# Construction of Data Resource Sharing Platform for College Students’ Ideological and Political Education Based on Constraint Clustering

Authors: Jiaoyan Cheng
Journal: Wireless Communications and Mobile Computing
 (2022)

---

## Abstract

Online learning communities have changed the “silo” learning structure of traditional online learning and provided an effective support environment for wisdom sharing and collaborative knowledge building. The construction of a good online learning community has become a core issue in the field of online learning research. The study abstracts the construction of online learning community as a constrained clustering problem and proposes an intelligent construction method for online learning community based on constrained clustering. In particular, the three principles include the combination of limited freedom of choice and continuous iterative improvement, the combination of openness and standardization of the sharing platform, and the combination of public welfare and paid attributes of data resources. This paper discusses how to share valuable basic statistical data and social survey data in the field of college students’ ideological and political education (IPE) and make them public to the whole society by building a data resource sharing platform, so as to improve the utilization of data on the one hand and support comparative research topics in related fields through statistical analysis among survey data on the other hand. The experimental results show that the proportion of low-level posts (KC1 and KC2) of the two groups of students is relatively high (79.4% and 76.5%, respectively), indicating that the students in the learning community have carried out a lot of knowledge sharing and discussed and compared them to a certain extent. The proportion of high-level posts by students in both groups is very small (20.8% and 23.5%, respectively), but the number of high-level (KC3, KC4) posts by the students in the experimental group is significantly higher than that of the students in the control group, indicating that the students in the experimental group have good exchanges and discussions have resulted in meaningful negotiation and testing on some issues.


---

## Body

## 1. Introduction

Teachers and students are increasingly appealing for the online learning model. However, with the rapid development of online learning, the problems of less interaction, strong loneliness, and low intelligence sharing among learners have become increasingly prominent, which has seriously affected the improvement of online learning quality. The birth of the online learning community has changed the “isolated island” learning structure of the traditional online learning space, emphasized the diversified interaction among learners, realized interpersonal psychological compatibility and communication, and built a smart collaborative learning environment for online learning [1]. The valuable social survey data obtained by spending a huge amount of human, material, and financial resources are only used within the scope of the research group and are not made public to the society. Therefore, the utilization rate of many social survey data is very low, which virtually wastes the valuable resources of the society. Due to the limitations of technology and conditions, the IPE resources in universities are relatively scattered.Constraint clustering can comprehensively provide analysis materials to quantify the thoughts and behaviors of the group. Use new technology to develop new resources for IPE to dig deep into old resources and develop new ones. With the development of the Internet + era and the continuous innovation of teaching methods, it is imperative to find new teaching resources in order to ensure the smooth implementation of IPE. For on-campus resources, we need to dig deeper into teaching materials, resources of teachers and students on campus, and campus culture resources; for family resources, we need to dig deeper into family atmosphere and parents’ influence; for off-campus resources, we need to dig deeper into material and spiritual resources of domestic universities and communities; for traditional culture resources, we need to dig deeper into the essence of traditional culture; for online media resources, we need to dig deeper into the latest and deepest information resources; for other resources, we need to dig deeper into the physical environment, natural environment, and human environment [2].Making use of this advantage can increase the attraction and appeal of IPE to college students. The network can realize the socialization of IPE in universities to the greatest extent. The IPE of universities can be connected with families and society through the network, which provides convenience for the teachers of the whole university to participate in the IPE of college students and teach and educate people and all sectors of society to participate in the IPE of college students and is also conducive to the formation of the joint force of the IPE of college students. In this paper, learners are clustered with constraints based on learning styles to achieve heterogeneous balanced grouping based on learning styles. The research results are verified and applied, and the impact of intelligent construction methods on learners’ knowledge construction level and learning performance is analyzed. Its innovation lies in the following:(1)
With the help of real-time information processing and analysis of clustering technology, it is possible to comprehensively, timely, and dynamically grasp the ideological information of IPE group objects(2)
Clustering technology the technology of IPE is different from ordinary technology. It is derived from the whole process and immediate behavior records, and it is a “three-dimensional” description of people’s thoughts and behaviors. Educating people provides the technical foundation and supportThis paper studies the problem of constructing a data sharing platform for IPE of college students based on constrained clustering, with the following structure.Section1 is the introductory part. This part mainly elaborates the research background and research significance of the construction of data resource sharing platform for IPE of college students based on constraint clustering and puts forward the research purpose, method, and innovation of this paper. Section 2 is mainly a review of related literature, summarizing the advantages and shortcomings of it and putting forward the research ideas of this paper. Section 3 is the methodological part, focusing on the combination of constrained clustering and the construction of data sharing platform for college students’ IPE data. Section 4 is the experimental analysis part. This part conducts experimental validation in the data set to analyze the performance of the model. Section 6 is the conclusion and outlook. This part mainly reviews the main contents and results of this paper’s research, summarizes the research conclusions, and points out the direction of further research.


## 2. Related Work

Śmieja and Wiercioch reduced the dependence ofk-means clustering results on initial object selection [3]. M. Śmieja adjusted the method of calculating the representative of each cluster in each iteration, which improved the performance of k-means, while Wiercioch proposed x-means on the basis of k-means, which accelerated the iterative process and improved efficiency. Association rules were first proposed by Śmieja and Wiercioch in customer transactions in 1993 [4]. And it has been continuously developed in the future and has played a very important role in e-commerce and other fields. The general understanding of association rules is that some connection or pattern is implied between two or more items. Śmieja and Wiercioch pointed out that online learning communities solve the problems of traditional learning communities such as temporal and spatial limitations and lack of asynchronous interaction and provide effective support for information exchange, teamwork, and wisdom sharing [5]. Cao et al. used mobile computing devices to support collaborative learning members to jointly perform English reading understanding in unfamiliar word annotations as well as short text translation, and teachers manually group learners according to their English reading ability [6]. Niknejad et al. pointed out that, compared to homogeneous grouping, for heterogeneous grouping, in addition to effectively alleviating the problems of little learner interaction and isolation, its internal differentiation helps learners within the community to help each other and complement each other’s strengths, while external equalization helps ensure the balanced development of each community and provides an effective method for mobilizing learning motivation [7]. Wu et al. pointed out that in the Kolb learning style, the classification model analyzes learners from the perspective of the learning process and can point out the behavioral performance of learners in the learning process, which helps to understand the essence of learning and helps to design and organize the curriculum of learning communities so as to guide learning and grasp the laws of learning. For this reason, the Kolb model of learning styles was chosen as the basis of this study [8].This paper firstly clarifies the importance and urgency of doing a good job in the IPE of college students in the modern network environment. Based on this, in view of the current problems of lagging information feedback, imperfect sharing platform, and weak risk management and control capabilities in the current IPE work in universities, this paper develops a data resource sharing platform construction system for college students’ IPE based on constraint clustering. Finally, the model performance is simulated and verified. The analysis results show that the construction of a data resource sharing platform for college students’ IPE based on constraint clustering has achieved good results. The proposed model can realize the construction of a comprehensive data resource sharing platform for college students’ IPE based on constraint clustering, which has obvious advantages.


