# Analysis of University Teaching Evaluation in the Era of BD

Authors: Jing Tian
Journal: Mobile Information Systems
 (2022)

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## Abstract

College teaching evaluation is an important part of teaching because it not only summarizes the semester's teaching results but also guides the next semester's teaching. However, with the implementation of China's quality-oriented education reform and the arrival of the era of big data (BD), new requirements for college teaching evaluation are being put forward, which also brings many reform conditions. Teaching evaluation in the traditional sense is typically based solely on the course's student scoring data. This method is based on a single data source and thus cannot evaluate the teaching effect comprehensively, objectively or fairly. To address this issue, a teaching evaluation method in colleges and universities operating within the BD environment is proposed. This paper investigates college and university teaching evaluation in the post-BD era. The observed value of the Bartlett sphericity test statistic is 6619.943, and the corresponding probabilityp is nearly equal to 0. If the significance level an is 0.05, the null hypothesis should be rejected because the probability p is less than the significance level a, indicating that the correlation coefficient matrix cannot be the unit matrix. This also implies that there must be some correlation between the original variables, making factor analysis possible. BD can combine scattered and single evaluation data to create continuous and systematic data. BD can provide a large amount of data support for teaching evaluation, making it more scientific and fair; at the same time, driven by BD, teaching evaluation can return the results of teaching evaluation more quickly.


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## Body

## 1. Introduction

With the rapid development of information technology and the increasing investment of university in information technology, BD has entered major college campuses. How to truly and effectively use the BD generated in school teaching and management has emerged as a critical challenge in the development of a smart campus [1, 2]. Various data generated by the teaching process, for example, can be effectively used to solve problems in teaching evaluation. It is not difficult to discover, through practical teaching, that the teaching evaluation link is an important yardstick for reflecting the teaching effect. We must change our way of thinking and understanding and optimize and improve the system of teaching evaluation and supervision. The most important link in the teaching process should be considered to be teaching evaluation. Only scientific and objective teaching evaluation can better promote students’ personalized development, comprehensively deepen the reform system of physical education, and improve college students’ core literacy and ability [3, 4]. College teaching evaluation is an important aspect of the teaching process. It not only summarizes the semester's teaching results, but it also guides the next semester’s teaching [5]. However, with the implementation of China's quality education reform and the advent of the BD era, new requirements for college teaching evaluation are put forward, and many reform conditions are introduced. Building a new teaching evaluation system based on the emergence of BD background is extremely important in promoting the development of teaching work [6, 7]. In traditional teaching evaluation, we typically rely solely on student scoring data for teaching evaluation. This method is based on a single data source and cannot assess the teaching effect comprehensively, objectively, or fairly. In response to this issue, this paper proposes a teaching evaluation method in a university setting within the context of BD.The advent of the BD era has had a significant impact on politics, social economy, education, and other fields. The Chinese government places a high value on BD research and application in the field of education and has launched a research surge aimed at promoting educational reform and innovation using BD technology [8–10]. The amount of data in people's lives and studies is growing by the day in the BD era. Using BD technology, you can solve the problem of accumulating a large amount of data, such as text data, image data [11–13], learning process data, and so on. The “big” of BD comes from its potential “great value” for comprehensive data analysis, not its apparent “large capacity.” In addition to descriptive statistical reports on data, the key to reflecting data value lies in data's diverse application. To begin with, data can be used to provide an early warning about a student's learning status. Information subjects can communicate anytime and anywhere via various wired and wireless networks, which inevitably leads to the exchange of information, and the key to the effectiveness of data and information lies in improving speed, while BD technology can process data efficiently and quickly [14, 15].BD can provide a lot of data support for teaching evaluation and make it more scientific and fair; at the same time, driven by BD, teaching evaluation can feed back the results of teaching evaluation more quickly. Efficient teaching evaluation is primarily one of the standards that reflect the teaching process and its impact. It can accurately reflect flaws in the teaching process and is a valuable tool for teachers seeking to improve their teaching methods and teaching quality. Simultaneously, it can detect and improve students' learning efficiency by detecting problems that are likely to arise during the learning process. When BD technology is used in education and teaching, educators can obtain all aspects of learners' information more quickly, effectively, comprehensively, and truly and provide real-time feedback, allowing them to provide decision-making, a scheme, and a foundation for educational implementation. BD can combine scattered and single evaluation data to create continuous and systematic data. Using BD technology, the university teaching quality evaluation system can cover all stages and aspects of students’ learning and growth and realize the dual function of “accountability and improvement,” accurately “portrait” the teaching quality of the university, and comprehensively improve the teaching quality of the university. This paper studies and innovates the above problems from the following aspects:(1)
A teaching evaluation model of university in the era of BD is proposed. Data-based facilities, popular mobile learning, digital teaching behavior, visual learning behavior, etc. all become the data sources of teaching quality evaluation in university. The collection method of this model can avoid the temporality of university' response to external quality accountability, and at the same time, it can also avoid the blindness of teachers in improving students' course learning, so as to realize the evaluation function of accountability and improvement.(2)
The university teaching evaluation system based on BD is constructed. In the teaching evaluation system under the background of BD application, we can apply BD related technology to process the data collected in the process of teaching evaluation in university. By applying BD related technology to teaching evaluation, we can integrate and analyze the collected data, dig deeply, and obtain a lot of information, which makes the evaluation result more scientific and objective and have certain application value.


