing frequency sets based on two-level recursive algorithm. Association rules are the close association rules between item sets given by frequent item sets. The association rules in classification are single and one-dimensional Boolean association rules. When the support exceeds the user-defined threshold, the item set is called frequent item set, or frequency set for short.

The basic idea of this algorithm is to find out all the frequent sets, which are at least the same as the predefined minimum support count. Strong association rules are generated in frequent itemsets, and these rules must meet the minimum support and minimum confidence. Then set the frequency set found in the first step to generate ideal rules, and the result can only contain the set of all rule items, with only one right part of each rule. Once a defined rule is generated, users with a minimum confidence greater than the specified unique rule will be excluded.

The disadvantage of Apriori algorithm is that there are too many combinations of candidate item sets, and unnecessary parameter combinations must be removed. In the support calculation, the computer will scan all the data. Assuming that it is a huge database, the Apriori algorithm will increase the load of the computer system and increase the load in equal proportion. The flowchart of Apriori algorithm for association rules is shown in Fig. 1.

2.3 Microsoft SQL Server 2016 Overview

Microsoft SQL Server 2016 is an embedded database programming software, which simplifies the management and maintenance of the database. SQL can fully support web-based XML and Internet standard database programming, and send requests to access Web data and database applications through HTTP.SQL scalability and security can meet the requirements of large enterprises (such as Alibaba's global e-commerce enterprises and Foxconn's large enterprises).

SQL Server is a relational database management system, which is used to store and retrieve data. It can perform queries, store and retrieve data, update data in the database, and write or retrieve data from the database through SQL statements. The program development interface of Microsoft SQL server is friendly and the graphical interface is the same as that of Microsoft Windows, which is convenient for beginners.

In addition, as an application of the background database ^[14], Microsoft SQL Server 2016 must establish a connection with the foreground, and issue commands in the foreground to call the data in the background database or edit the data in the database. Microsoft SQL is an application for data storage, not for query purposes. To query, you must use SQL query statements to find the data in the database.

1. The Client / Server Mode Is Implemented

Microsoft SQL Server 2016 implements the client/Server mode. Client / server (C / s) computing mode is a distributed data storage, access and processing technology. SQL server is a typical example of client / server system application. This mode uses the server to store data centrally, which is convenient for maintenance and management. The client is connected to the running SQL server server through the network. The processing process of the database application is distributed on the client and the server, and the client completes the data representation and most of the business logic^[15]. With the development of network technology and the continuous improvement of database transaction processing requirements, the current client / server model has developed from two-tier structure to multi-tier structure. The SQL database architecture is shown in Fig. 2.

2. Support Internet Integrated

SQL can fully support web-based XML and Internet standard database program-

ming, and send requests to access Web data and database applications through HTTP.SQL can meet the needs of large enterprises. SQL is an open database connection. It must be ensured that the system has started the TCP / IP network protocol, otherwise it cannot be implemented internet resource sharing data. In order to improve the security of data, we must encrypt the data during the development process.

3.1 User requirements

According to the characteristics of each index of the evaluation system of an art college, a set of information management system for real-time evaluation, inquiry and management of teachers' teaching quality is developed through the interactive mode of web browsing. In view of the characteristics of the teaching quality evaluation system, such as the collection of teachers and curriculum information, the entry of evaluation content, the filling of the teaching quality evaluation form, the collection, statistics and archiving of evaluation results, and the background management of the evaluation system, the demand analysis of the system is designed based on the investigation and research of the actual teaching evaluation activities. The teaching quality evaluation system involves many objects in teaching management and teaching operation, the teaching quality evaluation activity system consists of experts, teachers, students and teaching managers, as shown in Fig. 3.

3.2 Functional requirement

The system is required to achieve the following functions:

The system is required to enable supervisors to use the Internet to view the detailed information of teachers being evaluated;

The system is required to be able to use intranet and extranet, and students of each class can score and give suggestions to teachers of the class through the Internet;

The system is required to enable teachers to view the evaluation results of the teachers through the Internet;

The system administrator is required to be able to manage and maintain the data information of the system through the intranet and extranet;

The system is required to enable the supervisor to make statistics and feedback on the results of the evaluated teachers through the intranet and the Internet.