## 3. Basis of Work

### 3.1. Basic Concepts of Cluster Analysis

Clustering is the process of grouping a collection of physical or abstract objects into classes of similar objects. In the process of clustering, there is no prior knowledge about classification and no teacher’s guidance, and only the similarity between things is used as the criterion for classification, so in the field of machine learning, clustering belongs to the category of unsupervised classification. The class generated by clustering is a collection of a set of data objects; the objects in the same class are similar to each other and are quite different from the objects in other classes [9].


### 3.2. Overview of Basic Clustering Methods

The history of clustering can be traced back to the 1950s. Therefore, there are a large number of clustering methods in the literature. Even on the same data set, different clustering methods will bring different divisions. Therefore, finding a suitable clustering method for the problem scenario can achieve twice the result with half the effort. According to the basic idea of clustering methods, they can be divided into four categories: partition methods, hierarchical methods, density based methods, and grid based methods. Next, these methods will be described in turn.


#### 3.2.1. Partitioning Methods

Given a collection ofn objects, the division method divides it into k parts, each part to represent a cluster, and needs to satisfy k≤n and ensure that each cluster contains at least one object [10]. In general, the basic partitioning method is a mutually exclusive partitioning; i.e., each object belongs to exactly one cluster. To achieve global optimality, it may be necessary to consider all possible cases, which is undoubtedly very time-consuming. Therefore, many applications use heuristics such as k-means and k-centroids, where k objects are initially selected as cluster representatives of each of the k clusters, followed by continuous iterations to obtain a new set of cluster representatives for each iteration and to improve the clustering results, thus gradually approximating the local optimal solution.(1)C-Mean Algorithm. In much of the literature, the C-mean algorithm is called k-mean algorithm, and C− mean algorithm is simple in idea, is easy to implement, converges quickly, runs fast, consumes little memory, can effectively handle large data sets, and is one of the most commonly used clustering algorithms.TheC-mean algorithm is based on the error sum of squares criterion, ifNiis the number of samples in theiclusterGi;miis the number of samples in the II cluster; andiis the mean value of the samples in cluster II, i.e.,
(1)mi=1NiΣy∈Giy.The sum of squares of the errors between the samples inGi and the mean mi is added to all classes as
(2)J=Σi=1cΣy∈Giy−mi2.(2)C-Mean Value Algorithm Flow.
(1)
Randomly selectC objects, each of which initially represents the mean of a class, i.e., the center(2)
For each remaining object, it is assigned to the nearest class according to its distance from the center of each class(3)
Based on the newly generatedC classes, the average value of each class is updated(4)
Repeat (2) and (3) until the mean value no longer changes or until the error sum of squares criterion function converges


#### 3.2.2. Hierarchical Methods

Hierarchical methods create a hierarchical decomposition of a given set of data objects. According to the way the hierarchy is generated, hierarchy methods can be divided into bottom-up aggregation hierarchy methods and top-down decomposition hierarchy methods. Aggregate hierarchy methods start by forming each object into a group and then merge similar groups layer by layer until some termination condition is met. The decomposition hierarchy approach, on the other hand, treats all objects as a group and then decomposes them until a final condition is satisfied, resulting in a group that is a cluster [11].


#### 3.2.3. FuzzyC-Mean Algorithm Process

The main difference between theFCM algorithm and the C mean algorithm is that it uses FCM fuzzy division, so that each given data point is determined by the membership degree between the value and 0 and 1 to determine the degree of its belonging to each class. FCM divides the vector n into xii=1,2,⋯,n fuzzy groups and finds the cluster center of each group, so that the objective function of the dissimilarity index can be minimized. Adapting to the introduction of fuzzy partitioning, membership matrix ∪ allows elements with values between 0 and 1. However, with the normalization provisions, the sum of the membership degrees of a data set always equals 1:
(3)Σi=1cuij=1,∀j=1,⋯,n.Then, theFCM objective function is defined as
(4)JU,c1,⋯,cc=∑i=1cJi=∑i=1c∑jnuijmdij2.Here,uij is between 0 and 1; ci is the clustering center of fuzzy group I; dij=ci=xj is the Euclidean distance between the ith clustering center and the jth data point; and m∈1,∞ is a weighted index. The difference of C mean algorithm is that the fuzzy weight index m is added to the objective function.The following new objective function is constructed to obtain the necessary conditions to make formula (4) reach the minimum value:
(5)J¯U,c1,⋯,cc,λ1,⋯,λn=JU,c1,⋯,cc+∑j=1nλj∑i=1cuij−1=∑i=1c∑jnuijmdij2+∑j=1nλj∑i=1cuij−1.The Lagrangian multipliers of the individual constraints of Equation (1) are mentioned here. The necessary conditions for minimizing Equation (1) by deriving all the input parameters are as follows:
(6)ci=∑j=1nuijmxj∑j=1nuijm,(7)uij=1∑k=1cdij/dkj2/m−1.From the above two necessary conditions, the fuzzyC-mean clustering algorithm is a simple iterative process [12]. When run in batch mode, the following steps are used to determine the clustering centers and the affiliation matrix:Step 1.
Initialize the affiliation matrixU with random numbers with values between 0 and 1 so that it satisfies the constraints in Equation (3).Step 2.
Calculate using Equation (6). c cluster centers ci, i=1,⋯,c.Step 3.
Calculate the value function according to Equation (7), if it is less than some determined threshold value or if it changes relative to the last value.
If it is less than a certain threshold or if it changes by less than a certain threshold relative to the last value, the algorithm stops.Step 4.
Compute the newU matrix using Equation (4) and return to Step 2.The above algorithm can also initialize the cluster centers first and then perform the iterative process. Since it is not guaranteed thatFCM converges on an optimal solution. The performance of the algorithm depends on the initial cluster centers. Therefore, we either use another fast algorithm to determine the initial cluster centers or start the algorithm with a different initial cluster center each time and run FCM multiple times.


