# Modeling of Ideological and Political Education System in Colleges and Universities Based on Naive Bayes-BP Neural Network in the Era of Big Data

Authors: Yan Cui
Journal: Mobile Information Systems
 (2022)

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## Abstract

Big data is the product of the development of the times, which affects all aspects of social development and reorganizes the ideological and political network system of universities. The development of big data brings new opportunities and challenges to the NIPE (network ideological and political education) system of universities. Therefore, universities should pay attention to the data in the ideological and political network system. The NIPEDM service platform was developed, which combines the existing Web-based PC and mobile development technology to find a new educational DM (data mining) path. Using classifiers to predict students’ future performance, a new NB_BPNN (Naive Bayes-BP neural network) model is proposed, which effectively combines the advantages of two existing models. The experimental results show that the new model has achieved good results in the field of learning evaluation.


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## Body

## 1. Introduction

With the development of information technology [1], it has entered the era of big data, which is characterized by massive information data, rich diversity, and fast data processing. In this era, university NIPE (network ideological and political education) has great development space and opportunities. At the same time, we should keep pace with the times, grasp the advantages of big data, and constantly improve and develop the university NIPE system. Therefore, university NIPE must find working ideas and goals to serve students’ growth and development, establish the concept of student-centered and service-oriented, and serve students’ growth and development. We must strengthen college students’ ideological and political education in order to build a high-quality and distinctive university [2]. For the management of traditional university NIPE, the in-depth development of the era of big data is both a challenge and an opportunity. At the moment, there is a new trend in university NIPE management [3].With the rapid advancement of technology, the flow of information is becoming increasingly smooth, and people’s communication is becoming increasingly intimate. Big data is a result of this era’s development. The collection, organization, and analysis of a large amount of data over a short period of time are referred to as big data. NIPE business in universities is facing new crises and challenges in the era of big data, which necessitates a deeper understanding and analysis. The new period’s combination of education and management and the strengthening of NIPE management in universities are all related to improving the suitability and effectiveness of ideological and political education, as well as its appeal, persuasiveness, and training. The formation of “four new talents” in university NIPE provides a guarantee for the successful completion of basic tasks [4]. Li and others said “Schools can actively exert the power of collective education, give full play to the role of democratic management, improve students’ self-management ability, realize the effective combination of education and management, and make students achieve coordination and unity” [5]. Xu pays attention to humanistic management thought and pays more attention to students’ subjective understanding of value judgment and behavior choice [6]. Through the evaluation and explanation of various values, Gurcan and Cagiltay expounded the influence and value of information technology and Internet on the times and people’s lives [7]. Ms and others believe that the political education in science colleges mainly involves five aspects: leadership, planning, control, evaluation, and management innovation [8]. Zhang et al. believe that big data is the precondition to achieve the goal and plays the role of prediction, guidance, and control [9].NIPE work is a collaborative effort between educators and educational materials. We can effectively guide the orderly progress of the university NIPE project if we strengthen the research of the university NIPE method. Strengthening university NIPE method research in university NIPE courses is conducive to equal communication between NIPE workers and college students, as well as changing the college students’ habit of unilateral brainwashing. Enhance the effectiveness of NIPE at the university level. This paper systematically introduces the thoughts and ideas of the university NIPE system from a global perspective, advances the concept of refined management and systematic promotion of university NIPE, and enriches the university NIPE theory. Improve and provide a theoretical foundation for university NIPE practice and NIPE.Innovation:(1)
Based on the understanding of the connotation of the era of big data, the Internet is recognized as an important means to solve NIPE management problems in universities. Taking advantage of the Internet, NIPE management in universities can become a dynamic and open operation mode, enhance business timeliness, and successfully complete the objectives and tasks of NIPE(2)
In this paper, we use the supervised learning method as the basic model of a classifier and propose NB_BPNN based on NB and BPNN models. The model has achieved good results in student behavior evaluationThe structure of this topic chapter is as follows:The first section of this paper introduces the research background and significance before moving on to the main work. The second section focuses on the education DM technologies. The third section outlines the research’s specific methods and methods of implementation. The fourth section establishes the research model’s superiority and viability. The summary and prospect of the full text are presented in the fifth section.


## 2. Related Work

### 2.1. Research Status of NIPE

The exploratory stage of the university NIPE methodology conceptual research is currently underway. Equal interaction, virtual context, and modern openness, according to Rubin et al. [10], are the main methods of NIPE. The university’s NIPE methods were summarized by Shadroo and Rahmani as follows: information, information hiding, subject interaction, virtual reality, and online and offline education methods [11]. Zhang proposed, first, that all teachers and students be informed about university NIPE; second, that a firm position and concept be established; third, that management transformation be realized; and fourth, that compulsory education not be introduced and that the concept of concept guidance be implemented [12]. Continuous improvement and perfection should be carried out in the process of forming the NIPE system, according to Wang et al. [13]. The most important point in the so-called online NIPE, according to Sumalatha and Subramanyam, is online psychological education [14], which is in addition to online moral and rational education. According to Mapca et al. [15], the current NIPE of the school network is plagued by the following issues: first, the internal system is flawed, and educational resources are in short supply; second, the Internet platform facilitates the spread of false information, and the lack of strict laws and regulations limits network-related laws; and third, Western ideas gradually influenced college students’ self-esteem, and the concept of foreigners and their worship began to spread.In a word, it can be seen that there are significant defects in NIPE online. First of all, from the perspective of communication, universities have not done in-depth research and research on the problems arising from the development of network ideological and political education. Second, there is no corresponding solution to the problem. You may find that universities still need to constantly explore problems and solutions to find a better breakthrough.


