# Application of Intelligent Education Using Interactive Modeling in Higher Ideological and Political Education Platform

Authors: Shaoying Jiang; Mishal Sohail
Journal: Security and Communication Networks
 (2022)

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## Abstract

In order to improve the efficiency of ideological and political intelligent teaching, this paper uses interactive modeling technology to build an ideological and political education platform. In the classroom interactive image processing, this paper uses the maximum circumscribed moment of the contour to extract the rectangular block area and uses the convolutional neural network to distinguish the defect and the text for the extracted rectangular block area. In addition, this paper comprehensively judges the detection results of each detection area and designs a corresponding detection algorithm for each part to detect defects. From the experimental research results, it can be seen that the interactive ideological and political teaching platform proposed in this paper has a good interactive teaching effect and can effectively improve the effect of ideological and political teaching.


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## Body

## 1. Introduction

With the fast expansion of higher education in our nation, a variety of innovative teaching tools and technology have made it much easier for colleges and universities to improve teaching and scientific research. Simultaneously, a wide range of multimedia goods and network technologies are making their way into colleges and universities, offering solid technological backing for the powerful combination of college education and distant learning. In particular, as college and university enrollments have grown, resource pressure on practical teaching links such as experiments and internships has increased, and large-class teaching has become common in colleges and universities, causing a slew of issues such as difficulty answering questions for teachers, tutoring, and problem-solving for students. In computer courses and computer-aided design courses, establishing a multimedia network experimental computer room using multimedia and network technology can effectively solve the difficult problem of information interaction between teachers and students, as well as improve the teaching effect of practical links. Furthermore, multimedia interactive technology may provide real-time monitoring of the educational process and increase classroom monitoring effectiveness.In actual teaching, one student can control another student’s mouse and keyboard and adopt a mutual-aid learning method to improve students’ learning efficiency. Teachers can set the screens of all (a certain group or a certain) students to “black screen” during lectures to remind students to pay attention to the lecture and can also monitor a certain group or all students. The teacher can monitor the computer screen of the students in real time, understand the learning situation of the students without leaving their seats, realize the monitoring and management of the students’ computers on the entire network, and regularly save the computer screen images of all members to the folder designated by the teacher’s computer, which is convenient for unattended or after-the-fact viewing of student computer usage. Students choose the electronic hand-raising function, and the screen of the teacher can immediately display the specific position of the “raised hand” student. The teacher can judge whether it is a personality or a common problem according to the situation of the student’s question and then choose a separate or centralized problem. The teacher or a student operates the computer of other students, just like operating his own computer; the teacher can use this function to conduct individual interactive tutoring/teaching for the students. It has the function of prohibiting students from using the keyboard and mouse to operate the computer. It can shut down or restart the computers of a group of students or all students, let the students’ computers run a certain program at the same time, and also restrict students’ access to the computer, including only allowing running some programs, hiding the hard disk, prohibiting the use of registry, control panel, and other permission settings, viewing the system, memory, drive, process, and other information of a student’s computer, and can forcibly end a process running on the student’s computer.This paper uses interactive modeling technology to build an ideological and political education platform to improve the interactive teaching effect of ideological and political education and provide a reference for the follow-up ideological and political education reform.