## 3.1. Basic Concepts of Cluster Analysis

Clustering is the process of grouping a collection of physical or abstract objects into classes of similar objects. In the process of clustering, there is no prior knowledge about classification and no teacher’s guidance, and only the similarity between things is used as the criterion for classification, so in the field of machine learning, clustering belongs to the category of unsupervised classification. The class generated by clustering is a collection of a set of data objects; the objects in the same class are similar to each other and are quite different from the objects in other classes [9].


## 3.2. Overview of Basic Clustering Methods

The history of clustering can be traced back to the 1950s. Therefore, there are a large number of clustering methods in the literature. Even on the same data set, different clustering methods will bring different divisions. Therefore, finding a suitable clustering method for the problem scenario can achieve twice the result with half the effort. According to the basic idea of clustering methods, they can be divided into four categories: partition methods, hierarchical methods, density based methods, and grid based methods. Next, these methods will be described in turn.


### 3.2.1. Partitioning Methods

Given a collection ofn objects, the division method divides it into k parts, each part to represent a cluster, and needs to satisfy k≤n and ensure that each cluster contains at least one object [10]. In general, the basic partitioning method is a mutually exclusive partitioning; i.e., each object belongs to exactly one cluster. To achieve global optimality, it may be necessary to consider all possible cases, which is undoubtedly very time-consuming. Therefore, many applications use heuristics such as k-means and k-centroids, where k objects are initially selected as cluster representatives of each of the k clusters, followed by continuous iterations to obtain a new set of cluster representatives for each iteration and to improve the clustering results, thus gradually approximating the local optimal solution.(1)C-Mean Algorithm. In much of the literature, the C-mean algorithm is called k-mean algorithm, and C− mean algorithm is simple in idea, is easy to implement, converges quickly, runs fast, consumes little memory, can effectively handle large data sets, and is one of the most commonly used clustering algorithms.TheC-mean algorithm is based on the error sum of squares criterion, ifNiis the number of samples in theiclusterGi;miis the number of samples in the II cluster; andiis the mean value of the samples in cluster II, i.e.,
(1)mi=1NiΣy∈Giy.The sum of squares of the errors between the samples inGi and the mean mi is added to all classes as
(2)J=Σi=1cΣy∈Giy−mi2.(2)C-Mean Value Algorithm Flow.
(1)
Randomly selectC objects, each of which initially represents the mean of a class, i.e., the center(2)
For each remaining object, it is assigned to the nearest class according to its distance from the center of each class(3)
Based on the newly generatedC classes, the average value of each class is updated(4)
Repeat (2) and (3) until the mean value no longer changes or until the error sum of squares criterion function converges


### 3.2.2. Hierarchical Methods

Hierarchical methods create a hierarchical decomposition of a given set of data objects. According to the way the hierarchy is generated, hierarchy methods can be divided into bottom-up aggregation hierarchy methods and top-down decomposition hierarchy methods. Aggregate hierarchy methods start by forming each object into a group and then merge similar groups layer by layer until some termination condition is met. The decomposition hierarchy approach, on the other hand, treats all objects as a group and then decomposes them until a final condition is satisfied, resulting in a group that is a cluster [11].


### 3.2.3. FuzzyC-Mean Algorithm Process

The main difference between theFCM algorithm and the C mean algorithm is that it uses FCM fuzzy division, so that each given data point is determined by the membership degree between the value and 0 and 1 to determine the degree of its belonging to each class. FCM divides the vector n into xii=1,2,⋯,n fuzzy groups and finds the cluster center of each group, so that the objective function of the dissimilarity index can be minimized. Adapting to the introduction of fuzzy partitioning, membership matrix ∪ allows elements with values between 0 and 1. However, with the normalization provisions, the sum of the membership degrees of a data set always equals 1:
(3)Σi=1cuij=1,∀j=1,⋯,n.Then, theFCM objective function is defined as
(4)JU,c1,⋯,cc=∑i=1cJi=∑i=1c∑jnuijmdij2.Here,uij is between 0 and 1; ci is the clustering center of fuzzy group I; dij=ci=xj is the Euclidean distance between the ith clustering center and the jth data point; and m∈1,∞ is a weighted index. The difference of C mean algorithm is that the fuzzy weight index m is added to the objective function.The following new objective function is constructed to obtain the necessary conditions to make formula (4) reach the minimum value:
(5)J¯U,c1,⋯,cc,λ1,⋯,λn=JU,c1,⋯,cc+∑j=1nλj∑i=1cuij−1=∑i=1c∑jnuijmdij2+∑j=1nλj∑i=1cuij−1.The Lagrangian multipliers of the individual constraints of Equation (1) are mentioned here. The necessary conditions for minimizing Equation (1) by deriving all the input parameters are as follows:
(6)ci=∑j=1nuijmxj∑j=1nuijm,(7)uij=1∑k=1cdij/dkj2/m−1.From the above two necessary conditions, the fuzzyC-mean clustering algorithm is a simple iterative process [12]. When run in batch mode, the following steps are used to determine the clustering centers and the affiliation matrix:Step 1.
Initialize the affiliation matrixU with random numbers with values between 0 and 1 so that it satisfies the constraints in Equation (3).Step 2.
Calculate using Equation (6). c cluster centers ci, i=1,⋯,c.Step 3.
Calculate the value function according to Equation (7), if it is less than some determined threshold value or if it changes relative to the last value.
If it is less than a certain threshold or if it changes by less than a certain threshold relative to the last value, the algorithm stops.Step 4.
Compute the newU matrix using Equation (4) and return to Step 2.The above algorithm can also initialize the cluster centers first and then perform the iterative process. Since it is not guaranteed thatFCM converges on an optimal solution. The performance of the algorithm depends on the initial cluster centers. Therefore, we either use another fast algorithm to determine the initial cluster centers or start the algorithm with a different initial cluster center each time and run FCM multiple times.