### 2.2. Application and Development of DM in the Field of Education

In the field of education, DM (data mining) is a relatively new information processing technology, and its application and research are not yet mature. With the continuous progress of this technology in the field of education, many university staffs have begun to apply DM technology to many systems such as teacher management, teacher evaluation, personalized training, and rationalization of curriculum structure, which will improve the effective management of universities, and played a prominent leading role in the remarkable improvement of education level.DM technology has advanced rapidly in recent decades. In general, there have been a lot of international studies in the field of DM, and the results have been very impressive. Jin et al. [16] proposed an Apriori-based DM method. An improved NB (Naive Bayes) model was proposed by Braun et al. [17]. Yang and colleagues enhanced the existing BPNN (BP neural network) algorithm and applied it to DM technology. The results of the comparison show that the improved algorithm can improve data classification and recognition ability [18]. Jk and Jak developed a sliding window model-based K-medians clustering algorithm [19]. To construct learners’ ability DT, Majeed et al. used the ID3 algorithm, which is classified as DT (Decision Tree) [20]. Park and colleagues developed a variable precision rough set model based on relational calculus and conducted a preliminary analysis of students’ performance [21]; Xie et al. used the Apriori association rule algorithm to analyze the key factors that lead to students receiving excellent degrees in professional courses [22].Of course, there are still some problems in the application of DM in education at this stage. There is a huge amount of information data for students in universities, but at the same time, the valuable information obtained by educators is very scarce. Therefore, what educators need to do now is to conduct extensive screening and in-depth analysis of a large number of student data, so as to obtain valuable information that can guide the development of educational decision-making and education system. We still have a long way to go in this respect.


## 2.1. Research Status of NIPE

The exploratory stage of the university NIPE methodology conceptual research is currently underway. Equal interaction, virtual context, and modern openness, according to Rubin et al. [10], are the main methods of NIPE. The university’s NIPE methods were summarized by Shadroo and Rahmani as follows: information, information hiding, subject interaction, virtual reality, and online and offline education methods [11]. Zhang proposed, first, that all teachers and students be informed about university NIPE; second, that a firm position and concept be established; third, that management transformation be realized; and fourth, that compulsory education not be introduced and that the concept of concept guidance be implemented [12]. Continuous improvement and perfection should be carried out in the process of forming the NIPE system, according to Wang et al. [13]. The most important point in the so-called online NIPE, according to Sumalatha and Subramanyam, is online psychological education [14], which is in addition to online moral and rational education. According to Mapca et al. [15], the current NIPE of the school network is plagued by the following issues: first, the internal system is flawed, and educational resources are in short supply; second, the Internet platform facilitates the spread of false information, and the lack of strict laws and regulations limits network-related laws; and third, Western ideas gradually influenced college students’ self-esteem, and the concept of foreigners and their worship began to spread.In a word, it can be seen that there are significant defects in NIPE online. First of all, from the perspective of communication, universities have not done in-depth research and research on the problems arising from the development of network ideological and political education. Second, there is no corresponding solution to the problem. You may find that universities still need to constantly explore problems and solutions to find a better breakthrough.


## 2.2. Application and Development of DM in the Field of Education

In the field of education, DM (data mining) is a relatively new information processing technology, and its application and research are not yet mature. With the continuous progress of this technology in the field of education, many university staffs have begun to apply DM technology to many systems such as teacher management, teacher evaluation, personalized training, and rationalization of curriculum structure, which will improve the effective management of universities, and played a prominent leading role in the remarkable improvement of education level.DM technology has advanced rapidly in recent decades. In general, there have been a lot of international studies in the field of DM, and the results have been very impressive. Jin et al. [16] proposed an Apriori-based DM method. An improved NB (Naive Bayes) model was proposed by Braun et al. [17]. Yang and colleagues enhanced the existing BPNN (BP neural network) algorithm and applied it to DM technology. The results of the comparison show that the improved algorithm can improve data classification and recognition ability [18]. Jk and Jak developed a sliding window model-based K-medians clustering algorithm [19]. To construct learners’ ability DT, Majeed et al. used the ID3 algorithm, which is classified as DT (Decision Tree) [20]. Park and colleagues developed a variable precision rough set model based on relational calculus and conducted a preliminary analysis of students’ performance [21]; Xie et al. used the Apriori association rule algorithm to analyze the key factors that lead to students receiving excellent degrees in professional courses [22].Of course, there are still some problems in the application of DM in education at this stage. There is a huge amount of information data for students in universities, but at the same time, the valuable information obtained by educators is very scarce. Therefore, what educators need to do now is to conduct extensive screening and in-depth analysis of a large number of student data, so as to obtain valuable information that can guide the development of educational decision-making and education system. We still have a long way to go in this respect.