## 2. Related Work

Blended learning, which is a blend of face-to-face teaching and online learning, is a novel learning style pushed by the age of educational informationization [1]. The interaction and reciprocal influence of interacting individuals are referred to as interaction [2]. Teaching interaction refers to the communication that takes place between students and their learning environment, including communication between instructors and students, as well as communication and interaction between students and different tangible resources [3]. In the blended learning context, teaching interaction research primarily focuses on the teaching interaction process technique and practice strategy, as well as study on the teaching interaction impact. The interactive design of online and offline teaching activities, such as the bullet screen interaction and peer evaluation interaction in the online microvideo learning process, as well as various collaborative learning and problem-solving activities supported by offline computers, is the focus of the classroom design of teaching interaction [4]. Learners’ classroom engagement and involvement have considerably enhanced when online courses are combined with conventional courses [5]. A good social environment is developed in a blended learning environment to increase interaction between instructors and students, which is favorable to producing a good learning environment and boosting learners’ learning expectations and satisfaction [6]. According to similar research, instructors have a favorable perspective of course interaction in terms of social and instructional existence categories, whereas students have a negative opinion of course interaction [7]. The linkage and synergy between the aspects of teaching engagement are inadequate in the learning process. The absence of structured classroom instruction will be alleviated by incorporating the notion of ecosystems into the educational and teaching process. At the moment, smart classroom ecosystem research is still in its infancy, with the emphasis mostly on the phases of model development and route exploration [8]. System application research is still in its infancy.In view of the limitations of the existing online 3D modeling software in function integration, system interaction, and data lightweight, the mainstream framework 3D modeling system is adopted. Compared with the existing online 3D modeling software, the system integrates basic geometry parametric modeling, 3D model Boolean operations, texture mapping, light and shadow rendering, and other model creation and processing functions [9]. In order to improve the human-computer interaction of the system, lower the threshold of 3D modeling, and meet the personalized modeling needs of different user groups, the traditional hand-painted interactive 3D modeling method is transformed with the aid of two auxiliary equipment, the hand-painted board and the 3D scanner, and laser 3D scanning methods are integrated into the Web environment [10]; the hand-drawn sketch 3D modeling method using the hand-drawn board as the input method is an interactive operation mode that relies on vision, touch, and high randomness. It is simple to operate, highly interactive, conforms to the traditional paper-pencil interaction habits, and can effectively lower the threshold of 3D modeling, especially suitable for ordinary users without any 3D modeling skills and experience [11]. The modeling method based on laser 3D scanning is a symmetrical application of the traditional forward modeling method, which can reversely obtain the 3D digital model of the scanned object according to the existing object. With the continuous progress of laser measurement technology, the speed of laser 3D scanning modeling is getting faster and faster, the accuracy is getting higher and higher, and the operation process is getting easier and simpler. It is one of the most commonly used 3D modeling methods for 3D printing enthusiasts [12]. In order to meet the lightweight requirements of interactive 3D modeling in the Web environment, the hand-drawn sketch data and point cloud data reduction algorithms in the Web environment are designed, respectively [13].The process of information exchange between humans and computers to fulfil certain tasks through an interactive or communication interface is referred to as human-computer interaction [14]. The computer graphics community is paying more and more attention to computer interaction as research on human-computer interaction continues to heat up. An emerging research area [15] is how to incorporate easy and natural interaction mechanisms into the field of computer graphics to aid in addressing classic computer graphics difficulties. The evolution of computer graphics is almost timed with the study on interactive 3D model modeling. In terms of user experience and algorithm function, the interactive model modeling technique outperforms the completely automated model generating algorithm. The interactive modeling technique is straightforward and easy from the user’s perspective, and the modeling results may be acquired with a few numbers of interactive activities [16]. The interactive modeling technique decreases the user’s entry barrier. The capacity of the user to control, alter, and participate in the modeling process outweighs that of autonomous algorithms. Interactive 3D modeling, from the standpoint of algorithms, delivers user-level semantic information through human-computer interaction, which may help solve and simplify challenges in model analysis and building [17]. The algorithm’s flexibility is increased by the addition of interactive information, and the accuracy of the modeling results may be considerably improved by increasing the quantity of interactive information. As a result, interactive 3D modeling has always been significant in the modeling and processing of models [18].