## 2. Related Work

### 2.1. Research Status at Home and Abroad

Yuan proposed that with teachers, as the main body of talent training, the quality of their teaching directly affects the quality of students' learning. The evaluation of teaching quality is very important for the optimal management of teaching quality [16]. Yuanmeng proposed that, using the network server, a large amount of personal information can be stored for a long time and dynamically, and all the stored information is conducive to a series of work such as retrieval, search, addition, deletion, calculation, and statistics, which can provide timely and accurate reference information for teaching work and teacher team construction [17]. Liu et al. put forward that the evaluation of teaching quality is to reasonably judge the influencing factors and teaching effect of teaching with the help of effective technical means, mobilize teachers' teaching enthusiasm, timely improve and deal with the problems existing in teaching activities, further improve teachers' teaching quality, and realize a more perfect talent training mechanism [18]. Zhang proposed that most evaluation tools use induction method to extract some teacher characteristics and classroom behaviors that are considered to have an impact on teaching effect from the existing teacher effectiveness research and list them together. The researchers do not know which factors are more important, but the evaluation is often aimed at obtaining the synthetic score [19]. Nivash and Babu proposed that the calculation and analysis process of evaluation should be completed by the computer itself. In this way, not only is the efficiency of teachers’ teaching evaluation improved, and the teaching evaluation of the whole school teachers can be completed in a short time, but also the whole process is paperless, and the evaluation process is completed by computer. Therefore, it is more open and fair, and the teaching evaluation is more objective, which can improve the level of teaching management, promote the improvement of teachers' self-ability, ensure the teaching quality, and further improve the school teaching quality [20]. Huafeng L pointed out that the objective, fair, and reasonable evaluation of teaching quality is an important task in talent cultivation at present. It is an important means to find teaching problems, clarify teaching direction, improve teaching process, and improve teaching effect [21]. Fu et al. put forward that the quantitative data obtained by the network platform is more accurate and timely, which creates conditions for automatic evaluation and feedback of the system, simplifies the work of evaluators, shortens the evaluation period, and ensures the effectiveness and quality of information by participating in evaluation activities at the most suitable time [22]. Pang et al. put forward the evaluation of teaching quality as an evaluation of teachers’ teaching activities, which appeared with the emergence of schools. However, the teaching quality evaluation activity has shown its due value in school education management, which really started in the early 20th century [23]. Araújo et al. proposed that, through systematic interaction, the direct contact between the evaluation subject and the evaluation object should be reduced, so as to avoid the subjective impression of the evaluation subject on the evaluation object and ensure that every evaluation object is treated equally [24]. Fu et al. pointed out that, internationally, the United States and the United Kingdom were the first countries to launch teaching quality evaluation activities, and they had a deep understanding of teaching quality evaluation activities and summarized rich evaluation experience. They not only made great progress in theoretical research, but also achieved fruitful results and accumulated valuable experience in practical verification [25].


### 2.2. Research Status of Teaching Evaluation in University in the Era of BD

To sum up, the evaluation and management of teaching quality at home and abroad have a long history, and a relatively perfect standard and evaluation index system has been formed. In order to evaluate teachers' teaching quality comprehensively and objectively, this paper analyzes the teaching evaluation of university in the era of BD. The application of BD makes the school have assessment standards for every aspect of students and teachers, not just for one aspect of students or teachers. We should also comprehensively score and assess the learning attitude and the degree of progress. The application of BD technology has the effect of low density and high value. Some scattered and different types of data use BD analysis technology to mine the potential value of information, so as to facilitate learning and research and realize the convenience and depth of data mining. Teachers have the most say in students’ learning, so teachers’ evaluation is the main part when evaluating students' teaching. In this study, teachers can log in to the teacher system to evaluate the teaching of students in the substitute class. In the context of BD application, rebuild the teaching evaluation system of university, organically combine the online and offline classes of teaching, more systematically integrate the various processes of teaching activities, organize the teaching content targeted, and improve the interest and quality of students’ participation in learning.