## 3. Methodology

### 3.1. NIPEDM Service Platform

The existing information system is slow in obtaining information, lagging behind, and has many channels for information transmission, but it is complex and unbalanced. With the advent of the era of big data, massive data can be quickly collected and processed, effectively improving the timeliness of the university NIPE system and maximizing the educational role of the NIPE system. With the help of big data, universities can better capture students’ ideological trends and reeducate them through relevant platforms to improve the effectiveness of NIPE. In short, with the development of big data, universities can not only get students’ ideological trends from big data but also spread ideological and political theories through various big data channels, so as to achieve the purpose of human education. It is also an information collection window and a data release window.The iterative loop solution process is known as DM. First and foremost, it extracts data from a database, unearths potentially unknown useful information, and applies it to people’s production and lives. Problem definition, data preparation, DM, analysis, and summary are the four steps. DM has the advantage of being able to analyze and predict future behaviors or trends and then use the information to assist humans in making decisions. It has made human production and life easier in many ways. Applications have gradually penetrated all levels due to the rapid development of DM-related technologies. The use of DM-related technologies in education is unavoidable [16]. The K-means clustering algorithm, which belongs to unsupervised clustering learning, is based on a simple concept. It aggregates but does not belong to a single related class with the same or similar attributes based on performance similarity of attributes. Clustering into categories using a traditional algorithm. The K-means algorithm works on the following principle: the average algorithm’s main idea is to divide a large number of data samples into k clusters. To begin, choose K data points from the cluster at random to serve as the initial cluster center, calculate the distance between each point in the centroid, divide all data objects by the nearest cluster centroid, average all points in each cluster, rewrite a new point for each class, and repeat until the last time. If there is no change, the clustering algorithm is complete, and the position where the final class is determined remains unchanged.The purpose of association analysis task is to discover the potential internal relations between data and find valuable associations. For example, there is a relationship between product A and product B because when users go to the supermarket, they often buy product A and product B at the same time. The purpose of association analysis task is to find these implicit associations, which is essentially an explanatory task. The advantages of software based on B/S architecture are good deployment, good scalability, strong interactivity, and relatively simple maintenance. It also has a relatively simple system upgrade routine. The NIPEDM service platform integrates the advantages of Web client and mobile client and is committed to integrating massive educational information, finding useful information from it, inquiring information quickly and conveniently anytime, anywhere, and supporting campus decision-making.The database sends commands to determine whether the database can be accessed directly; the database returns response information and sends the service request to the Web or mobile terminal through the data layer, the cache layer, and the business logic layer to realize traversal. The overall framework divided by commands is shown in Figure1.Figure 1
Platform integral frame.The data layer is mainly responsible for data storage and conversion, while the cache layer is mainly used to reduce server load and speed up program access, because the cache layer caches datasets in the database, and the cache time is short. The logic layer is responsible for exchanging data and completing the execution of business processes before and after linking.


### 3.2. NIPE Association Rule Mining

At present, the imperfect university NIPE system is largely due to the negligence of relevant personnel. In order to establish a complete NIPE system, relevant university leaders, all relevant personnel, university NIPE staff, and all members of society must carry out university NIPE work and promote the construction and improvement of the university NIPE system by constructing data formed by thinking. Through the research and analysis of students’ information data, in order to find specific rules and make specific predictions for future data, we can know the changing direction of college students’ psychological state and ideas in advance and formulate preventive measures accordingly. Students should be able to watch educational videos and explore educational content whenever and wherever they want, and teachers should be able to create educational content and adjust lecture time whenever and wherever they want. The online NIPE platform generates a lot of information. Big education data can analyze and mine all kinds of information hidden behind the data based on massive data information about individual students, making it more targeted and personalized. Individual students generate individual information sources in educational big data as part of the normal learning process. Educators can use real-time data collection and analysis to guide their teaching methods in a timely and targeted manner.The Apriori algorithm must scan the whole database when finding a set of candidate frequencies, and with the increase of the number of sets of 1 frequency entries, the number of sets of 2 candidates increases linearly, which leads to excessive I/O load and high efficiency. The parallel Apriori algorithm based on the Hadoop platform can effectively make up for these shortcomings by using distributed clusters.LetX=x1,x2,⋯,xn be the set of data points to be clustered, and k be the number of categories to be divided. The K-means algorithm first randomly selects k data points from X as the initial cluster, then calculates the distance between data points and cluster centroid, assigns the data points to the nearest centroid, recalculates the centroid after obtaining a new partition, and repeats the above process until the reference function converges.Here, the general definition of the criterion function is as follows:(1)E=∑i=1k∑x∈Cix−vi2.Among them,Ci represents the set of data points belonging to class i, vi is the middle point of class i, and ⋅ represents the norm, which is a distance calculation method, and the Euclidean distance is usually used.If the centroids of two clusters are not the closest but have similar density distribution, they are considered to be merged. If two clusters with similar density distribution are too far apart, they should not be merged. Based on this idea, considering distance and data distribution, this study proposes a new method to merge local clustering results.For the cluster centerVnew=V1new,V2new,⋯,VN×knew obtained by the local clustering stage, the Euclidean distance is used to measure the difference between the two cluster centers in the distance.Define the difference between two classesVinew,Vjnew, see the following formula:
(2)DIS=Vinew,Vjnew=1−βDVinew,Vjnew+βDKLVinew,Vjnew.In the above formula,β represents the weight coefficient, which represents the influence coefficient of distance difference and distribution difference on the difference between two classes. The following formula gives the definition of β:
(3)β=maxCVi,CVjCVi+CVj,whereCVi represents the set of data points belonging to class i in the global dataset. According to the definition of slice coefficient, when there is a big difference between two classes, the influence of distribution difference on the two classes is mainly considered.According to the core idea of the Apriori algorithm, a set of 1_frequent items must be mined first, and parallel mining of 1_frequent items in Map/Reduce is equivalent to processing the number of words of one item in each transaction. Therefore, in the Map operation, the output key-value pair of the map can be changed to use the previousk−2 item as the key and k−1item as the value. The first k−2 entries and k−1 entries.Using the existing Apriori algorithm, actionable information can be found by fully mining a set of frequent items, but if we analyze the dataset generated by users clicking online courses from the training data, as you can see, the number of times a user clicks on an online course is a number. The traditional Apriori algorithm must be a set of Boolean data when mining association rules, and the numerical data must be processed to adapt to the algorithm.Before association mining, the FT (frequent tree) algorithm is similar to the Apriori algorithm, which realizes the setting of a minimum support and selects a group of frequent items according to this support. The core idea of the algorithm is to construct an FT based on a group of items and mark the FT with relevant information. It scans the database once, mining FT from bottom to top, and deleting the child nodes of FT to generate the required frequency set.If the new data isN, the data to be processed is O and the standard deviation is K; then, the formula for z-score standardization is
(4)N=O−O¯K.The processed data can better reflect users’ clicks on online courses, thus making the mining of link rules more accurate.Finally, the generated key-value pair is used as the input of Reduce, in which the Reduce operation combines two elements of the array in key and value to generate a set of k_candidate items. For example, if there are 6 groups of 3_frequencies, the process of grouping them to generate 4_candidate 3_frequent set is shown in Figure2.Figure 2
Candidate frequency set mining.