## 3. Interactive Modeling Algorithms

Aiming at the inconsistency of the gray value of the student image, in this paper, the grayscale transformation is used to enhance the student image, improve the contrast of the image, and stretch the gray value of the student image to between 0 and 255. Grayscale stretching is performed using the following formula:(1)gx,y=255gmax−gminfx,y−gmin.Among them,fx,y represents the gray value of the extracted student area coordinates x,y, gmin represents the minimum value of the gray value of the student area, and gmax represents the maximum value of the gray value of the student area. By using formula (1), the gray value of the student area can be stretched to the [0, 255] interval and the gray difference can be reduced. The images before and after grayscale stretching are shown in Figure 1. It can be seen from the experimental results that grayscale stretching can not only solve the problem of grayscale inconsistency but also enhance the contrast of the image, which in turn makes the defect area more obvious.Figure 1
Grayscale stretching comparison diagram. (a) Before grayscale stretching. (b) After grayscale stretching.
(a)(b)The mathematical principle of image segmentation based on threshold is calculated according to formula (2), where f(i, j) represents the pixel value of the original image at coordinates (i, j), g(i, j) represents the thresholded image (binary image), and th is the threshold for segmentation. It can be seen from the formula that the quality of the segmentation result is closely related to the selection of the segmentation threshold.(2)gi,j=255,iffi,j≥th,0,iffi,j<th.From the collected images of classroom students, the defect area is usually darker than the intact area, the gray value is lower, and there is a certain gray difference with the normal area. It can process student images through threshold segmentation or edge detection algorithms, segment defect areas, extract features, and then identify and judge them. The flowchart of the detection and recognition algorithm for threshold segmentation of student images is shown in Figure2.Figure 2
Overall detection process.Students in the classroom are divided into groups based on a set of criteria, and experiments are conducted on the acquired data. Figure3 shows the segmentation detection findings. The detection region drawn according to the student positioning result and the student model is surrounded by green in the figure, and the red section represents the fault part after threshold segmentation. Although the student’s defect area can be identified properly, the hard band region at the bottom of the student is also recognized as a defect, according to the experimental findings. The wear-resistant belt region at the bottom of the empty bottle students was in touch and friction with the conveyor belt, according to the student photographs and classroom teaching images. As a consequence, there is a tiny grind in this region and the image’s gray value is reduced. However, after speaking with the beer maker, they feel that the quality of such students is normal, which means that the antiskid belt component of the bottle’s bottom half cannot be considered a faulty part in practice.Figure 3
Segmentation detection results. (a) Before segmentation detection. (b) After segmentation detection.
(a)(b)When a given threshold is used to segment the whole image of students, the dark antiskid strip at the bottom is usually mistakenly detected as a defective part. The threshold-based image segmentation method can detect some defects of students, but it is difficult to balance the detection of subtle defects while there is interference. In response to this difficulty, the paper will give specific solutions in the following chapters.In the image segmentation algorithm based on edge detection, we usually use the edge detection operator to extract the edge part of the image, and then segment the target object. The edge of an object appears in the form of discontinuity in the local characteristics of the image, such as the sudden change of gray level, the change of color, and the sudden change of texture structure. Common edge detection operators are as follows:(1)
The central difference operator isΔ2xfi,j=fi+1,j−fi−1,jΔ2yfi,j=fi,j+1−fi,j−1(2)
The Roberts operator isΔ+fi,j=fi+1,j+1−fi,jΔ−fi,j=fi,j+1−fi+1,j(3)
The Prewitt operator isΔx−101−101−101(4)
The Sobel operator is−101Δx−202−101Δx−101−202−101(5)
The Laplace operator is0101−41010In addition, there are some other operators, such as Canny operator.For students’ gray distribution characteristics (2), it can be explained by experiments. From the coordinates in the figure, the calculation formula of convolution is calculated according to (3). Among them, f(i, j) is the gray value of the image at coordinates (i, j) and g(i, j) is the image after convolution. Figure 4 shows the image after viewing the convolution.(3)gi,j=fi,j−14+fi−7,j−7+fi,j−7+fi+7,j−7+fi−7,j+fi−5,j−12fi,j+fi+5,j+fi+7,j+fi−7,j+7+fi,j+7+fi+7,j+7+fi,j+14.Figure 4
Convolution processing results.Combining the convolution results and the analysis of the students’ images in the classroom, it is found that the grayscale distribution of the students’ images is block-shaped, and the area division is shown in Figure5.Figure 5