## 2.1. Research Status at Home and Abroad

Yuan proposed that with teachers, as the main body of talent training, the quality of their teaching directly affects the quality of students' learning. The evaluation of teaching quality is very important for the optimal management of teaching quality [16]. Yuanmeng proposed that, using the network server, a large amount of personal information can be stored for a long time and dynamically, and all the stored information is conducive to a series of work such as retrieval, search, addition, deletion, calculation, and statistics, which can provide timely and accurate reference information for teaching work and teacher team construction [17]. Liu et al. put forward that the evaluation of teaching quality is to reasonably judge the influencing factors and teaching effect of teaching with the help of effective technical means, mobilize teachers' teaching enthusiasm, timely improve and deal with the problems existing in teaching activities, further improve teachers' teaching quality, and realize a more perfect talent training mechanism [18]. Zhang proposed that most evaluation tools use induction method to extract some teacher characteristics and classroom behaviors that are considered to have an impact on teaching effect from the existing teacher effectiveness research and list them together. The researchers do not know which factors are more important, but the evaluation is often aimed at obtaining the synthetic score [19]. Nivash and Babu proposed that the calculation and analysis process of evaluation should be completed by the computer itself. In this way, not only is the efficiency of teachers’ teaching evaluation improved, and the teaching evaluation of the whole school teachers can be completed in a short time, but also the whole process is paperless, and the evaluation process is completed by computer. Therefore, it is more open and fair, and the teaching evaluation is more objective, which can improve the level of teaching management, promote the improvement of teachers' self-ability, ensure the teaching quality, and further improve the school teaching quality [20]. Huafeng L pointed out that the objective, fair, and reasonable evaluation of teaching quality is an important task in talent cultivation at present. It is an important means to find teaching problems, clarify teaching direction, improve teaching process, and improve teaching effect [21]. Fu et al. put forward that the quantitative data obtained by the network platform is more accurate and timely, which creates conditions for automatic evaluation and feedback of the system, simplifies the work of evaluators, shortens the evaluation period, and ensures the effectiveness and quality of information by participating in evaluation activities at the most suitable time [22]. Pang et al. put forward the evaluation of teaching quality as an evaluation of teachers’ teaching activities, which appeared with the emergence of schools. However, the teaching quality evaluation activity has shown its due value in school education management, which really started in the early 20th century [23]. Araújo et al. proposed that, through systematic interaction, the direct contact between the evaluation subject and the evaluation object should be reduced, so as to avoid the subjective impression of the evaluation subject on the evaluation object and ensure that every evaluation object is treated equally [24]. Fu et al. pointed out that, internationally, the United States and the United Kingdom were the first countries to launch teaching quality evaluation activities, and they had a deep understanding of teaching quality evaluation activities and summarized rich evaluation experience. They not only made great progress in theoretical research, but also achieved fruitful results and accumulated valuable experience in practical verification [25].


## 2.2. Research Status of Teaching Evaluation in University in the Era of BD

To sum up, the evaluation and management of teaching quality at home and abroad have a long history, and a relatively perfect standard and evaluation index system has been formed. In order to evaluate teachers' teaching quality comprehensively and objectively, this paper analyzes the teaching evaluation of university in the era of BD. The application of BD makes the school have assessment standards for every aspect of students and teachers, not just for one aspect of students or teachers. We should also comprehensively score and assess the learning attitude and the degree of progress. The application of BD technology has the effect of low density and high value. Some scattered and different types of data use BD analysis technology to mine the potential value of information, so as to facilitate learning and research and realize the convenience and depth of data mining. Teachers have the most say in students’ learning, so teachers’ evaluation is the main part when evaluating students' teaching. In this study, teachers can log in to the teacher system to evaluate the teaching of students in the substitute class. In the context of BD application, rebuild the teaching evaluation system of university, organically combine the online and offline classes of teaching, more systematically integrate the various processes of teaching activities, organize the teaching content targeted, and improve the interest and quality of students’ participation in learning.


## 3. University Teaching Evaluation Model Based on BD

BD technology is a new technology that has yet to establish a technical framework with practical utility. Following the format of the IOS network’s seven-layer protocol, the specific technologies that are currently primarily used are mapped to the corresponding level, which is useful for deepening understanding and memory. The foundation and necessary condition of teaching evaluation is the establishment of a scientific and effective evaluation system. The evaluation system for university teachers falls under the purview of soft science. The fundamental requirement of soft science is that scientific research introduces management and decision-making processes and makes management and decision-making a process with scientific basis and institutional guarantee by using modern scientific and technological means, in order to achieve efficient, scientific, and modern management and decision-making. In some ways, the university’s teacher evaluation system serves as a model for further improving evaluation objectives and serves as a passing of the baton in teaching. The university teaching quality evaluation system based on BD will evaluate the entire process and record and analyze data related to students’ learning. Students’ learning is an extremely complicated process, and a school’s teaching quality cannot be judged solely on the basis of results, just as whether a student is excellent or not cannot be judged solely on academic achievement. Furthermore, we cannot improve the education process unless we know what happened to students during the learning process. The teaching evaluation system is a campus-based evaluation system that is open, diverse, and people-oriented. The overall structure of the system is shown in Figure1. BD will be divided into the layers as given in the folowing.Figure 1
Overall structure of the BD teaching evaluation system.


### 3.1. Data Integration Layer

At the bottom of the whole data architecture, the data integration layer is the data source that the system needs to process, including private application data, data stored in the database, log data generated by the operation of the analyzed system, etc. These data have the characteristics of various structures and types, including structured data, unstructured or semistructured data, text format data, and video format data.


### 3.2. File Storage Layer

The file storage layer protects the upper application from technical details such as storage device type, model, interface protocol, and distribution location, and it provides various management functions to ensure reliable file access services such as data backup, fault tolerance, condition monitoring, and security mechanisms.


### 3.3. Data Storage Layer

Different from the traditional relational database, the data storage layer does not require complete support for SQL technology, nor does it require support for the storage function of relational data. It only needs to realize the management ability of large data table under low conditions. It can quickly complete data reading and writing operations under massive data and realize the linear growth of storage capacity through simple hardware expansion.