### 3.3. NIPE Achievement Forecast

The classifier predicts the students’ recent performance, and the classifier predicts the students’ future performance according to the data. The classifier model calculates the probability that samples fall into another category and assigns the most likely category to the predicted category. The selection of features will also affect the final classification results. Therefore, in this chapter, how to choose students’ characteristics and how to define students’ categories are the first issues to be considered.This paper adopts the supervised learning method as the basic model of a classifier. The task of supervising the classifier is to learn how to make the classification model better predict the category of a given sample for classification. In this paper, based on the two existing models, this study proposes a new NB_BPNN model, which effectively combines the advantages of two existing models.Let the input spaceδ∈RK be the set of all K-dimensional feature vectors, the category set of the output space β∈c1,c2,⋯,cm, the input feature vector v∈δ, and the output category y∈β, v are random variables defined on δ, Y is the random variable defined on β, and PV,Y is the joint probability distribution of V,Y. Assume that the training dataset is
(5)T=v1,y1,v2,y2,⋯,vN,yN.Let the possible value ofith feature vi be si1,⋯,sil,⋯, then the maximum likelihood estimation of conditional probability PVi=silY=cj is
(6)PVi=silY=cj=∑n=1NIvni=sil,yn=cj∑n=1Nyn=cj,i=1,2,⋯,K,j=1,2,⋯,m.In the formula,vni is the ith feature of the nth training sample; sil is the lth possible value of the ith feature, and I is the indicator function of classification.Where in order to ensure that the probability sum is still 1, a term ofm×1 is added to the denominator, where m is the total number of all possible values of this dimension feature. Similarly, the estimation result of prior probability is deformed as follows:
(7)PY=cj=∑n=1NIyn=cj+1N+m×1.Type, in order to ensure that the probability sum is still 1, the denominator adds a term ofm×1.In this chapter, we put forward the NB_BPNN model to predict students’ future learning outcomes. In this structure, we first extract and transform nonlinear features through multilayer BPNN and then send the output of neural network to NB classifier to get the corresponding classification. The left and right parts of the model are treated in the same way as the traditional neural network or NB model, which is an improved model combining NB and BPNN. The structure is shown in Figure3.Figure 3
NP_BPNN model structure.The specific calculation method is to modify the weights of NB by loss first, then find the partial derivative of loss to the connection vector, and finally modify the partial derivative of the ownership value of neural network by gradient descent. In the testing stage, every time the characteristics of the test sample are input, the intermediate output is obtained through several layers of networks, and the intermediate output is input into the NB classifier, and the classification is judged according to the output.The AP (Affinity Propagation) algorithm proposes an effective method to find out the center point ofK, and it can satisfy the condition that the sum of the above similarities is the minimum. Let σi represent the center point closest to the data point ei, then the goal of the algorithm is to find the most suitable set of σ, so as to maximize Eσ as defined in the following:
(8)Eσ=∑i=1NSei,eσi−∑i=1NXiσ.When the Euclidean distance is used as the similarity between points,Sei,ej is defined as −di,j2. When i=j, Sei,ej is called the reference degree of data point i, which is recorded as Pi. The larger the value of Pi, the more likely the data point i is to become the cluster representative point.The WAP (Weighted AP) algorithm is a generalization of the AP algorithm, which is suitable for clustering complex multidata sets. The WAP algorithm can merge continuous points in large datasets and make the clustering process more efficient without changing the clustering results.The definition datasetξ′=ei,ni contains ni data items ei, of which i=1,⋯,L. Then, the similarity matrix of dataset ξ′ is defined as follows:
(9)S′=ei,ej=−nidi,j2,i≠j,s∗+ni−1×εi,εi≥0.Solve the problem as the center pointσ of the above dataset ξ′, that is, solve
(10)E′σ=∑i=1LS′ei,eσi−∑i=1LXiσ.The maximum optimization problem, if the datasetξ consists of ni data items ei=i=1,⋯,L and εi=0, is equivalent to the optimization problem.Therefore, this paper uses the data processing mechanism of the Hadoop platform and the divide-and-conquer idea of the HI_WAP algorithm to design the P_WAP (Parallel WAP) algorithm.Assuming the size of the original datasetξ is N, we can see from Figure 4 that the basic idea of the P_WAP algorithm is as follows:Figure 4
P_WAP algorithm.Distributing a subset of data nodes with similarK performance of Hadoop, this balanced job scheduling and processing mechanism is the unique advantage of the Hadoop platform. The clustering results are stored on the local disk for subsequent processing. If the amount of data is very large, the number of cluster representative points generated in the WAP stage is very large. At this time, WAP clustering can be carried out for many times to obtain enough clustering results. The obtained clustering results are reduced by the reduce function, so that the same data representative points are clustered into the same cluster, and finally, the results are written into the output file.