Five-area division diagram.The entropy rate clustering method can realize the segmentation of the image into any givenk superpixel. This method is a segmentation method based on graph theory. The undirected graph model of the image is set as G=V,E,W, where V represents the node of the graph, E represents the edge of the image, and W is the weight of the edge of the graph. The edges of an undirected graph can be defined using 4 neighborhoods or 8 neighborhoods. The weight value W is usually defined by a Gaussian function or a Cauchy function and is calculated by formulas (3) and (4), respectively, and the weight value is used to measure the similarity between adjacent pixels.(4)Wij=e−Ix−Iy2/σ2,(5)Wij=11+Ix−Iy/σ2.The process of entropy rate clustering is to select a subset A from the boundary setE by optimizing the objective function FA, so that the subset A can divide the node set V into several superpixels. The objective function FA is defined as shown in formula (6); FA is composed of the entropy rate term HA and the balance term BA, and the weight term λ is introduced as the weight of the balance term. Therefore, in a relatively uniform image area, the balance term can well ensure that the size of the superpixel can be basically the same. The objective function F(A) is optimized based on entropy.(6)maxFA=HA+λBA,A⊆Esubject toNA≥k.For the calculation of the first termHA of the objective function, a walk model is used. The boundary subset A constructs a spanning tree on the graph G′=V,A, and the entropy rate is calculated by the static distribution probability μ and transition probability pij in the random process, as shown in the following formula:(7)HA=−∑iμi∑jpijAlogpijA.The transition probabilitypij is the ratio of the weight of the boundary to the sum of the weights of all the boundaries connected to node i in the graph wi. If the boundary is not connected to node i, the transition probability is 0. However, this calculation does not guarantee that the sum of all transition probabilities of a node i is 1. Therefore, the self-transition probability is introduced in the paper to ensure that the sum of the transition probabilities of any node is 1, as shown in the following formula:(8)pij=wijwi,ifi≠jandeij∈A,0,ifi≠jandeij∉A,1−∑j:eij∈Awijwi,ifi=j.The calculation of the sum of weights of adjacent pixels of a node is shown in the following formula:(9)wi∑k:eik∈Ewik.The static distribution probabilityμ is the ratio of the weight sum wi of each node to the sum wT of all wi, as shown in the following formula:(10)μ=μ1,μ2,…μV=w1wT,w2wT…wVwT,(11)wT=∑IVwi.The calculation of the balance term is determined by the size of the superpixel. The first step is to convert its size into the ratiopZA of the number of pixels Si contained in the superpixel relative to the total pixels V, as shown in the following formula:(12)pZAi=SiV.Afterwards, the size distribution of superpixel is estimated by the factor ofpZA, and a fixed term −NA is added at the same time, as shown in the following formula:(13)BA=HZA−NA=−∑i=1NAPZAilogPZAi−NA.For the clustering of superpixel, a clustering method based on graph theory segmentation is used in this paper. The result of superpixel segmentation is established as an undirected graph of superpixelGh=Vh,Eh,Wh, the edges of the undirected graph are connected by 4 neighborhoods or 8 neighborhoods, and the weights of the edges are calculated by the following formula:(14)WhSi,Sj=LASi−LASj.Among them,Si and Sj represent superpixels i and j, WhSi,Sj is the weight of the edge connecting superpixels Si and Sj, and LASi represents the average gray value of the pixels contained in superpixel i.The clustering and merging process of superpixel is realized by calculating the inner and outer differences of adjacent superpixel. For two adjacent superpixel regionsC1 and C2⊂Vh, when their heterodyne is less than the minimum inner difference, the two regions are merged. The heterodyne is the minimum value of the edges connecting the regions, and the inner difference is the maximum weight in the minimum spanning tree of the region. The heterodyne and the minimum inner difference are calculated by formulas (15) and (16):(15)DifSi,Sj=minWhSi,Sj,Si∈C1,Sj∈C2,Si,Sj∈Vh,(16)MIntC1,C2=minIntC1+τC1,IntC2+τC2.In formula (16), IntC represents the maximum weight of the minimum spanning tree of C, τC=kτ/C represents the offset term, the denominator C represents the number of superpixels in the region, and the numerator kτ is a control parameter. The setting of the offset item is convenient to increase the inner difference of the small area, which is beneficial to its fusion with the adjacent area.According to the important feature that the gray value of the defect area is low, the paper performs defect identification by calculating the ratio of the gray average value of the segmented area and the average value of the entire detection area, as shown in formula (19). Here, the ratio is used for defect recognition instead of the average value of a single gray value. The main reason is to enhance the robustness of the algorithm and avoid the influence of changes in light intensity.(17)ACi=∑x,y∈Cifx,yM,(18)AI=∑x,y∈Ifx,yN,(19)Di=ACiAI=Defect area,ifDi<Ti,Normal area,ifDi≥Ti.In formulas (17)–(19), ACi represents the grayscale average value of the pixels in the separated area Ci, fx,y represents the grayscale value at the coordinates x,y, M represents the number of pixels in the area, AI represents the average pixel gray level of the entire detection area, N represents the number of pixels in the entire detection area, and Ti is the defect recognition threshold, which is set to 1 in the paper.For hard band regions, only the relative gradient in thex-direction is calculated. Its algorithm is as follows:(1)
