# Prediction of Vocational Education Coursework Assessment Results Based on the Simulated Annealing Neural Network

Authors: Yan Gao; Aidi Tan; Yanping Wei
Journal: Wireless Communications and Mobile Computing
 (2022)

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## Abstract

In order to improve the predictive effect of vocational education assessment results and provide a reliable boundary assessment platform for vocational education, this paper uses the simulated annealing neural network algorithm to process vocational education assessment data. Moreover, this article uses the SA algorithm based on the simulated annealing fish school algorithm (SAFSA) in the local search in the later stage of the AFSA algorithm to help the AFSA algorithm jump out of the local optimal solution. Finally, this article combines the improved algorithm to construct a simulated annealing neural network algorithm and builds a vocational education coursework assessment result prediction system, which verifies the prediction effect and assessment effect of this system through experimental research.


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## Body

## 1. Introduction

Vocational education has an important position in China’s education system. It provides a large number of basic, constructive, technical, and application-oriented talents for China’s social construction. The development of vocational education affects the improvement of the overall education level of our country. Which can serve as the front line for the development of the local economy, vocational education focuses more on the cultivation of practical ability compared with ordinary higher education. It takes application as its purpose and the needs of society as its goal. The main line is to cultivate the ability to apply technology [1]. The core job of the school is teaching. Therefore, the quality of teachers’ teaching directly affects the quality of talent training. Therefore, as the main body of teaching activities, teachers play a pivotal role in school education [2]. Teachers should not only have a solid theoretical foundation and a high level of scientific research but also have a wealth of practical experience and professional practical guidance capabilities. Therefore, how to mobilize teachers’ enthusiasm for work and improve their professional level has become a decisive factor affecting the development prospects of vocational colleges [3].In today’s era where information science and technology have developed rapidly, the Internet has changed and will continue to change many aspects of our lives. The rapid development of the Internet has also injected new vitality into modern education, and school education and teaching management have also become a hot spot in the current education. Now, the number of students in each school is gradually increasing amidst the massive expansion of the school’s enrollment. The scale is unmatched by any previous era and any foreign country. The teaching management of vocational schools has become more and more important. If it is more onerous, the workload and complexity increase by several times and, at the same time, contribute to the modernization and informatization of higher education. All these make the demand of software development related to the teaching management of vocational schools urgent, and the development of teaching management system has directly become an urgent task. China has a huge population base, and the number of students in the world is unmatched by other countries. The social background of software system research and development is simply determined by the status quo of Chinese education. The emergence of any tool requires its fundamental reasons for demand. One of the status quo of our Chinese universities is that there are too many people but few talents. Because of the strong demands of the society and economic market, college students are everywhere. Obviously, there is an urgent need for well-designed software to assist and improve the management of our education and teaching. The second situation is that the investment is low and the cost is high. China’s education is uneven; the country pursues industrialization, but China’s difficulty is that it has become commercialized in the end. Excellent education management software will reduce the cost of education in China to a large extent, thereby maximizing the science, culture, and technology of the people, improving the overall quality of the people, and building a new society.The innovations of this paper are (1) build a vocational education coursework assessment result prediction system through the simulated annealing neural network algorithm to provide theoretical support for improving vocational education teaching quality and assessment and evaluation and (2) use the simulated annealing neural network algorithm to conduct vocational education assessment data. Moreover, this paper uses the SA algorithm based on the simulated annealing fish swarm algorithm (SAFSA) in the local search in the later stage of the AFSA algorithm, which helps the AFSA algorithm jump out of the local optimal solution.


## 2. Related Work

Literature [4] realized that there are obvious differences in the academic performance of college students, used statistical methods to determine the reasons for this gap, established a statistical tool to measure the academic performance of students in one year, and proposed improvement measures. Literature [5] proposed an analysis method of the adaptive system for optimizing learning sequence. Based on the decision tree algorithm, a predictive model of student file management is established. According to the five characteristics of students’ performance, the rules for establishing the optimal learning sequence are proposed. Teachers can not only reduce teaching costs but also achieve optimal adaptive learning effects. Literature [6] analyzes the application of data mining in higher education and provides reference suggestions for higher education teaching. The main goal of the literature [7] is to provide students with high-quality education. The alienated traditional classroom teaching model is used for online testing through legitimate means to predict student performance and predict student final exam results through the decision tree method. Literature [8] discussed the academic performance of freshman engineering students and predicted the graduation status through factor analysis and regression analysis.Literature [9] believes that it is necessary to attribute a specific job to a specific type of performance. Literature [10] believes that only by clearly understanding one’s own work tasks can one clearly understand what needs to be analyzed and improved in the work. Literature [11] puts forward the concept of task performance. Task performance focuses on activities that are directly related to the production and service or can be used to maintain core technologies. Literature [12] puts forward the concept of peripheral performance. Different from task performance, peripheral performance emphasizes the individual’s interpersonal communication skills and some interactions to enhance interpersonal relationships. The realization of peripheral performance requires a supportive organization and psychological environment. Literature [13] believes that performance appraisal is the process of collecting, analyzing, evaluating, and transmitting a person’s performance in his job. Literature [14] pointed out that the performance appraisal of enterprises and institutions should include four aspects: morality, ability, diligence, and performance. These four items point out that a person’s political ideological character, business knowledge, diligence and dedication, and work results, respectively, all play an important role in performance appraisal. Literature [15] pointed out that the assessment of teachers can be consistent with the requirements of public institutions for staff and the content and methods of assessment can be individually formulated by each school according to its own situation. Literature [16] believes that the evaluation of teachers should start from different angles and comprehensively examine the teachers’ ideology, morality, knowledge, scientific research results, teaching quality, etc. Although his research has made an objective reference for teachers’ evaluation, the specific method is not easy to implement. Literature [17] pointed out that the traditional virtue, ability, diligence, and performance cannot be treated equally and more attention should be paid to virtue and performance. These experts and scholars have made great contributions to the prediction of vocational education coursework assessment results based on the simulated annealing neural network.The Internet speed of foreign developed countries is also much higher than that of domestic ones. Most schools have their own advanced campuses [18]. The network has laid a prerequisite and foundation for accelerating the construction of campus informatization [19]. Research on the educational administration management system has formed a relatively standardized management model for many years. The management system is generally a central database model. The management system architecture is mostly C/S architecture, and some of which are B/S architecture. Based on NET and J2EE platforms, they integrate teaching materials into one platform to realize data sharing and avoid information islands [20].