## 3.1. NIPEDM Service Platform

The existing information system is slow in obtaining information, lagging behind, and has many channels for information transmission, but it is complex and unbalanced. With the advent of the era of big data, massive data can be quickly collected and processed, effectively improving the timeliness of the university NIPE system and maximizing the educational role of the NIPE system. With the help of big data, universities can better capture students’ ideological trends and reeducate them through relevant platforms to improve the effectiveness of NIPE. In short, with the development of big data, universities can not only get students’ ideological trends from big data but also spread ideological and political theories through various big data channels, so as to achieve the purpose of human education. It is also an information collection window and a data release window.The iterative loop solution process is known as DM. First and foremost, it extracts data from a database, unearths potentially unknown useful information, and applies it to people’s production and lives. Problem definition, data preparation, DM, analysis, and summary are the four steps. DM has the advantage of being able to analyze and predict future behaviors or trends and then use the information to assist humans in making decisions. It has made human production and life easier in many ways. Applications have gradually penetrated all levels due to the rapid development of DM-related technologies. The use of DM-related technologies in education is unavoidable [16]. The K-means clustering algorithm, which belongs to unsupervised clustering learning, is based on a simple concept. It aggregates but does not belong to a single related class with the same or similar attributes based on performance similarity of attributes. Clustering into categories using a traditional algorithm. The K-means algorithm works on the following principle: the average algorithm’s main idea is to divide a large number of data samples into k clusters. To begin, choose K data points from the cluster at random to serve as the initial cluster center, calculate the distance between each point in the centroid, divide all data objects by the nearest cluster centroid, average all points in each cluster, rewrite a new point for each class, and repeat until the last time. If there is no change, the clustering algorithm is complete, and the position where the final class is determined remains unchanged.The purpose of association analysis task is to discover the potential internal relations between data and find valuable associations. For example, there is a relationship between product A and product B because when users go to the supermarket, they often buy product A and product B at the same time. The purpose of association analysis task is to find these implicit associations, which is essentially an explanatory task. The advantages of software based on B/S architecture are good deployment, good scalability, strong interactivity, and relatively simple maintenance. It also has a relatively simple system upgrade routine. The NIPEDM service platform integrates the advantages of Web client and mobile client and is committed to integrating massive educational information, finding useful information from it, inquiring information quickly and conveniently anytime, anywhere, and supporting campus decision-making.The database sends commands to determine whether the database can be accessed directly; the database returns response information and sends the service request to the Web or mobile terminal through the data layer, the cache layer, and the business logic layer to realize traversal. The overall framework divided by commands is shown in Figure1.Figure 1
Platform integral frame.The data layer is mainly responsible for data storage and conversion, while the cache layer is mainly used to reduce server load and speed up program access, because the cache layer caches datasets in the database, and the cache time is short. The logic layer is responsible for exchanging data and completing the execution of business processes before and after linking.


## 3.2. NIPE Association Rule Mining

At present, the imperfect university NIPE system is largely due to the negligence of relevant personnel. In order to establish a complete NIPE system, relevant university leaders, all relevant personnel, university NIPE staff, and all members of society must carry out university NIPE work and promote the construction and improvement of the university NIPE system by constructing data formed by thinking. Through the research and analysis of students’ information data, in order to find specific rules and make specific predictions for future data, we can know the changing direction of college students’ psychological state and ideas in advance and formulate preventive measures accordingly. Students should be able to watch educational videos and explore educational content whenever and wherever they want, and teachers should be able to create educational content and adjust lecture time whenever and wherever they want. The online NIPE platform generates a lot of information. Big education data can analyze and mine all kinds of information hidden behind the data based on massive data information about individual students, making it more targeted and personalized. Individual students generate individual information sources in educational big data as part of the normal learning process. Educators can use real-time data collection and analysis to guide their teaching methods in a timely and targeted manner.The Apriori algorithm must scan the whole database when finding a set of candidate frequencies, and with the increase of the number of sets of 1 frequency entries, the number of sets of 2 candidates increases linearly, which leads to excessive I/O load and high efficiency. The parallel Apriori algorithm based on the Hadoop platform can effectively make up for these shortcomings by using distributed clusters.LetX=x1,x2,⋯,xn be the set of data points to be clustered, and k be the number of categories to be divided. The K-means algorithm first randomly selects k data points from X as the initial cluster, then calculates the distance between data points and cluster centroid, assigns the data points to the nearest centroid, recalculates the centroid after obtaining a new partition, and repeats the above process until the reference function converges.Here, the general definition of the criterion function is as follows:(1)E=∑i=1k∑x∈Cix−vi2.Among them,Ci represents the set of data points belonging to class i, vi is the middle point of class i, and ⋅ represents the norm, which is a distance calculation method, and the Euclidean distance is usually used.If the centroids of two clusters are not the closest but have similar density distribution, they are considered to be merged. If two clusters with similar density distribution are too far apart, they should not be merged. Based on this idea, considering distance and data distribution, this study proposes a new method to merge local clustering results.For the cluster centerVnew=V1new,V2new,⋯,VN×knew obtained by the local clustering stage, the Euclidean distance is used to measure the difference between the two cluster centers in the distance.Define the difference between two classesVinew,Vjnew, see the following formula:
(2)DIS=Vinew,Vjnew=1−βDVinew,Vjnew+βDKLVinew,Vjnew.In the above formula,β represents the weight coefficient, which represents the influence coefficient of distance difference and distribution difference on the difference between two classes. The following formula gives the definition of β:
(3)β=maxCVi,CVjCVi+CVj,whereCVi represents the set of data points belonging to class i in the global dataset. According to the definition of slice coefficient, when there is a big difference between two classes, the influence of distribution difference on the two classes is mainly considered.According to the core idea of the Apriori algorithm, a set of 1_frequent items must be mined first, and parallel mining of 1_frequent items in Map/Reduce is equivalent to processing the number of words of one item in each transaction. Therefore, in the Map operation, the output key-value pair of the map can be changed to use the previousk−2 item as the key and k−1item as the value. The first k−2 entries and k−1 entries.Using the existing Apriori algorithm, actionable information can be found by fully mining a set of frequent items, but if we analyze the dataset generated by users clicking online courses from the training data, as you can see, the number of times a user clicks on an online course is a number. The traditional Apriori algorithm must be a set of Boolean data when mining association rules, and the numerical data must be processed to adapt to the algorithm.Before association mining, the FT (frequent tree) algorithm is similar to the Apriori algorithm, which realizes the setting of a minimum support and selects a group of frequent items according to this support. The core idea of the algorithm is to construct an FT based on a group of items and mark the FT with relevant information. It scans the database once, mining FT from bottom to top, and deleting the child nodes of FT to generate the required frequency set.If the new data isN, the data to be processed is O and the standard deviation is K; then, the formula for z-score standardization is
(4)N=O−O¯K.The processed data can better reflect users’ clicks on online courses, thus making the mining of link rules more accurate.Finally, the generated key-value pair is used as the input of Reduce, in which the Reduce operation combines two elements of the array in key and value to generate a set of k_candidate items. For example, if there are 6 groups of 3_frequencies, the process of grouping them to generate 4_candidate 3_frequent set is shown in Figure2.Figure 2
Candidate frequency set mining.