### 3.4. Platform Management

The platform management layer components, which primarily include configuration management, operation monitoring, fault management, performance optimization, safety management, and so on, are the guarantee for the smooth and safe operation of the entire data processing platform. This aspect of technology is still in its infancy. Zookeeper and Ambari are two open source technologies for this layer.From the depth of data, we can not only collect the data of learning results that students have achieved, but also record the influencing factors of learning results that have reached this level, measure and analyze each influencing factor periodically, and record the results. From the perspective of the breadth of data, the scope of data recording and analysis includes not only the macro national and regional level, the meso school level, and classroom level, but also the micro student level. The final result of the analytic hierarchy process is to obtain the teacher teaching evaluation index system of Table1 relative to the elements of the lowest level of the overall goal, which can form the corresponding hierarchical structure model, as shown in Figure 2.Figure 2
Analytic hierarchy process model of the teaching evaluation index system.Use the students’ performance of not listening carefully in offline class to highlight the characteristics of students’ learning attitude. The comprehensive performance of students not listening carefully isU, and the calculation method is(1)U=∑i=1NEiN.Here,Ei is the average value of careless listening performance in the i course and N is the number of observations.Ei is defined as(2)Ei=∑j=1nBijn.Here,Bij is the weight of the j performance factor in the i observation and n is the number of observation points.The online and offline comprehensive performance of students’i class is Si, which adopts the percentage system and the calculation method is(3)Si=xLi+y1−Ei×100+zKi3.Here,Li is the online comprehensive score of students' i class given by the system, and 1−Ei×100 is the average performance value of students' careful listening in i class. For the offline scores of Ki students in the i class, x,y,z are the weight of online comprehensive score, classroom learning attitude, and offline score, which can be dynamically determined according to the nature of the course and teachers’ needs. By default, the weight can be set equal, indicating the same degree of attention.To sum up, the comprehensive performance of students’ courses isS, and the calculation method is(4)S=∑i=1NSiN.Here,N is the number of observations, and Si is the comprehensive online and offline performance value of the students’ i course.The evaluation process of students’ participation in class is similar to that of students' learning attitude. The differences are as follows: when labeling images, find out all the students who are not listening carefully in the image sequence; performance quantification is to quantify the performance sequence according to the performance factors and the number of students with this performance and obtain the group performance value; performance analysis is to calculate the comprehensive value of classroom performance according to the performance value and evaluation method.Students’ class participation is recorded asC and calculated as follows:(5)C=∑i=1NFiN.Here,Fi is the average performance value of the proportion of students who listen carefully in class in the i course, and N is the number of observations.Fi is defined as follows:(6)Fi=∑j=1MPijM.Here,Pij is the proportion of students who listened carefully in class during the observation of j in the i course, and M is the number of observations.Pij is defined as follows:(7)Pij=mn.Here,m is the number of students who listen carefully within the specified range, and n is the total number of students within the specified range.The online and offline comprehensive participation of thei course of the student group is Ti, and the calculation method is(8)Ti=xFi+yLi2.Here,Fi is the average performance value of the proportion of students who attend class carefully in the abovementioned i course, and Li is the online participation degree given by the i course system. x,y are the weight of classroom participation and online participation, respectively, which can be dynamically determined according to the nature of the curriculum and teachers’ needs. By default, equal weight can be set, indicating equal attention.The online and offline comprehensive average score of thei course of the student group is recorded as Hi, and the calculation method is(9)Hi=xKi+yOi2.Here,Ki is the average score of offline homework of students in the i course, and Oi is the average score of online homework of students in the i course. x,y refer to their respective weights, which can be dynamically determined according to the nature of the course and the needs of teachers. By default, equal weights can be set, indicating the same degree of attention.To sum up, the comprehensive participation of students in the course isT, the average score is H, and the calculation method is(10)T=∑i=1NTiNH=∑i=1NHiN,where N is the number of observations, Ti is the online and offline comprehensive participation of the i course of the student group, and Hi is the online and offline comprehensive average score of the i course of the student group.The “accompanying” data collection method of BD provides “evidence-based” materials for the evaluation of the two paradigms at the same time, so as to realize the dual functions of accountability and improvement. As mentioned above, relying on BD, we can collect and analyze the whole process and multidimensional data related to students' learning, and all elements related to teaching quality evaluation are recorded in the form of data: digital facilities and equipment, popularized mobile learning, digital teaching behavior, visual learning behavior, etc. have become the data source of teaching quality evaluation in university. This “accompanying” collection method can avoid the temporary nature of university in dealing with external quality accountability and can also avoid teachers’ blindness in improving students' curriculum learning, so as to realize the evaluation function of accountability and improvement.


## 3.1. Data Integration Layer

At the bottom of the whole data architecture, the data integration layer is the data source that the system needs to process, including private application data, data stored in the database, log data generated by the operation of the analyzed system, etc. These data have the characteristics of various structures and types, including structured data, unstructured or semistructured data, text format data, and video format data.


## 3.2. File Storage Layer

The file storage layer protects the upper application from technical details such as storage device type, model, interface protocol, and distribution location, and it provides various management functions to ensure reliable file access services such as data backup, fault tolerance, condition monitoring, and security mechanisms.


## 3.3. Data Storage Layer

Different from the traditional relational database, the data storage layer does not require complete support for SQL technology, nor does it require support for the storage function of relational data. It only needs to realize the management ability of large data table under low conditions. It can quickly complete data reading and writing operations under massive data and realize the linear growth of storage capacity through simple hardware expansion.