## 3.2.1. Partitioning Methods

Given a collection ofn objects, the division method divides it into k parts, each part to represent a cluster, and needs to satisfy k≤n and ensure that each cluster contains at least one object [10]. In general, the basic partitioning method is a mutually exclusive partitioning; i.e., each object belongs to exactly one cluster. To achieve global optimality, it may be necessary to consider all possible cases, which is undoubtedly very time-consuming. Therefore, many applications use heuristics such as k-means and k-centroids, where k objects are initially selected as cluster representatives of each of the k clusters, followed by continuous iterations to obtain a new set of cluster representatives for each iteration and to improve the clustering results, thus gradually approximating the local optimal solution.(1)C-Mean Algorithm. In much of the literature, the C-mean algorithm is called k-mean algorithm, and C− mean algorithm is simple in idea, is easy to implement, converges quickly, runs fast, consumes little memory, can effectively handle large data sets, and is one of the most commonly used clustering algorithms.TheC-mean algorithm is based on the error sum of squares criterion, ifNiis the number of samples in theiclusterGi;miis the number of samples in the II cluster; andiis the mean value of the samples in cluster II, i.e.,
(1)mi=1NiΣy∈Giy.The sum of squares of the errors between the samples inGi and the mean mi is added to all classes as
(2)J=Σi=1cΣy∈Giy−mi2.(2)C-Mean Value Algorithm Flow.
(1)
Randomly selectC objects, each of which initially represents the mean of a class, i.e., the center(2)
For each remaining object, it is assigned to the nearest class according to its distance from the center of each class(3)
Based on the newly generatedC classes, the average value of each class is updated(4)
Repeat (2) and (3) until the mean value no longer changes or until the error sum of squares criterion function converges


## 3.2.2. Hierarchical Methods

Hierarchical methods create a hierarchical decomposition of a given set of data objects. According to the way the hierarchy is generated, hierarchy methods can be divided into bottom-up aggregation hierarchy methods and top-down decomposition hierarchy methods. Aggregate hierarchy methods start by forming each object into a group and then merge similar groups layer by layer until some termination condition is met. The decomposition hierarchy approach, on the other hand, treats all objects as a group and then decomposes them until a final condition is satisfied, resulting in a group that is a cluster [11].


## 3.2.3. FuzzyC-Mean Algorithm Process

The main difference between theFCM algorithm and the C mean algorithm is that it uses FCM fuzzy division, so that each given data point is determined by the membership degree between the value and 0 and 1 to determine the degree of its belonging to each class. FCM divides the vector n into xii=1,2,⋯,n fuzzy groups and finds the cluster center of each group, so that the objective function of the dissimilarity index can be minimized. Adapting to the introduction of fuzzy partitioning, membership matrix ∪ allows elements with values between 0 and 1. However, with the normalization provisions, the sum of the membership degrees of a data set always equals 1:
(3)Σi=1cuij=1,∀j=1,⋯,n.Then, theFCM objective function is defined as
(4)JU,c1,⋯,cc=∑i=1cJi=∑i=1c∑jnuijmdij2.Here,uij is between 0 and 1; ci is the clustering center of fuzzy group I; dij=ci=xj is the Euclidean distance between the ith clustering center and the jth data point; and m∈1,∞ is a weighted index. The difference of C mean algorithm is that the fuzzy weight index m is added to the objective function.The following new objective function is constructed to obtain the necessary conditions to make formula (4) reach the minimum value:
(5)J¯U,c1,⋯,cc,λ1,⋯,λn=JU,c1,⋯,cc+∑j=1nλj∑i=1cuij−1=∑i=1c∑jnuijmdij2+∑j=1nλj∑i=1cuij−1.The Lagrangian multipliers of the individual constraints of Equation (1) are mentioned here. The necessary conditions for minimizing Equation (1) by deriving all the input parameters are as follows:
(6)ci=∑j=1nuijmxj∑j=1nuijm,(7)uij=1∑k=1cdij/dkj2/m−1.From the above two necessary conditions, the fuzzyC-mean clustering algorithm is a simple iterative process [12]. When run in batch mode, the following steps are used to determine the clustering centers and the affiliation matrix:Step 1.
Initialize the affiliation matrixU with random numbers with values between 0 and 1 so that it satisfies the constraints in Equation (3).Step 2.
Calculate using Equation (6). c cluster centers ci, i=1,⋯,c.Step 3.
Calculate the value function according to Equation (7), if it is less than some determined threshold value or if it changes relative to the last value.
If it is less than a certain threshold or if it changes by less than a certain threshold relative to the last value, the algorithm stops.Step 4.
Compute the newU matrix using Equation (4) and return to Step 2.The above algorithm can also initialize the cluster centers first and then perform the iterative process. Since it is not guaranteed thatFCM converges on an optimal solution. The performance of the algorithm depends on the initial cluster centers. Therefore, we either use another fast algorithm to determine the initial cluster centers or start the algorithm with a different initial cluster center each time and run FCM multiple times.


## 4. Design of a Personalized Recommendation System for the Data Resource Sharing Platform of College Students’ IPE Based on Constraint Clustering

### 4.1. System Architecture

The core work of collaborative filtering personalized recommendation technology is twofold: one is to build a user model, i.e., to represent the user’s needs in a form that can be recognized by the computer; the other is to calculate the similarity between users, i.e., to determine the set of nearest neighbors of the target user. In this paper, we propose a collaborative filtering personalized recommendation system based on user clustering by first building a spatial vector model for all users through the user model module and then clustering all users, so that users with similar resource preferences are clustered in one class. Finally, based on the resource preferences of the nearest neighbor set, the recommended resource information is generated to the target user [13]. The architecture of the system is shown in Figure 1.Figure 1
System architecture.The architecture divides the system into six modules:(1)
Database management module. This module is responsible for the storage and management of relevant data of educational resources and users, mainly the storage and management of original data and transformed data, and is convenient for program access and use(2)
Data cleaning module. This module is responsible for extracting the data that can reflect the user’s interest from the database and carrying out corresponding optimization processing to preprocess the user model module for the user module operation(3)
User model module. This module is responsible for the generation and update of user model similar user clustering module(4)
Similar user clustering module. This module divides the whole user space into several similar user cluster recommendation engine modules(5)
Recommended engine modules. According to the implementation principle of collaborative filtering recommendation technology, this module constructs the recommendation engine of the recommendation system. The main operations are to determine the nearest neighbor set of the target user and generate the user interface module of recommendation resources(6)
User interface module. This module realizes the online interaction platform between the target user and the system, obtains the user feedback information through this platform, and presents the recommended resources to the user. Figure2 shows the class diagram of main classes in the systemFigure 2
The class diagram of the core classes in the system.