## 3.3. NIPE Achievement Forecast

The classifier predicts the students’ recent performance, and the classifier predicts the students’ future performance according to the data. The classifier model calculates the probability that samples fall into another category and assigns the most likely category to the predicted category. The selection of features will also affect the final classification results. Therefore, in this chapter, how to choose students’ characteristics and how to define students’ categories are the first issues to be considered.This paper adopts the supervised learning method as the basic model of a classifier. The task of supervising the classifier is to learn how to make the classification model better predict the category of a given sample for classification. In this paper, based on the two existing models, this study proposes a new NB_BPNN model, which effectively combines the advantages of two existing models.Let the input spaceδ∈RK be the set of all K-dimensional feature vectors, the category set of the output space β∈c1,c2,⋯,cm, the input feature vector v∈δ, and the output category y∈β, v are random variables defined on δ, Y is the random variable defined on β, and PV,Y is the joint probability distribution of V,Y. Assume that the training dataset is
(5)T=v1,y1,v2,y2,⋯,vN,yN.Let the possible value ofith feature vi be si1,⋯,sil,⋯, then the maximum likelihood estimation of conditional probability PVi=silY=cj is
(6)PVi=silY=cj=∑n=1NIvni=sil,yn=cj∑n=1Nyn=cj,i=1,2,⋯,K,j=1,2,⋯,m.In the formula,vni is the ith feature of the nth training sample; sil is the lth possible value of the ith feature, and I is the indicator function of classification.Where in order to ensure that the probability sum is still 1, a term ofm×1 is added to the denominator, where m is the total number of all possible values of this dimension feature. Similarly, the estimation result of prior probability is deformed as follows:
(7)PY=cj=∑n=1NIyn=cj+1N+m×1.Type, in order to ensure that the probability sum is still 1, the denominator adds a term ofm×1.In this chapter, we put forward the NB_BPNN model to predict students’ future learning outcomes. In this structure, we first extract and transform nonlinear features through multilayer BPNN and then send the output of neural network to NB classifier to get the corresponding classification. The left and right parts of the model are treated in the same way as the traditional neural network or NB model, which is an improved model combining NB and BPNN. The structure is shown in Figure3.Figure 3
NP_BPNN model structure.The specific calculation method is to modify the weights of NB by loss first, then find the partial derivative of loss to the connection vector, and finally modify the partial derivative of the ownership value of neural network by gradient descent. In the testing stage, every time the characteristics of the test sample are input, the intermediate output is obtained through several layers of networks, and the intermediate output is input into the NB classifier, and the classification is judged according to the output.The AP (Affinity Propagation) algorithm proposes an effective method to find out the center point ofK, and it can satisfy the condition that the sum of the above similarities is the minimum. Let σi represent the center point closest to the data point ei, then the goal of the algorithm is to find the most suitable set of σ, so as to maximize Eσ as defined in the following:
(8)Eσ=∑i=1NSei,eσi−∑i=1NXiσ.When the Euclidean distance is used as the similarity between points,Sei,ej is defined as −di,j2. When i=j, Sei,ej is called the reference degree of data point i, which is recorded as Pi. The larger the value of Pi, the more likely the data point i is to become the cluster representative point.The WAP (Weighted AP) algorithm is a generalization of the AP algorithm, which is suitable for clustering complex multidata sets. The WAP algorithm can merge continuous points in large datasets and make the clustering process more efficient without changing the clustering results.The definition datasetξ′=ei,ni contains ni data items ei, of which i=1,⋯,L. Then, the similarity matrix of dataset ξ′ is defined as follows:
(9)S′=ei,ej=−nidi,j2,i≠j,s∗+ni−1×εi,εi≥0.Solve the problem as the center pointσ of the above dataset ξ′, that is, solve
(10)E′σ=∑i=1LS′ei,eσi−∑i=1LXiσ.The maximum optimization problem, if the datasetξ consists of ni data items ei=i=1,⋯,L and εi=0, is equivalent to the optimization problem.Therefore, this paper uses the data processing mechanism of the Hadoop platform and the divide-and-conquer idea of the HI_WAP algorithm to design the P_WAP (Parallel WAP) algorithm.Assuming the size of the original datasetξ is N, we can see from Figure 4 that the basic idea of the P_WAP algorithm is as follows:Figure 4
P_WAP algorithm.Distributing a subset of data nodes with similarK performance of Hadoop, this balanced job scheduling and processing mechanism is the unique advantage of the Hadoop platform. The clustering results are stored on the local disk for subsequent processing. If the amount of data is very large, the number of cluster representative points generated in the WAP stage is very large. At this time, WAP clustering can be carried out for many times to obtain enough clustering results. The obtained clustering results are reduced by the reduce function, so that the same data representative points are clustered into the same cluster, and finally, the results are written into the output file.