## 3.4. Platform Management

The platform management layer components, which primarily include configuration management, operation monitoring, fault management, performance optimization, safety management, and so on, are the guarantee for the smooth and safe operation of the entire data processing platform. This aspect of technology is still in its infancy. Zookeeper and Ambari are two open source technologies for this layer.From the depth of data, we can not only collect the data of learning results that students have achieved, but also record the influencing factors of learning results that have reached this level, measure and analyze each influencing factor periodically, and record the results. From the perspective of the breadth of data, the scope of data recording and analysis includes not only the macro national and regional level, the meso school level, and classroom level, but also the micro student level. The final result of the analytic hierarchy process is to obtain the teacher teaching evaluation index system of Table1 relative to the elements of the lowest level of the overall goal, which can form the corresponding hierarchical structure model, as shown in Figure 2.Figure 2
Analytic hierarchy process model of the teaching evaluation index system.Use the students’ performance of not listening carefully in offline class to highlight the characteristics of students’ learning attitude. The comprehensive performance of students not listening carefully isU, and the calculation method is(1)U=∑i=1NEiN.Here,Ei is the average value of careless listening performance in the i course and N is the number of observations.Ei is defined as(2)Ei=∑j=1nBijn.Here,Bij is the weight of the j performance factor in the i observation and n is the number of observation points.The online and offline comprehensive performance of students’i class is Si, which adopts the percentage system and the calculation method is(3)Si=xLi+y1−Ei×100+zKi3.Here,Li is the online comprehensive score of students' i class given by the system, and 1−Ei×100 is the average performance value of students' careful listening in i class. For the offline scores of Ki students in the i class, x,y,z are the weight of online comprehensive score, classroom learning attitude, and offline score, which can be dynamically determined according to the nature of the course and teachers’ needs. By default, the weight can be set equal, indicating the same degree of attention.To sum up, the comprehensive performance of students’ courses isS, and the calculation method is(4)S=∑i=1NSiN.Here,N is the number of observations, and Si is the comprehensive online and offline performance value of the students’ i course.The evaluation process of students’ participation in class is similar to that of students' learning attitude. The differences are as follows: when labeling images, find out all the students who are not listening carefully in the image sequence; performance quantification is to quantify the performance sequence according to the performance factors and the number of students with this performance and obtain the group performance value; performance analysis is to calculate the comprehensive value of classroom performance according to the performance value and evaluation method.Students’ class participation is recorded asC and calculated as follows:(5)C=∑i=1NFiN.Here,Fi is the average performance value of the proportion of students who listen carefully in class in the i course, and N is the number of observations.Fi is defined as follows:(6)Fi=∑j=1MPijM.Here,Pij is the proportion of students who listened carefully in class during the observation of j in the i course, and M is the number of observations.Pij is defined as follows:(7)Pij=mn.Here,m is the number of students who listen carefully within the specified range, and n is the total number of students within the specified range.The online and offline comprehensive participation of thei course of the student group is Ti, and the calculation method is(8)Ti=xFi+yLi2.Here,Fi is the average performance value of the proportion of students who attend class carefully in the abovementioned i course, and Li is the online participation degree given by the i course system. x,y are the weight of classroom participation and online participation, respectively, which can be dynamically determined according to the nature of the curriculum and teachers’ needs. By default, equal weight can be set, indicating equal attention.The online and offline comprehensive average score of thei course of the student group is recorded as Hi, and the calculation method is(9)Hi=xKi+yOi2.Here,Ki is the average score of offline homework of students in the i course, and Oi is the average score of online homework of students in the i course. x,y refer to their respective weights, which can be dynamically determined according to the nature of the course and the needs of teachers. By default, equal weights can be set, indicating the same degree of attention.To sum up, the comprehensive participation of students in the course isT, the average score is H, and the calculation method is(10)T=∑i=1NTiNH=∑i=1NHiN,where N is the number of observations, Ti is the online and offline comprehensive participation of the i course of the student group, and Hi is the online and offline comprehensive average score of the i course of the student group.The “accompanying” data collection method of BD provides “evidence-based” materials for the evaluation of the two paradigms at the same time, so as to realize the dual functions of accountability and improvement. As mentioned above, relying on BD, we can collect and analyze the whole process and multidimensional data related to students' learning, and all elements related to teaching quality evaluation are recorded in the form of data: digital facilities and equipment, popularized mobile learning, digital teaching behavior, visual learning behavior, etc. have become the data source of teaching quality evaluation in university. This “accompanying” collection method can avoid the temporary nature of university in dealing with external quality accountability and can also avoid teachers’ blindness in improving students' curriculum learning, so as to realize the evaluation function of accountability and improvement.