The convolution of the region image and the horizontal gradient operator is obtained as follows:(20)gx,y=fx−1,y−1−fx+1,y−1+fx−1,y−fx+1,y+fx−1,y+1−fx+1,y+1.(2)
The threshold is given. If it is greater than the threshold, it is a defect; otherwise, it is not a defect:(21)p=gx,y≥T,gx,y>T.The traditional Canny operator is filtered by a Gaussian function and detected by a given threshold. Its adaptive ability is slightly weaker, and it obtains the gradient value for a 2 × 2 window, which is sensitive to noise. Here, by means of adaptive filtering, the gradient calculation window is increased to realize edge extraction. After the edge is extracted, the LOGO character area and defect area are extracted by calculating the circumscribing moment of the edge. The specific steps are as follows.Step 1.
The algorithm adopts an adaptive way to replace the traditional Gaussian filtering way to filter the image. The algorithm convolves the image through a weighted average template and iterates multiple times to sharpen the edges of the image.
We assume thatfx,y is the input image, and the calculation steps of one iteration are as follows:(1)
The gradient componentsGxx,yandGyx,y are determined by the following formulas:(22)Gxx,y=Ix+1,y−Ix−1,y,Gyx,y=Ix,y+1−Ix,y−1.(2)
The template coefficient is determined by the following formula:(23)wx,y=exp−Gx2x,y+Gy2x,y2k2.(3)
The weighted average of the imagefnx,y is carried out by the following formula:(24)In+1x,y=∑i=−11∑j=−11Inx+i,y+jwnx+i,y+j∑i=−11∑j=−11wnx+i,y+j.
A constant is given fork in (24), so as to ensure that the magnitude of the abrupt edge is not too large or too small. In this experiment, the value of k is 10 Inx,y is the image after the nth iteration, and the number of iterations in this experiment is 2.Step 2.
Gradient size and direction are obtained: the traditional Canny detection operator only detects edges from two directions, and it is sensitive to noise. It refers to the gradient template of the Sobel operator to calculate the gradient size and direction on the vertical, horizontal, and two diagonals of the eight neighborhoods of the pixel to suppress noise. The detection template is shown in Figure6.
The four-direction gradient componentsGxx,y¸Gyx,y¸G45x,y, and G135x,y are obtained by convolving the filtered image with the four templates, respectively. The formula for calculating the gradient size and gradient direction is(25)Mx,y=Gx2+Gy2+G452+G1352,θx,y=arctanGy2x,yGx2x,y.
The training process of the convolutional neural network is almost the same as that of the common BP neural network. The main points include 4 steps, which are divided into two stages: forward propagation stage and backward propagation stage. The specific process is as follows:
The first stage is the forward propagation stage, which is defined as follows: (a) the algorithm takes a sampleX,Yp from the sample set and inputs X into the network; (b) the algorithm calculates the corresponding actual output Op.
At this stage, the information of the network is transformed layer by layer through the input layer and then transmitted to the output layer step by step. This process is also the normal execution of the trained network. The layer-by-layer transformation is essentially a calculation process. The calculation method is to perform dot multiplication between the input of each layer and the weight matrix of the corresponding layer to obtain the corresponding output result:(26)Op=Fn…F2F1XpW1W2…Wn.
The second stage is the backward propagation stage, which is defined as follows: (a) the algorithm calculates the difference between the actual outputOp and the corresponding ideal output Yp; (b) the algorithm adopts the minimization error method to adjust the weights by backpropagation.
The size of the training sample data is 60 × 80, and it is converted into a 4800-dimensional vector for processing under the TensorFlow framework. The sample image is a grayscale image, and the grayscale value of the image is in the range of 0–255. The input vector is normalized by (27), and the input vector is normalized to be between [−1, 1].(27)X=X0128.0−1.0.
In (27), X0 represents the unnormalized vector and X represents the normalized vector. The normalization process can not only speed up the convergence speed but also possibly improve the accuracy.Figure 6
Gradient template. (a)x direction of principal axis. (b) y direction of principal axis. (c) 45° direction. (d) 135° direction.
(a)(b)(c)(d)