## 4. Experiment and Results

### 4.1. Setting Up Experiment

Hadoop is a distributed big data processing framework for cloud computing. HDFS and MapReduce are two of the most important modules in the system. MapReduce is a parallel computing mode that uses HDFS as its base file system. Hadoop has few machine configuration requirements, and the cost of constructing a small Hadoop cluster is low. It is ideal for small businesses looking to set up their own data center, and the cluster can scale up as data volumes grow. As a result, this paper creates an experimental cloud computing platform and tests the clustering effect of an algorithm using the open source distributed software Hadoop. The experimental platform consists of five general-purpose PCs and one router, and the cluster software configuration is shown in Table1.Table 1
Cluster configuration.
SoftwareVersionLinuxUbuntu 9.11HadoopHadoop-0.20.2JDKJava version1.7.0_71SSHOpenSSH 5.6The main computing tasks of cluster are completed by TaskTracker. In order to realize the benefits of local data reading and local computing, it is best to run TaskTracker on a DataNode. This improves the efficiency of running jobs on the cluster.


### 4.2. Result Analysis

As can be seen from Figure5, compared with the FT algorithm, the Apriori algorithm exponentially increases the time needed to find a group of items. The FT algorithm is very effective because it only needs to scan the dataset twice, and the project information is stored in a tree structure.Figure 5
Algorithm efficiency comparison.When the improved association rule algorithm is used to test the performance of the algorithm on datasets with less than 2000 entries in parallel environment, it can be seen that the execution time of datasets with 2000 entries is longer than that of a single computer. The reason is that the parallel environment is suitable for big data processing. When the amount of data is too small, the execution time of the algorithm is no longer the benefit of multinode block operation. The execution time is mainly determined by data transmission and replication.Figure6 shows the performance analysis of the improved parallel association rule mining algorithm in parallel environment, with 2000 datasets.Figure 6
Efficiency comparison of parallel environment algorithms.From the analysis of Figure6, it can be seen that in the parallel environment, when the minimum number of supports is small, the running speed of two nodes is faster than that of a single node. This is because if the node and the single node are equal, when the minimum number of support reaches 4, the number of support for most projects is less than 4, so they are eliminated, that is, there are fewer key-value pairs and fewer factors. It is no longer multinode computing that affects the execution time of the algorithm, but data transmission, replication, and time consumption.The number of input nodes is 6, the number of output nodes is 6, the hidden layer can be [1,2,3] layers, the number of hidden layer nodes is [32,64], the training stop condition is iterated continuously for 10 times, and the loss variance is less than 0.05. The results are shown in Table2 and Figure 7.Table 2
Classification performance of NB_BPNN.
50% off cross-validationAccuracyRecallF110.930.910.9220.910.870.8830.920.880.9140.870.940.9150.910.920.91Figure 7
Classification performance of NB_BPNN.It can be seen that the performance of the model is improved by increasing the number of hidden layer nodes to 64. It shows that the performance of the model can also be improved by appropriately increasing the number of hidden layer nodes. NB_BPNN successfully combines the advantages of two classifiers and is an effective classifier. Using the above configuration, we built an independent pseudo-distributed environment on the node and ran the algorithm on three datasets 1, 2, and 3, respectively. In the first stage of the experiment, 100 data were AP clustered at a time, and the experimental results are shown in Figure8.Figure 8
Algorithm performance comparison.As shown in Figure8, when clustering dataset 1 with the same hardware configuration, the HI WAP algorithm takes less time to execute than the P WAP algorithm. The execution time of the P WAP algorithm and the HI WAP algorithm increases as the amount of input data increases, but the increase of the P WAP algorithm is less than that of the HI WAP algorithm, and the increase rate remains stable. When dealing with large amounts of data, the P WAP algorithm outperforms the HI WAP algorithm. In this experiment, we run the P WAP algorithm in a distributed environment cluster with 1 to 8 DataNodes and compare the execution time of P WAP in clusters of various sizes using pseudo-distributed environment calculation results. The experimental results shown in Figure 9 were obtained.Figure 9
The execution time of the algorithm.As shown in Figure9, the execution time of the algorithm decreases linearly with the increase of the number of DataNode in the cluster. From the experimental results of dataset 1, it can be seen that when a Hadoop cluster processes a dataset, it has the most effective number of nodes for processing a certain scale dataset, and increasing the number of nodes does not necessarily shorten the dataset. This is because with the increase of the number of cluster nodes, the resource consumption required to run the cluster itself will also increase. Therefore, when dealing with big data, it is necessary to choose a suitable Hadoop cluster to deal with datasets.