## 4. Implementation of the Teaching Evaluation System in University

### 4.1. Design of the College Teaching Evaluation System under BD

Using the visualization and analysis tools of data mining system, such as SPSS, SAS, Weka, and so on, the track of teachers’ growth is generated from the change curve of various indicators of teaching evaluation data over the years. BD carries out cluster analysis on all kinds of data and aggregates the high and low points in the teaching evaluation data, so that we can fully understand what is popular and what needs further improvement. In addition, the association rule method can also be used to analyze the student’s teaching evaluation score and the student’s score of a course, to explore whether the student’s teaching evaluation score is objective and reasonable, so as to correct the teacher’s teaching evaluation score. BD has built a teaching evaluation system to ensure the fairness, impartiality, and objectivity of the evaluation through online and offline comprehensive evaluation, superior evaluation of subordinates, students’ evaluation of teachers, teachers’ evaluation of teachers, teachers’ personal evaluation, parents’ evaluation of teachers, and other evaluation means and subjects, so as to attract the attention of teachers and schools and improve their attention to teaching and finally promote the healthy development of the school cause. The three-tier architecture is closely linked, and it cannot work without one of them. This architecture system connects the basic data with the top-level end users to form a technical structure. The business layer is the middle layer of the system, which is mainly the logic layer to realize various functions of the system, realize the transformation of user operations, and convert user read-write operations into operation processing completed by various functions. The data layer is associated with the business logic layer through the interface to realize various operations of data and complete data reading and storage. The presentation layer is the interaction layer between the system and users. In addition to the interfaces of various peripherals, it also provides users’ access to the system. The application server in the teaching evaluation system separates the client from the database server, and the client cannot directly access the database server. The application server can control which data is changed and accessed and how the data is changed and accessed. In addition, the storage permissions of applications and data can be set hierarchically, so that even if the external intruder breaks through the security defense line of the client, if there is another security mechanism in the teaching evaluation system and database server, the system can also prevent the intruder from entering other parts.In the teaching evaluation system, we can use BD related technology to process the data collected during the teaching evaluation process at the university. By applying BD related technology to teaching evaluation, we can integrate and analyze collected data, dig deeply, and obtain a large amount of information, making the evaluation result more scientific, objective, and useful. The teaching evaluation system’s three-tier structure is clearly divided, logically independent, and self-contained. Because they are logically divided and do not always correspond to physical locations, their hardware system structure is very flexible, and each part can select the hardware that is best suited to its processing load and processing characteristics. The three layers can be on a single computer or two or more computers, as long as they adhere to the system’s three-layer structure. The indicators of university evaluation have become more specific since the advent of the BD era. Enriching the evaluation system and evaluation content can also make teachers more aware of their shortcomings and advantages, allowing them to continuously maximize their advantages, minimize their shortcomings, ensure their overall development, and ultimately promote the overall and healthy development of teaching.


### 4.2. Experimental Results and Analysis

The use of statistical models to analyze data is a modern society’s trend. Scholars are increasingly applying statistical models to various data analyses, such as financial data analysis and geological exploration data analysis. This chapter analyzes the score data of teachers’ teaching quality evaluation in a university using the factor analysis model and the analysis of variance model in statistics to provide a more reasonable evaluation method for teachers’ teaching quality evaluation. Check to see if each index of the teacher’s teaching quality evaluation system is suitable for factor analysis. To begin, the data of teachers’ teaching quality evaluations should be listed in the table according to the classification of indicators; then, using the SPSS17.0 software, factor analysis on the data listed in the table is performed to obtain the correlation coefficient matrix between indicators and kMO and Bartlett test tables, as shown in Tables1 and 2.Table 1
Correlation coefficient matrix.
X1X2X3X4X5X6X11.0110.6410.5630.6170.5520.487X20.6411.1210.6110.6530.6410.485X30.5630.6121.1240.5360.5530.456X40.6170.6540.5361.1210.6820.451X50.5540.6410.5530.6821.1310.457X60.4870.4880.4570.4510.4581.214Table 2
KMO and Bartlett test.
Kaiser–Meyer–Olkin measure of sampling degree0.896Bartlett’s sphericity testApproximate chi-square6619.943df14Sig.0.000As can be seen from Table1, most of the correlation coefficients between the evaluation indexes of teachers’ teaching quality are greater than 0.5, which means that most of the variables have strong correlation, so it is suitable for factor analysis.KMO test statistics and Bartlett sphericity test results can be seen from Table2. The observed value of Bartlett sphericity test statistic is 6619.943, and the corresponding probability p is almost close to 0. If the significance level α is 0.05, because the probability p is less than the significance level α, the zero hypothesis should be rejected, which means that the correlation coefficient matrix cannot be a unit matrix at all, which means that there must be correlation between the original variables, and it is suitable for factor analysis. The transformation of evaluation paradigm will direct the evaluation of higher education quality to the student center more directly. This student-centered view of higher education quality puts students' learning results and development in the fundamental position of higher education quality improvement, returns to the origin of university, and remoulds the primary position of higher education personnel training.After completing the calculation of the scores of each dimension of teachers through the efficient teacher ability rating system and obtaining the scores of different dimensions of teachers through the questionnaire and preprocessing the data, the evaluation system will be applied to test its effectiveness. According to the classroom video, this experiment intercepts the images of different behaviors of four students A, B, C, and D at different times in class, obtains their behavior performance in each class, and obtains their performance value at each observation time by combining the performance factors and weights. Get the classroom carelessness performance value and comprehensive performance value of students A, B, C and D, as shown in Figures3–5.Figure 3
Comparison of careless performance of observation objects in class.Figure 4
Comparison of online comprehensive scores of observation objects.Figure 5
Comparison of comprehensive performance of observation objects.From Figures3–5, we can see that students A, B, and D have relatively high online comprehensive scores, while student C has relatively low scores. Student A's offline scores are higher, while students B and C’s are lower. Student A has the best classroom learning attitude, followed by student B, then student D, and student C. Online data can reflect the learning situation of students to a certain extent, but can not fully reflect the learning effect of students. It is necessary to comprehensively consider the offline situation. From the online data, students B, A, and D scored higher. However, online data contains many dimensions, and relevant online data outside the classroom can only provide some reference for students' learning. Whether teachers’ teaching quality evaluation is reasonable or not is not only related to teachers’ own interests, but also related to various teaching activities in university and the interests of colleges and students. Scientific and reasonable evaluation of teachers' teaching quality will not only provide a favorable basis for efficient teaching management in university, but also more effectively improve teachers’ teaching level and better cultivate students’ ability. According to the calculation of this experiment, the comparison chart of each class participation and average score of Class A, Class B, and Class D is shown in Figures 6 and 7.Figure 6
Comparison chart of the participants' participation in class.Figure 7
Comparison chart of comprehensive average score of observed objects.From Figures6 and 7, it can be seen that, with the same teacher and the same teaching method, the teaching effect of Class A students is slightly worse, while that of Class B students is relatively better. Class D students are in the middle. Class B students are active and active in the learning process, and their participation in the course is high both online and offline, so the overall average score of the class is higher than that of Class A and Class D. Moreover, for these students with strong learning initiative, the course adopts the form of flipped classroom, with students as the main body and teachers as the leading factor, which can improve students’ learning efficiency and learning quality. For students who are not very active in the learning process, teaching is mainly in the form of lectures, and the teaching effect is relatively good. Students mainly have the right to inquire and modify basic information and evaluate teachers’ teaching. Teachers and experts have basically the same authority as students and can only edit their own information and make corresponding evaluation. Therefore, by setting different roles, the security of system information can be further improved.