## 4. Application of Intelligent Education Using Interactive Modeling in Higher Ideological and Political Education Platform

The interactive ideological and political education classroom environment has a complicated hierarchical structure due to the extensive variety of interactions between different parts within the system and the variances in teaching topics. This study looks at how different parts in the system interact, and it creates an interactive classroom environment model, as illustrated in Figure7. Furthermore, the association between ecosystem components from the core layer, intermediate layer, cornerstone layer, and aggregation layer is examined in this work. The model focuses on the interaction between teaching subjects, pays close attention to the synergy between various elements, and mines, aggregates, analyses, and applies the teaching interaction data generated by the interaction of various elements in order to promote better circulation and interaction among various elements, resulting in a harmonious, symbiotic, and sustainable organic life community.Figure 7
Interactive ideological and political classroom ecosystem model.Based on the above research methods and routes, this paper adopts a layered architecture to design an online 3D modeling system that meets the individual needs of different user groups. The framework is shown in Figure8. The overall structure of the system can be divided into the following five layers: device layer, function layer, key technology layer, database layer, and network protocol layer.Figure 8
Overall framework of the system.On the basis of the above research, a simulation schematic diagram, as shown in Figure9, is constructed.Figure 9
Simulation diagram of interactive teaching of ideology and politics.On the basis of the above research, the effect of the interactive ideological and political teaching platform proposed in this paper is verified, and the statistical teaching effect is shown in Table1.Table 1
Effectiveness verification of interactive ideological and political teaching platform.
NumberTeaching simulationNumberTeaching simulationNumberTeaching simulationNumberTeaching simulation193.801983.663780.115583.16281.722084.273885.695691.59386.132189.593979.185779.42487.462289.804093.405885.97582.662390.204179.385980.35680.472486.844281.636080.89789.752585.824383.006190.40892.872684.084480.706283.90985.792785.704593.726382.501083.302889.214688.486488.991187.172981.374790.976592.411284.803079.504879.576691.871381.663183.434989.296782.541488.213282.515087.136889.701586.903391.225187.966986.261684.653481.355285.707084.681792.073579.415381.547188.751881.753692.125481.197284.16From the results in Table1, the interactive ideological and political teaching platform proposed in this paper has a good interactive teaching effect, which can effectively improve the ideological and political teaching effect.


## 5. Conclusion

The teacher computer may broadcast information such as the screen display and voice of a specific student to all students or a specific group at any moment in the classroom teaching interaction and information interaction control and management of instructors and students. They may also monitor a student’s information at all time, manage the student’s keyboard and mouse, provide “hand-in-hand” supervision, and swap between students at will. The software’s window intelligent scrolling remote control and window remote control tutoring enable instructors to do remote control operations on numerous student PCs while performing other instructional duties. This study focuses on the process of interactive ideological and political teaching, and it employs interactive modeling technology to create an ideological and political education platform with the goal of improving the effectiveness of interactive ideological and political teaching. The interactive ideological and political teaching platform described in this study has a good interactive teaching impact and may effectively increase the ideological and political teaching effect, according to the experimental research [19].



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