## 4.1. Setting Up Experiment

Hadoop is a distributed big data processing framework for cloud computing. HDFS and MapReduce are two of the most important modules in the system. MapReduce is a parallel computing mode that uses HDFS as its base file system. Hadoop has few machine configuration requirements, and the cost of constructing a small Hadoop cluster is low. It is ideal for small businesses looking to set up their own data center, and the cluster can scale up as data volumes grow. As a result, this paper creates an experimental cloud computing platform and tests the clustering effect of an algorithm using the open source distributed software Hadoop. The experimental platform consists of five general-purpose PCs and one router, and the cluster software configuration is shown in Table1.Table 1
Cluster configuration.
SoftwareVersionLinuxUbuntu 9.11HadoopHadoop-0.20.2JDKJava version1.7.0_71SSHOpenSSH 5.6The main computing tasks of cluster are completed by TaskTracker. In order to realize the benefits of local data reading and local computing, it is best to run TaskTracker on a DataNode. This improves the efficiency of running jobs on the cluster.


## 4.2. Result Analysis

As can be seen from Figure5, compared with the FT algorithm, the Apriori algorithm exponentially increases the time needed to find a group of items. The FT algorithm is very effective because it only needs to scan the dataset twice, and the project information is stored in a tree structure.Figure 5
Algorithm efficiency comparison.When the improved association rule algorithm is used to test the performance of the algorithm on datasets with less than 2000 entries in parallel environment, it can be seen that the execution time of datasets with 2000 entries is longer than that of a single computer. The reason is that the parallel environment is suitable for big data processing. When the amount of data is too small, the execution time of the algorithm is no longer the benefit of multinode block operation. The execution time is mainly determined by data transmission and replication.Figure6 shows the performance analysis of the improved parallel association rule mining algorithm in parallel environment, with 2000 datasets.Figure 6
Efficiency comparison of parallel environment algorithms.From the analysis of Figure6, it can be seen that in the parallel environment, when the minimum number of supports is small, the running speed of two nodes is faster than that of a single node. This is because if the node and the single node are equal, when the minimum number of support reaches 4, the number of support for most projects is less than 4, so they are eliminated, that is, there are fewer key-value pairs and fewer factors. It is no longer multinode computing that affects the execution time of the algorithm, but data transmission, replication, and time consumption.The number of input nodes is 6, the number of output nodes is 6, the hidden layer can be [1,2,3] layers, the number of hidden layer nodes is [32,64], the training stop condition is iterated continuously for 10 times, and the loss variance is less than 0.05. The results are shown in Table2 and Figure 7.Table 2
Classification performance of NB_BPNN.
50% off cross-validationAccuracyRecallF110.930.910.9220.910.870.8830.920.880.9140.870.940.9150.910.920.91Figure 7
Classification performance of NB_BPNN.It can be seen that the performance of the model is improved by increasing the number of hidden layer nodes to 64. It shows that the performance of the model can also be improved by appropriately increasing the number of hidden layer nodes. NB_BPNN successfully combines the advantages of two classifiers and is an effective classifier. Using the above configuration, we built an independent pseudo-distributed environment on the node and ran the algorithm on three datasets 1, 2, and 3, respectively. In the first stage of the experiment, 100 data were AP clustered at a time, and the experimental results are shown in Figure8.Figure 8
Algorithm performance comparison.As shown in Figure8, when clustering dataset 1 with the same hardware configuration, the HI WAP algorithm takes less time to execute than the P WAP algorithm. The execution time of the P WAP algorithm and the HI WAP algorithm increases as the amount of input data increases, but the increase of the P WAP algorithm is less than that of the HI WAP algorithm, and the increase rate remains stable. When dealing with large amounts of data, the P WAP algorithm outperforms the HI WAP algorithm. In this experiment, we run the P WAP algorithm in a distributed environment cluster with 1 to 8 DataNodes and compare the execution time of P WAP in clusters of various sizes using pseudo-distributed environment calculation results. The experimental results shown in Figure 9 were obtained.Figure 9
The execution time of the algorithm.As shown in Figure9, the execution time of the algorithm decreases linearly with the increase of the number of DataNode in the cluster. From the experimental results of dataset 1, it can be seen that when a Hadoop cluster processes a dataset, it has the most effective number of nodes for processing a certain scale dataset, and increasing the number of nodes does not necessarily shorten the dataset. This is because with the increase of the number of cluster nodes, the resource consumption required to run the cluster itself will also increase. Therefore, when dealing with big data, it is necessary to choose a suitable Hadoop cluster to deal with datasets.


## 5. Conclusions

The interpretation and construction of the university NIPE model in the era of big data will promote the qualitative improvement of NIPE. The value of big data lies in that it can only be used for work if it reflects the overall situation and effectively analyzes and studies the laws it contains. In this environment, the NIPEDM service platform developed in this paper can meet this purpose. The classifier model is applied to the mapping of students’ behavior information and the prediction of students’ scores. Two methods, NB classifier and neural network classifier, are used to test. The advantages and disadvantages are analyzed, and a new method, NB_BPNN model, which combines the advantages of both methods is proposed. The model can effectively classify and predict students’ future learning outcomes according to their learning behaviors and habits. In this paper, the experimental dataset of the P_WAP algorithm is homologous text data, and the actual application environment is often multisource heterogeneous data. How to deal with multisource heterogeneous data is also an important topic for future research.



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*Source: 7609697-2022-04-22.xml*