### 4.2. Constraint Clustering-Based Learning Community Intelligent Construction Algorithm

In order to perform heterogeneous clustering and control the cluster size, the study proposes an intelligent construction algorithm for learning communities based on constrained clustering based on a combination of mean drift algorithm and improved classical hierarchical clustering algorithm, and the core work of the algorithm includes preliminary similarity clustering, creating initial learning communities, and hierarchical aggregation [14]. The basic idea of the algorithm is as follows: firstly, initial similarity clustering of learners using the classical mean drift algorithm; then, K initial learning communities are created by selecting the K most similar learners from the maximum similarity clusters (these K learners have similar styles and will not and should not be grouped into the same community); secondly, according to the similarity between learning styles, a modified hierarchical clustering method is used to merge each community in turn with a heterogeneous member; repeat Step 3 until all learners are merged to complete the intelligent construction of learning communities. The details are as follows: initial similarity clustering. The study uses the mean drift algorithm to cluster learners with similar styles. The mean drift algorithm is a nonparametric method based on density gradient ascent, which finds the target position and achieves clustering through iterative operations, with the advantages that the algorithm is computationally small, real-time, and suitable for situations where the number of clusters is uncertain. The basic idea of the algorithm is shown in Figure 3: given the threshold R (the average distance between all nodes is taken in the study), a point is randomly selected as the initial centroid (i.e., cluster center) cent. Find out all point sets n whose distance from centroid cent is less than R, and calculate new centroids based on these points. Repeat the previous step until the centroid does not change, and remember the center point (the “final centroid” in the figure). Repeat the above three steps until all points are classified; Finally, according to the access frequency of each class to each point, the class with the highest access frequency is the class of the node [15].Figure 3
Algorithm for constructing heterogeneous learning community.


### 4.3. Code of Conduct System

The code of conduct system is the cornerstone to ensure the good and effective operation of the platform. It is mainly used to regulate the research behavior of platform members (users) in relevant scientific research projects in line with the standards and requirements of the data resource sharing platform. It is to implement the above three systems into the system tools in the frame. Carry out online psychological counseling to provide assistance for the development of IPE for college students. Mental health education is an important means to improve and develop personality, and it is a new topic faced by IPE. The characteristics of openness, interaction, and concealment of network information provide a good platform for the realization of online mental health education. Universities should actively use the campus network platform to publish the knowledge of mental health, the common psychological problems of college students at different stages, the performance characteristics of psychological disorders, and self-diagnosis methods on the Internet, so as to open up new ways for college students to obtain knowledge of mental health; scientifically provide psychological evaluation system, identify the mental health index of college students, and help college students to evaluate themselves scientifically and correctly, so as to understand themselves more accurately; systematically understand the psychological characteristics of students, establish online psychological files of college students, and use online suggestions, conversations, and video recordings to inspire students to correctly understand the problem; rationally use appropriate methods to vent and relieve the psychological pressure of college students, so as to promote their personality to develop in a sound and perfect direction [16]. The establishment of a network information exchange platform provides a new idea for us to adapt to the ideological and political work of college students in the new era and improve the quality and efficiency of work. With the development of network information technology, timely absorption and creative application to the ideological and political work of college students will surely promote the ideological and political work of college students to achieve better results.


### 4.4. Pretreatment

The preprocessing function is used to preprocess the data set and judge the visibility and obstacle distance. The specific sequence is shown in Figure4. (1) The preprocessing of a university’s one-day student distribution data set in Shanghai is mainly to remove outliers and transform the coordinate system of the geographic information in the data set and store the preprocessed results in the database. The college students’ distribution data set obtained in this paper is stored in CSV format, in which each line represents a piece of college students’ relevant information, and each piece of college students’ information is separated by commas [17]. This formatted data set read-in processing is quite convenient.Figure 4
Sequence diagram of preprocessing.


## 4.1. System Architecture

The core work of collaborative filtering personalized recommendation technology is twofold: one is to build a user model, i.e., to represent the user’s needs in a form that can be recognized by the computer; the other is to calculate the similarity between users, i.e., to determine the set of nearest neighbors of the target user. In this paper, we propose a collaborative filtering personalized recommendation system based on user clustering by first building a spatial vector model for all users through the user model module and then clustering all users, so that users with similar resource preferences are clustered in one class. Finally, based on the resource preferences of the nearest neighbor set, the recommended resource information is generated to the target user [13]. The architecture of the system is shown in Figure 1.Figure 1
System architecture.The architecture divides the system into six modules:(1)
Database management module. This module is responsible for the storage and management of relevant data of educational resources and users, mainly the storage and management of original data and transformed data, and is convenient for program access and use(2)
Data cleaning module. This module is responsible for extracting the data that can reflect the user’s interest from the database and carrying out corresponding optimization processing to preprocess the user model module for the user module operation(3)
User model module. This module is responsible for the generation and update of user model similar user clustering module(4)
Similar user clustering module. This module divides the whole user space into several similar user cluster recommendation engine modules(5)
Recommended engine modules. According to the implementation principle of collaborative filtering recommendation technology, this module constructs the recommendation engine of the recommendation system. The main operations are to determine the nearest neighbor set of the target user and generate the user interface module of recommendation resources(6)
User interface module. This module realizes the online interaction platform between the target user and the system, obtains the user feedback information through this platform, and presents the recommended resources to the user. Figure2 shows the class diagram of main classes in the systemFigure 2
The class diagram of the core classes in the system.