## 4.1. Design of the College Teaching Evaluation System under BD

Using the visualization and analysis tools of data mining system, such as SPSS, SAS, Weka, and so on, the track of teachers’ growth is generated from the change curve of various indicators of teaching evaluation data over the years. BD carries out cluster analysis on all kinds of data and aggregates the high and low points in the teaching evaluation data, so that we can fully understand what is popular and what needs further improvement. In addition, the association rule method can also be used to analyze the student’s teaching evaluation score and the student’s score of a course, to explore whether the student’s teaching evaluation score is objective and reasonable, so as to correct the teacher’s teaching evaluation score. BD has built a teaching evaluation system to ensure the fairness, impartiality, and objectivity of the evaluation through online and offline comprehensive evaluation, superior evaluation of subordinates, students’ evaluation of teachers, teachers’ evaluation of teachers, teachers’ personal evaluation, parents’ evaluation of teachers, and other evaluation means and subjects, so as to attract the attention of teachers and schools and improve their attention to teaching and finally promote the healthy development of the school cause. The three-tier architecture is closely linked, and it cannot work without one of them. This architecture system connects the basic data with the top-level end users to form a technical structure. The business layer is the middle layer of the system, which is mainly the logic layer to realize various functions of the system, realize the transformation of user operations, and convert user read-write operations into operation processing completed by various functions. The data layer is associated with the business logic layer through the interface to realize various operations of data and complete data reading and storage. The presentation layer is the interaction layer between the system and users. In addition to the interfaces of various peripherals, it also provides users’ access to the system. The application server in the teaching evaluation system separates the client from the database server, and the client cannot directly access the database server. The application server can control which data is changed and accessed and how the data is changed and accessed. In addition, the storage permissions of applications and data can be set hierarchically, so that even if the external intruder breaks through the security defense line of the client, if there is another security mechanism in the teaching evaluation system and database server, the system can also prevent the intruder from entering other parts.In the teaching evaluation system, we can use BD related technology to process the data collected during the teaching evaluation process at the university. By applying BD related technology to teaching evaluation, we can integrate and analyze collected data, dig deeply, and obtain a large amount of information, making the evaluation result more scientific, objective, and useful. The teaching evaluation system’s three-tier structure is clearly divided, logically independent, and self-contained. Because they are logically divided and do not always correspond to physical locations, their hardware system structure is very flexible, and each part can select the hardware that is best suited to its processing load and processing characteristics. The three layers can be on a single computer or two or more computers, as long as they adhere to the system’s three-layer structure. The indicators of university evaluation have become more specific since the advent of the BD era. Enriching the evaluation system and evaluation content can also make teachers more aware of their shortcomings and advantages, allowing them to continuously maximize their advantages, minimize their shortcomings, ensure their overall development, and ultimately promote the overall and healthy development of teaching.