System requirement analysis is to understand the customer's requirements for the system, and then analyze the system requirements in detail. The results required by the customer shall be analyzed according to the data entered, and the necessary and non necessary system requirements of the customer shall also be considered. Necessary system requirements: the system functions used by customers must be met. It is not necessary for the system to meet the customer's pre development and expansion of the functions used by the system in the future. Various models are often used to describe software system requirements analysis. A use case is a description of the action sequence of the interaction between the system, subsystem or class and external participants, including optional action sequences and action sequences with exceptions^[16].Use case modeling is a common method of system functional requirements analysis in object-oriented requirements analysis (OOA) modeling. TThe use cases involved in these activities include user management, user login, evaluation management, and viewing evaluation results.

3.3 Database requirement analysis

Database requirement analysis is a data classification design based on the user's realization of system functions and what data needs to be obtained. The number of tables required by the database, the number of keywords in the table and the relationship between tables must be analyzed and designed in detail. It lays a solid foundation for the function realization of the system. The summary is as follows:

The system divides user permissions into teachers, students and administrators;
The number of courses a teacher teaches per semester is arbitrary;
Students in each class can only evaluate the teachers in the class;
Different classes of the same course can be taught by different teachers, such as the public basic course and computer application foundation of the whole college;
Teachers can query their own teaching evaluation, and master the needs, opinions and suggestions of students, so as to better improve their teaching methods and means.

4.1 System architecture design model

B/S (Browser/Server) mode is a network structure mode after the rise of Web. This mode unifies the client and concentrates the core part of the system function realization on the server, which simplifies the development, maintenance and use of the system. B/S mode is also called B / S structure. The B/S structure is to split the computer application program into several subtasks. One of the subtasks is realized by the front-end computer (i.e. the client), but the main subtasks are realized by the back-end computer (i.e. the server). The data exchange is realized through Internet Explorer and webserver.

The system adopts a three-layer architecture model based on B/S mode, and the main functions of the system are all processed in the middle layer. Run the client browser in the web model. When the user sends a request and connects to the web server (middle layer) with application extension function on the network through the browser, it will be converted into information that can be recognized by the database server (bottom layer). Then, the database access and application program are completed on the back-end computer (i.e. server) through the data gateway and database exchange. The B/S three-tier architecture shown in Fig. 4 includes:

(1)The presentation layer is the web browser. The task of the client where the presentation layer is located is to send a service request and send it to the web server on the network through the web browser. The web server uses the HTTP protocol to transmit the required information and authenticate the user of the client. The received home page file is displayed in the web browser of the client.

(2) The business logic layer is used to verify the operation of the application and better ensure the stability of the application. In the analysis stage where the application is analyzed and summarized, it does not depend on the existence of the application. On the contrary, the pre-existing application will limit your application behavior. An application program that performs data processing by an SQL database, then waits for the relevant data processing results, submits them to the database on the web server, and finally sends the results to the client.

(3) The data service layer is the database server. This layer mainly provides data services for businesslogic and presentation. Its task is to request the data source service from the web server, maintain and modify the data structure and access mechanism, and even reconstruct the intermediate layer that does not affect the client layer and application components when necessary. The data service layer does not operate on the database.

4.2 System function module design

After careful analysis of the teaching evaluation system of an art college, combined with the actual needs of the college, the system is divided into the following four modules according to different content management mechanisms and systems, as shown in Fig. 5. Through program design and development, the system can realize digital paperless intelligent network evaluation system.

The establishment of these modules is mainly used for the evaluation system of teaching quality, and the most important is to establish the index system and the allocation of each weight in the index system. The summary is as follows:

The index system design module is the main body of student evaluation according to the indicators constructed by the index system;
The student management module evaluates the teaching quality of the teachers according to the class;
The teacher module is mainly used for information query, including the query of the evaluation results after the students make the evaluation and the query of the early warning information when the teacher's evaluation is low;
The administrator module includes basic information management of students, teachers and supervisors, evaluation processing of supervisors and data mining of association rules;
The basic information management module is used to input, maintain and manage basic information;
The evaluation processing module is used for solving the evaluation result;
The data mining module mainly aims at the data mining evaluation results of association rules combined with various kinds of databases of the paperless and information-based teaching quality evaluation system, and uses the data mining of association rules to extract the useful data hidden in the information.