## 4.2. Constraint Clustering-Based Learning Community Intelligent Construction Algorithm

In order to perform heterogeneous clustering and control the cluster size, the study proposes an intelligent construction algorithm for learning communities based on constrained clustering based on a combination of mean drift algorithm and improved classical hierarchical clustering algorithm, and the core work of the algorithm includes preliminary similarity clustering, creating initial learning communities, and hierarchical aggregation [14]. The basic idea of the algorithm is as follows: firstly, initial similarity clustering of learners using the classical mean drift algorithm; then, K initial learning communities are created by selecting the K most similar learners from the maximum similarity clusters (these K learners have similar styles and will not and should not be grouped into the same community); secondly, according to the similarity between learning styles, a modified hierarchical clustering method is used to merge each community in turn with a heterogeneous member; repeat Step 3 until all learners are merged to complete the intelligent construction of learning communities. The details are as follows: initial similarity clustering. The study uses the mean drift algorithm to cluster learners with similar styles. The mean drift algorithm is a nonparametric method based on density gradient ascent, which finds the target position and achieves clustering through iterative operations, with the advantages that the algorithm is computationally small, real-time, and suitable for situations where the number of clusters is uncertain. The basic idea of the algorithm is shown in Figure 3: given the threshold R (the average distance between all nodes is taken in the study), a point is randomly selected as the initial centroid (i.e., cluster center) cent. Find out all point sets n whose distance from centroid cent is less than R, and calculate new centroids based on these points. Repeat the previous step until the centroid does not change, and remember the center point (the “final centroid” in the figure). Repeat the above three steps until all points are classified; Finally, according to the access frequency of each class to each point, the class with the highest access frequency is the class of the node [15].Figure 3
Algorithm for constructing heterogeneous learning community.


## 4.3. Code of Conduct System

The code of conduct system is the cornerstone to ensure the good and effective operation of the platform. It is mainly used to regulate the research behavior of platform members (users) in relevant scientific research projects in line with the standards and requirements of the data resource sharing platform. It is to implement the above three systems into the system tools in the frame. Carry out online psychological counseling to provide assistance for the development of IPE for college students. Mental health education is an important means to improve and develop personality, and it is a new topic faced by IPE. The characteristics of openness, interaction, and concealment of network information provide a good platform for the realization of online mental health education. Universities should actively use the campus network platform to publish the knowledge of mental health, the common psychological problems of college students at different stages, the performance characteristics of psychological disorders, and self-diagnosis methods on the Internet, so as to open up new ways for college students to obtain knowledge of mental health; scientifically provide psychological evaluation system, identify the mental health index of college students, and help college students to evaluate themselves scientifically and correctly, so as to understand themselves more accurately; systematically understand the psychological characteristics of students, establish online psychological files of college students, and use online suggestions, conversations, and video recordings to inspire students to correctly understand the problem; rationally use appropriate methods to vent and relieve the psychological pressure of college students, so as to promote their personality to develop in a sound and perfect direction [16]. The establishment of a network information exchange platform provides a new idea for us to adapt to the ideological and political work of college students in the new era and improve the quality and efficiency of work. With the development of network information technology, timely absorption and creative application to the ideological and political work of college students will surely promote the ideological and political work of college students to achieve better results.


## 4.4. Pretreatment

The preprocessing function is used to preprocess the data set and judge the visibility and obstacle distance. The specific sequence is shown in Figure4. (1) The preprocessing of a university’s one-day student distribution data set in Shanghai is mainly to remove outliers and transform the coordinate system of the geographic information in the data set and store the preprocessed results in the database. The college students’ distribution data set obtained in this paper is stored in CSV format, in which each line represents a piece of college students’ relevant information, and each piece of college students’ information is separated by commas [17]. This formatted data set read-in processing is quite convenient.Figure 4
Sequence diagram of preprocessing.


## 5. Empirical Research Design

### 5.1. Knowledge Construction Impact Analysis

The 62 students involved in posting/replying to posts were screened semiautomatically, and invalid posts such as spamming were filtered out using a natural language processing program, and then, the remaining 2,000 posts were manually categorized and coded (KC1: sharing and clarification, KC2: cognitive conflict, KC3: negotiation of meaning, KC4: testing and revision, and KC5: reaching and application), and the number of postings/replies at each level of knowledge construction for each student was counted. The number of postings/responses and the percentage of them are counted, and the results are shown in Table1 on the following page. As can be seen from Table 1, the number of posts of students in both groups decreases as the level of knowledge construction increases, and the level of KC5 is 0. The proportion of low-level posts (KC1 and KC2) of students in both groups is higher (79.4% and 76.5%, respectively), which indicates that students in the learning community share a lot of knowledge and discuss and compare it to some extent; the proportion of high-level posts of students in both groups and the percentage of high-level posts of students in both groups were very small (20.8% and 23.5%, respectively), but the number of posts at high levels (KC3 and KC4) of students in the experimental group was significantly higher than that of students in the control group, indicating that students in the experimental group had better communication and discussion and reached meaningful negotiation and testing on some issues. (Figure 5).Table 1
Post/reply post classification code statistics.
Control groupExperience groupNumber of effective postsPercentage of posts (%)High-/low-level percentage of posts (%)Number of effective postsPercentage of posts (%)High-/low-level percentage of posts (%)KC164664.679.462562.576.5KC214814.814014.0KC313413.420.816816.823.5KC4727.2676.7KC50000Figure 5
Control group share.Figure6 is a comparison of the average and standard deviation of the number of posts in all experiments. Among them, the vertical coordinate span corresponding to the average number of posts and the standard deviation is 0 to 1.Figure 6
Comparison chart of average and standard deviation of posts.