## 4.2. Experimental Results and Analysis

The use of statistical models to analyze data is a modern society’s trend. Scholars are increasingly applying statistical models to various data analyses, such as financial data analysis and geological exploration data analysis. This chapter analyzes the score data of teachers’ teaching quality evaluation in a university using the factor analysis model and the analysis of variance model in statistics to provide a more reasonable evaluation method for teachers’ teaching quality evaluation. Check to see if each index of the teacher’s teaching quality evaluation system is suitable for factor analysis. To begin, the data of teachers’ teaching quality evaluations should be listed in the table according to the classification of indicators; then, using the SPSS17.0 software, factor analysis on the data listed in the table is performed to obtain the correlation coefficient matrix between indicators and kMO and Bartlett test tables, as shown in Tables1 and 2.Table 1
Correlation coefficient matrix.
X1X2X3X4X5X6X11.0110.6410.5630.6170.5520.487X20.6411.1210.6110.6530.6410.485X30.5630.6121.1240.5360.5530.456X40.6170.6540.5361.1210.6820.451X50.5540.6410.5530.6821.1310.457X60.4870.4880.4570.4510.4581.214Table 2
KMO and Bartlett test.
Kaiser–Meyer–Olkin measure of sampling degree0.896Bartlett’s sphericity testApproximate chi-square6619.943df14Sig.0.000As can be seen from Table1, most of the correlation coefficients between the evaluation indexes of teachers’ teaching quality are greater than 0.5, which means that most of the variables have strong correlation, so it is suitable for factor analysis.KMO test statistics and Bartlett sphericity test results can be seen from Table2. The observed value of Bartlett sphericity test statistic is 6619.943, and the corresponding probability p is almost close to 0. If the significance level α is 0.05, because the probability p is less than the significance level α, the zero hypothesis should be rejected, which means that the correlation coefficient matrix cannot be a unit matrix at all, which means that there must be correlation between the original variables, and it is suitable for factor analysis. The transformation of evaluation paradigm will direct the evaluation of higher education quality to the student center more directly. This student-centered view of higher education quality puts students' learning results and development in the fundamental position of higher education quality improvement, returns to the origin of university, and remoulds the primary position of higher education personnel training.After completing the calculation of the scores of each dimension of teachers through the efficient teacher ability rating system and obtaining the scores of different dimensions of teachers through the questionnaire and preprocessing the data, the evaluation system will be applied to test its effectiveness. According to the classroom video, this experiment intercepts the images of different behaviors of four students A, B, C, and D at different times in class, obtains their behavior performance in each class, and obtains their performance value at each observation time by combining the performance factors and weights. Get the classroom carelessness performance value and comprehensive performance value of students A, B, C and D, as shown in Figures3–5.Figure 3
Comparison of careless performance of observation objects in class.Figure 4
Comparison of online comprehensive scores of observation objects.Figure 5
Comparison of comprehensive performance of observation objects.From Figures3–5, we can see that students A, B, and D have relatively high online comprehensive scores, while student C has relatively low scores. Student A's offline scores are higher, while students B and C’s are lower. Student A has the best classroom learning attitude, followed by student B, then student D, and student C. Online data can reflect the learning situation of students to a certain extent, but can not fully reflect the learning effect of students. It is necessary to comprehensively consider the offline situation. From the online data, students B, A, and D scored higher. However, online data contains many dimensions, and relevant online data outside the classroom can only provide some reference for students' learning. Whether teachers’ teaching quality evaluation is reasonable or not is not only related to teachers’ own interests, but also related to various teaching activities in university and the interests of colleges and students. Scientific and reasonable evaluation of teachers' teaching quality will not only provide a favorable basis for efficient teaching management in university, but also more effectively improve teachers’ teaching level and better cultivate students’ ability. According to the calculation of this experiment, the comparison chart of each class participation and average score of Class A, Class B, and Class D is shown in Figures 6 and 7.Figure 6
Comparison chart of the participants' participation in class.Figure 7
Comparison chart of comprehensive average score of observed objects.From Figures6 and 7, it can be seen that, with the same teacher and the same teaching method, the teaching effect of Class A students is slightly worse, while that of Class B students is relatively better. Class D students are in the middle. Class B students are active and active in the learning process, and their participation in the course is high both online and offline, so the overall average score of the class is higher than that of Class A and Class D. Moreover, for these students with strong learning initiative, the course adopts the form of flipped classroom, with students as the main body and teachers as the leading factor, which can improve students’ learning efficiency and learning quality. For students who are not very active in the learning process, teaching is mainly in the form of lectures, and the teaching effect is relatively good. Students mainly have the right to inquire and modify basic information and evaluate teachers’ teaching. Teachers and experts have basically the same authority as students and can only edit their own information and make corresponding evaluation. Therefore, by setting different roles, the security of system information can be further improved.


## 5. Conclusions

With the more and more extensive acceptance and practice of educational ideas, higher requirements are put forward for the quality of teachers’ classroom teaching. This paper attempts to explore educational BD in the teaching mode, establish an evaluation model, and analyze how these large and multidimensional data truly reflect “students’ learning” and “teachers’ teaching,” so as to stop relying too much on experience and make the data the basis of educational decision-making. It can dig out students’ real learning ability more comprehensively and can deeply grasp students’ learning status, so as to achieve the goal of teaching according to people. It can also give early warning of students’ bad behavior according to the growth and change of students’ learning status. This paper puts forward the teaching evaluation system of university in the era of BD, refines the indicators of each dimension through layering and grading, and determines the calculation method of the indicators. It can be seen from the experiment that the observed value of Bartlett sphericity test statistic is 6619.943, and the corresponding probabilityp is almost close to 0. If the significance level α is 0.05, the zero hypothesis should be rejected because the probability p is less than the significance level α, which shows that the correlation coefficient matrix can not be the unit matrix at all, which also shows that there must be correlation between the original variables, which is suitable for factor analysis. To realize the institutionalization of data management, we need to mobilize all departments of university to participate together and clarify the responsibilities of each department in data management, so as to form a periodic and continuous data cycle process. The data design stage usually starts from the teaching research center or teacher development center. According to the theoretical basis of psychology, pedagogy, and sociology, the evaluation data contents and methods are designed. It is believed that the application of education BD will be more and more extensive and in-depth, which can continuously promote the improvement of education quality and the intelligent reform of education system.



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*Source: 5438050-2022-07-04.xml*