4.3 Data flow diagram of teaching quality evaluation system

The teaching quality evaluation system mainly includes the module construction of the evaluation index system, the student evaluation module, the peer mutual evaluation module and the data mining module of association rules. The data flow diagram of the teaching evaluation system and its subsystems is shown in Fig. 6.

Table 1

explanation for different data type
Data type	Description	Range
Char	for text or the combination of text and numbers	0 ~ 255 characters
VarChar	save variable-length strings	0 ~ 255 characters
smalldatetime	for date and time	1900-01-01 ~ 2079-06-06
Int	for integer	-2147483648 ~ 2147483647
Text	save long strings	0 ~ 65535 characters

4.4 Design and implementation of database physical structure

An art college teaching evaluation management system according to the above demand analysis, to establish a large number. According to the data table in the database, the following is the database used in part of the data table and structure design while the explanation for each data type is presented in Table 1.

1. User type table: when the application logs in, the application will judge whether the user is legal according to the user's permission and user name, as shown in Table 2.

Table 2

S_ user (user table)
Column _name	description	data type
Suser	user name	Char(8)
Pw	password	VarChar(50)
flag	Permission (s, t, a)	Char(1)

2. Teacher information table: it stores the basic information of college teachers and provides the basis for data analysis in the system, as shown in Table 3.

Table 3

JS(teacher information form)
Column _name	description	data type
Tnumr	Teacher No	Char(6)
Tname	teacher name	Char(10)
Tsex	sex	Char(2)
Birthday	birthdate	smalldatetime
Edu	Education	Char(6)
Title	title	Char(10)
Dept	Department	Char(20)
ZZ	Full time	Char(1)
JZ	Teacher qualification certificate	Char(1)

3. Student information table: it stores the number of college students and the corresponding class, as shown in Table 4.

Table 4

Student information
Column _name	description	data type
Snum	Student ID	Char(8)
Cnum	Class number	Char(9)

4. Class Information Table: It Stores The Number And Name Of Each Class In The college and the information of the Department, as shown in Table 5.

Table 5

Class (class information table)
Column _name	description	data type
Cnum	Class ID	Char(9)
Class	Class name	Char(20)
Dept	Department	Char(20)

5. Open course table: it stores the course code of each class in each semester of the college, as shown in Table 6.

Table 6

Course(course schedule)
Column _name	description	data type
Cnum	Class number	Char(9)
Semester	semester	VarChar(50)
Course1	Course Code1	Char(6)
Course2	Course Code2	Char(6)
Course3	Course Code3	Char(6)
Course4	Course Code4	Char(6)
Course5	Course Code5	Char(6)
Course6	Course Code6	Char(6)
Course7	Course Code7	Char(6)

6. Course opening & teaching teacher table: it stores the information of the courses offered by each class in each semester and the teachers undertaking the course, as shown in Table 7.

Table 7

C-T(list of courses and teachers)
Column _name	description	data type
Cnum	Class number	Char(9)
Semester	semester	Char(1)
Course	Course number	Char(6)
Tnum	Teacher No	Char(6)

7. Scoring table: it stores all the information of the evaluation table filled by the student information clerk, as shown in Table 8.