### 5.2. Standardization of Respondent Background Information Data

In order to improve the comparability of data and the value of multiple uses among them, it is necessary to standardize the data of respondents’ background information in the data resource sharing platform in the field of IPE of college students. We know that the most frequently used research method in the field of experimental science is the controlled variable method, which means to the maximum extent of ensuring the same influencing factors in two groups of experimental subjects, artificially causing a difference in one of the factors, and observing what differences exist in the experimental results, from which it can be basically inferred that the differences in the experimental results are caused by the artificially controlled differential variables [18]. Similarly in the empirical field of social science research, although there are no conditions for conducting experiments with comparison groups, the correlations between several variables can be studied through surveys, and if the correlation between two variables is high, it reflects the existence of some kind of association between the two variables. The correlation between variables can be reflected in a single study, or the variables can be selected in different research projects for comparative studies, and it is necessary to standardize the data on respondents’ background information in order to facilitate comparative studies between different studies. For example, suppose there are two independent studies on the topic of college students’ consumption perceptions, with Research Project A conducted at Fudan University and Research Project B conducted at Shanghai University, and although the topics of the two studies are not identical, there are several questions (including options) that are identical. Once the data results of both studies were uploaded to the sharing platform, researcher C, after purchasing the data resources of A and B, could conduct an additional study on the effect of university differences on college students’ consumption perceptions. In Research Project C, it is necessary to keep a certain factor the same for the validity of the study, such as the difference in the amount of money spent on electronic products per year by college students from different universities, given that the average monthly consumption of the group of college students surveyed is the same. It can be seen that the more standardized, modularized, and structured the study is, the greater the research value of different research studies that can be used interactively with each other. Standardization in terms of background information data should focus on building a question bank of background information surveys and setting standard specifications for the options, and each survey study can choose several topics of background information according to its actual needs [19–23]. These questions may cover various aspects of respondents’ age, gender, occupation, grade, place of origin, average monthly consumption, health status, family relationships, marital relationships, and romantic relationships. The flow is shown in Figure 7.Figure 7
A process for structuring the survey research projects and data results in the data resource sharing platform in the field of IPE for college students.Figure8 is a comparison of three understanding times. As can be seen from Figure 7, the author’s method is slightly better than the general method, while the experimental method teaching evaluation time comparison method takes a long time in the data anonymization coding processing stage, resulting in poor overall performance of the method. Because the author’s method adopts the constrained clustering method, it can effectively cluster and merge the index parameters, so as to improve the efficiency of index evaluation.Figure 8
Comprehension time comparison.


### 5.3. Constructing a Data Sharing Platform for IPE of College Students Based on Constrained Clustering

The model describes learning styles in two dimensions: “information processing” and “perception,” in which the information processing dimension describes the differences in individuals’ preferred ways of processing or transforming information, which can be divided into “active practice” and “reflective observation.” The information processing dimension describes the differences in individuals’ preferred ways of processing or transforming information and can be divided into two opposites: “active practice” and “reflective observation”; the perception dimension describes the differences in individuals’ preferred ways of perceiving the environment or acquiring experience and can be divided into two opposites: “concrete experience” and “abstract concept.” The perceptual dimension describes the difference in the individual’s preferred perceptual environment or experience acquisition style. The learning style vector can be mapped into two-dimensional coordinates, as shown in Figure9, where the learners correspond to the nodes in the diagram, and the distance between the nodes indicates the similarity between the two learners (or the two learning style vectors), and the shorter the distance, the greater the similarity.Figure 9
Learning style vector mapping.The construction of heterogeneous communities based on learning styles can be abstracted as a constrained clustering problem in essence: given the number of clustersK (i.e., the number of learning communities), learners with different learning styles are grouped into one class to realize the heterogeneous learning style of learners in each cluster, while the overall learning styles among clusters are consistent and the cluster size (i.e., the number of learning communities) is equal. However, the classical clustering algorithm will cluster learners with similar styles and cannot control the scale of clustering, resulting in uneven scale of learning community, and cannot guarantee the heterogeneity within the group and the homogeneity between groups [20]. In addition, when conducting network guidance and supervision, teachers should pay more attention to strategies and methods. Since teachers obtain and publish information on the Internet as an equal member with students, teachers should pay more attention to communication and equality and fully respect students when they learn about students’ situations through the Internet; regard students as individuals with thoughts, feelings, independent personality, dignity and value; and believe that they have rational thinking and independent analysis, judgment, problem-solving ability, self-discipline, self-education, self-management, and encouragement. They say what they really want to say and listen carefully to voices from different corners [21]. It is not taboo for young students to have ideas, emotions, and ego behaviors. Ideological and political workers must correctly understand the role of the Internet as an “exhaust valve” and “discharge port” for social life. It is necessary to understand and sympathize with the negative emotions of college students due to interpersonal barriers, emotional setbacks, learning anxiety, economic difficulties, etc. and make them moderately vent so as to resolve conflicts. This requires the ideological and political workers of college students to completely abandon the mentality of “quick success and quick profit” when guiding and supervising the online public opinion of college students and not to be satisfied with the refutation of one idea to another and stay at the level of dry and empty theoretical preaching but to promote students to “become talents” on the basis of “adulthood” through education full of ultimate care for life, affirmation and acceptance, and respect and trust for students [22].


## 5.1. Knowledge Construction Impact Analysis

The 62 students involved in posting/replying to posts were screened semiautomatically, and invalid posts such as spamming were filtered out using a natural language processing program, and then, the remaining 2,000 posts were manually categorized and coded (KC1: sharing and clarification, KC2: cognitive conflict, KC3: negotiation of meaning, KC4: testing and revision, and KC5: reaching and application), and the number of postings/replies at each level of knowledge construction for each student was counted. The number of postings/responses and the percentage of them are counted, and the results are shown in Table1 on the following page. As can be seen from Table 1, the number of posts of students in both groups decreases as the level of knowledge construction increases, and the level of KC5 is 0. The proportion of low-level posts (KC1 and KC2) of students in both groups is higher (79.4% and 76.5%, respectively), which indicates that students in the learning community share a lot of knowledge and discuss and compare it to some extent; the proportion of high-level posts of students in both groups and the percentage of high-level posts of students in both groups were very small (20.8% and 23.5%, respectively), but the number of posts at high levels (KC3 and KC4) of students in the experimental group was significantly higher than that of students in the control group, indicating that students in the experimental group had better communication and discussion and reached meaningful negotiation and testing on some issues. (Figure 5).Table 1
Post/reply post classification code statistics.
Control groupExperience groupNumber of effective postsPercentage of posts (%)High-/low-level percentage of posts (%)Number of effective postsPercentage of posts (%)High-/low-level percentage of posts (%)KC164664.679.462562.576.5KC214814.814014.0KC313413.420.816816.823.5KC4727.2676.7KC50000Figure 5
Control group share.Figure6 is a comparison of the average and standard deviation of the number of posts in all experiments. Among them, the vertical coordinate span corresponding to the average number of posts and the standard deviation is 0 to 1.Figure 6
Comparison chart of average and standard deviation of posts.