Table 8

Score(scoring table)
Column _name	description	data type
Cnum	Class number	Char(9)
Semester	semester	Char(1)
week	Week number	Char(2)
Course	Course number	Char(6)
Tnum	Teacher No	Char(6)
D1	Score 1	Int(4)
D2	Score 2	Int(4)
D3	Score 3	Int(4)
D4	Score 4	Int(4)
D5	Score 5	Int(4)
D6	Score 6	Int(4)
D7	Score 7	Int(4)
D8	Score 8	Int(4)
D9	Score 9	Int(4)
D10	Score 10	Int(4)
D11	Score 11	Int(4)
D12	Score 12	Int(4)
D13	Score 13	Int(4)
D14	Score 14	Int(4)
D15	Score 15	Int(4)
D16	Score 16	Int(4)
Ag	average	Int(4)
JY	proposal	Text

4.5 System implementation

The evaluation of teaching quality is the reflection of the evaluation of teachers' teaching effect and students' learning effect. It is reflected in the teacher's teaching process, knowledge points and classroom discipline management, and students' mastery of knowledge and skills. Therefore, this topic studies the relationship between teachers and students, that is, the evaluation of teaching and learning. Teaching quality evaluation is mainly to refine and quantify the evaluation indicators. The evaluation is based on the professional level of teachers (routine teaching work, classroom organization and management, extra-curricular student guidance, etc.), and the evaluation is based on the total scores of students (ordinary scores, homework (practical training) scores, ordinary test scores and final examination scores, etc.). The above is the core of teaching quality evaluation, and the evaluation process shown in Fig. 7 is constructed.

The following describes the user login interface of the Art Teaching Quality Assessment System, shown in Fig. 8 as a flowchart of the user login display.

In this paper, according to the index requirements of the teaching quality evaluation system of vocational education and the actual development requirements of teachers, a set of teaching quality evaluation rules based on an association rules algorithm is designed. The system is to promote the development of teachers and administrative departments to grasp the current situation of school development as the goal, the complete realization of online data collection, extraction, preprocessing, and statistical processes, and the realization of the whole process of association rules data mining analysis and statistics. Then, it deeply studies the improvement of data mining technology and algorithms, pays attention to the association rule Apriori algorithm and its improved algorithm, and uses a data mining module to evaluate the success of the teaching implementation of the association rule mining algorithm. The teaching quality evaluation system at Art College is a data mining system based on a SQL Server 2016 database. Through data classification and archiving, the teacher evaluation is associated with the data in the data mining of association rules, using the influence of key factors in the mining algorithm, the results of the mining analysis, and statistics to verify the effectiveness of the data mining of evaluation.

This paper analyses the association rule data mining of the teaching quality evaluation system, which is used as a data warehouse in teaching quality evaluation. This paper analyses the teaching quality evaluation system and obtains the evaluation result score of association rules that can affect the key factors of the system. The result not only produces the evaluation score but also allows you to view the feedback information. This requires results from internal and external factors such as class, school status, teachers, students, and majors. This study can not only combine the theoretical results already obtained but also obtain the factors affecting the teaching quality of art colleges more objectively and scientifically, which will provide guidance and a basis for teaching reform.

Compliance with Ethical Standards:
Funding: This paper was not funded by any project or organizations.
Conflict of Interest: The author declare that there are no conflicts of interest.

Ethical approval : All applicable international, national, and/or institutional guidelines for the care and use of animals were followed.
Ethical approval: All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.
Ethical approval: This article does not contain any studies with human participants performed by any of the authors.
Informed consent: Informed consent was obtained from all individual participants included in the study.

Author Contributions: Gang Zhou was responsible for the writing of this paper, data collection. Rui Zhan was responsible for the data preprocessing.

Data Availability: The dataset can be accessed upon request.

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You are reading this latest preprint version

Innovative design of an art teaching quality evaluation system based on big data and an association rule algorithm from the perspective of sustainable development

Status:

Journal Publication

Version 1

Abstract

Figures

1 Introduction

2 Big Data Mining Technology

2.1 Association Rule Mining

2.2 Related algorithms of association rule mining

2.3 Microsoft SQL Server 2016 Overview

1. The Client / Server Mode Is Implemented

2. Support Internet Integrated

3 System Requirement Analysis

3.1 User requirements

3.2 Functional requirement

3.3 Database requirement analysis

4 System Architecture Design And Implementation

4.1 System architecture design model

4.2 System function module design

4.3 Data flow diagram of teaching quality evaluation system

4.4 Design and implementation of database physical structure

4.5 System implementation

5 Conclusions And Recommendation

Declarations

References

Status:

Journal Publication

Version 1