## 5.2. Standardization of Respondent Background Information Data

In order to improve the comparability of data and the value of multiple uses among them, it is necessary to standardize the data of respondents’ background information in the data resource sharing platform in the field of IPE of college students. We know that the most frequently used research method in the field of experimental science is the controlled variable method, which means to the maximum extent of ensuring the same influencing factors in two groups of experimental subjects, artificially causing a difference in one of the factors, and observing what differences exist in the experimental results, from which it can be basically inferred that the differences in the experimental results are caused by the artificially controlled differential variables [18]. Similarly in the empirical field of social science research, although there are no conditions for conducting experiments with comparison groups, the correlations between several variables can be studied through surveys, and if the correlation between two variables is high, it reflects the existence of some kind of association between the two variables. The correlation between variables can be reflected in a single study, or the variables can be selected in different research projects for comparative studies, and it is necessary to standardize the data on respondents’ background information in order to facilitate comparative studies between different studies. For example, suppose there are two independent studies on the topic of college students’ consumption perceptions, with Research Project A conducted at Fudan University and Research Project B conducted at Shanghai University, and although the topics of the two studies are not identical, there are several questions (including options) that are identical. Once the data results of both studies were uploaded to the sharing platform, researcher C, after purchasing the data resources of A and B, could conduct an additional study on the effect of university differences on college students’ consumption perceptions. In Research Project C, it is necessary to keep a certain factor the same for the validity of the study, such as the difference in the amount of money spent on electronic products per year by college students from different universities, given that the average monthly consumption of the group of college students surveyed is the same. It can be seen that the more standardized, modularized, and structured the study is, the greater the research value of different research studies that can be used interactively with each other. Standardization in terms of background information data should focus on building a question bank of background information surveys and setting standard specifications for the options, and each survey study can choose several topics of background information according to its actual needs [19–23]. These questions may cover various aspects of respondents’ age, gender, occupation, grade, place of origin, average monthly consumption, health status, family relationships, marital relationships, and romantic relationships. The flow is shown in Figure 7.Figure 7
A process for structuring the survey research projects and data results in the data resource sharing platform in the field of IPE for college students.Figure8 is a comparison of three understanding times. As can be seen from Figure 7, the author’s method is slightly better than the general method, while the experimental method teaching evaluation time comparison method takes a long time in the data anonymization coding processing stage, resulting in poor overall performance of the method. Because the author’s method adopts the constrained clustering method, it can effectively cluster and merge the index parameters, so as to improve the efficiency of index evaluation.Figure 8
Comprehension time comparison.


## 5.3. Constructing a Data Sharing Platform for IPE of College Students Based on Constrained Clustering

The model describes learning styles in two dimensions: “information processing” and “perception,” in which the information processing dimension describes the differences in individuals’ preferred ways of processing or transforming information, which can be divided into “active practice” and “reflective observation.” The information processing dimension describes the differences in individuals’ preferred ways of processing or transforming information and can be divided into two opposites: “active practice” and “reflective observation”; the perception dimension describes the differences in individuals’ preferred ways of perceiving the environment or acquiring experience and can be divided into two opposites: “concrete experience” and “abstract concept.” The perceptual dimension describes the difference in the individual’s preferred perceptual environment or experience acquisition style. The learning style vector can be mapped into two-dimensional coordinates, as shown in Figure9, where the learners correspond to the nodes in the diagram, and the distance between the nodes indicates the similarity between the two learners (or the two learning style vectors), and the shorter the distance, the greater the similarity.Figure 9
Learning style vector mapping.The construction of heterogeneous communities based on learning styles can be abstracted as a constrained clustering problem in essence: given the number of clustersK (i.e., the number of learning communities), learners with different learning styles are grouped into one class to realize the heterogeneous learning style of learners in each cluster, while the overall learning styles among clusters are consistent and the cluster size (i.e., the number of learning communities) is equal. However, the classical clustering algorithm will cluster learners with similar styles and cannot control the scale of clustering, resulting in uneven scale of learning community, and cannot guarantee the heterogeneity within the group and the homogeneity between groups [20]. In addition, when conducting network guidance and supervision, teachers should pay more attention to strategies and methods. Since teachers obtain and publish information on the Internet as an equal member with students, teachers should pay more attention to communication and equality and fully respect students when they learn about students’ situations through the Internet; regard students as individuals with thoughts, feelings, independent personality, dignity and value; and believe that they have rational thinking and independent analysis, judgment, problem-solving ability, self-discipline, self-education, self-management, and encouragement. They say what they really want to say and listen carefully to voices from different corners [21]. It is not taboo for young students to have ideas, emotions, and ego behaviors. Ideological and political workers must correctly understand the role of the Internet as an “exhaust valve” and “discharge port” for social life. It is necessary to understand and sympathize with the negative emotions of college students due to interpersonal barriers, emotional setbacks, learning anxiety, economic difficulties, etc. and make them moderately vent so as to resolve conflicts. This requires the ideological and political workers of college students to completely abandon the mentality of “quick success and quick profit” when guiding and supervising the online public opinion of college students and not to be satisfied with the refutation of one idea to another and stay at the level of dry and empty theoretical preaching but to promote students to “become talents” on the basis of “adulthood” through education full of ultimate care for life, affirmation and acceptance, and respect and trust for students [22].


## 6. Conclusion

While information technology has spawned the emergence of big data, it has also changed people’s life and entertainment. Taking college students as an example, in the Internet environment, with the emergence of smart phones and the development of various social media, students are in a state of being inseparable, playing on the Internet all the time. However, at the same time, in the face of the intricate variety of ideological information on the Internet, the ideology and behavior of college students will also be impacted and adversely affected.This paper is based on the above two points: first, the important role of the integration of big data and education; second, students create data while being influenced by the data, propose the study of early warning mechanism of IPE in universities in the context of big data, apply big data to the early warning mechanism of IPE in universities, give full play to the value of big data in the field of education, enhance the effectiveness of early warning mechanism of IPE in universities, promote the good operation of IPE in universities, ensure that students in universities adhere to the correct ideological and political direction and good ideological and behavioral dynamics, and grow up healthily.The research on the construction of data resource sharing platform in the field of IPE for college students is a very meaningful research topic. It is not only that there are many low-level repeated empirical studies in the current research situation in this field, which wastes national resources and researchers’ energy, but also an effort and attempt to build a good empirical research ecosystem in the field of IPE for college students. It is undeniable that at present, there are still many difficulties and obstacles in the establishment of the data resource sharing platform in the field of IPE for college students. For example, the construction of the platform requires capital investment, the recognition of researchers, the recognition of universities, etc., which cannot be solved overnight. Success is achieved by action. It is expected that one day in the future, the data resource sharing platform in the field of IPE for college students will be truly completed to help the research and development in the field of IPE.



---

*Source: 2148657-2022-07-30.xml*