## 3. Vocational Education Coursework Assessment Data Processing Algorithm Based on the Simulated Annealing Neural Network

The fuzzy neural network has a parallel structure and can perform parallel data processing. This parallel mechanism can solve the large-scale real-time computing problem in the control system, and the redundancy in the parallel computing can make the control system have strong fault tolerance and robustness. The RBFNN is used to support vector machine and distributed data fitting is very important. The general Gaussian function is one of the radial basis functions.(1)Φx,y=ϕx−y.Among them,x is the Euclidean norm. According to the definition of E.M. Stein and G. Weiss, the radial basis function must satisfy the following: if x1=x2, then Φx1=Φx2.According to the literature, under certain conditions, it has been shown that the radial basis functionϕx−c can be close to almost any function. Among them, c is a fixed value. Therefore, multivariate functions can be regarded as unary functions. Simulated annealing is actually a greedy algorithm, but its search process introduces random factors. The simulated annealing algorithm accepts a solution that is worse than the current solution with a certain probability. Therefore, it is possible to jump out of this local optimal solution to reach the global optimal solution.All the above functions are symmetrical, and the independent variable decreases rapidly at the eccentric position. And the faster it decreases, the stronger the selectivity is. The common radial basis function curve is shown in Figure1.Figure 1
Common radial basis function curve.As shown in Figure2, the radial basis network structure is divided into three layers: input layer, mode layer, and output layer. The input layer is directly composed of input nodes. The mode layer can also be called the hidden layer, which consists of nodes directly connected to the input nodes, and the number of nodes in the mode layer is determined by the problem described in detail. This layer has K2≤K≤N neurons, and they are usually identified by Gaussian kernel function.
(2)ϕix=e−x−uiTx−ui/2σ2.Figure 2
Radial basis network structure.In the formula,ui is the function center of the ith node in the model layer and ϕiis the smoothing factor of theith node in the model layer. The linear unit in the output layer is connected to all mode layers, and the output function is
(3)fx=∑i=1Kwiϕix=∑i=1Kwie−x−uiTx−ui/2σ2.Among them,ωi is the connection weight of the ith mode layer unit.Usually, the RBFNN has different learning strategies and there are four common ones:(1)
The algorithm randomly selects a fixed center. In the method of randomly selecting a fixed center, the only parameter that needs to be trained and adjusted is the weight value between the mode layer and the output layer. In order to obtain the output weightω, the pseudo-inverse method is usually used. d=dkj is the expected output, dkj is the expected output value of the kth input vector at the jth output node, and the output weight matrix ω is expressed as(4)ω=G+d.In the formula, theG=gki matrix is ω=ωij.
(5)gki=ϕXK−Xi2,k=1,2,⋯,K;i=1,2,⋯,I.For a total ofK training input vectors, gki is the output value of the kth input vector at the ith implicit node and •+ represents the pseudo-inverse, which is also called the generalized inverse.
(2)
The algorithm self-organizes the selection center. The self-organizing selection center is divided into the self-organizing learning stage and supervised learning stage(6)σ=dmax2n.Among them,n is the number of mode layer nodes and dmax is the maximum distance between the selected cluster centers.
(3)
The formula for network parameter optimization calculation is as follows:(i)
The output weightA is(7)∂En∂ωin=∑k=1NeknGXk−tiCi,ωn+1=ωiIn−ηi∂En∂ωin,i=1,2,⋯,I(ii)
The centerti of the hidden layer is(8)∂En∂ωin=2ωin∑J=1NeknG′Xk−tiCiCiSiXk−tin,tin+1=tin−η2∂En∂tin,i=1,2,⋯,M(4)
The algorithm uses the orthogonal least square method. If the output layer has only one node, the network will be regarded as a special case of linear regression(9)dn=∑i=1Ipinωi+en,n=1,2,⋯,NEquation (9) is written in the following matrix form:
(10)P=UA.From the abovementioned equation, we can get(11)d=Pω+e=UAω+e=Ug.When both sides are multiplied by UT, we get(12)UTd=UTUg=Hg.Therefore, we can obtain(13)g=U−1UTd.At the same time, because ofd=Pω+e, when used as a least square estimation, there should be Aω=g. When both A and g have been calculated, they can be calculated according to this formula.The probabilistic neural network (PNN) is introduced in the late 1980s. This network can be regarded as a combination of the RBFNN.In order to simplify the types,c=c1, c=c2. And the prior probability is
(14)h1=pc1,h2=pc2,h1+h2=1.We are given an input vectorx=x1,x2,⋯,xN to obtain a set of observations.
(15)cc1,pc1∣x>pc2∣x,c2,otherwise.pc1∣x means that when x occurs, the posterior probability of the category c1 is
(16)pc1∣x=pc1px∣c1px.The classification decision classifies the input vector into a category and considers the losses and risks in practical applications. At the same time, the samples of thec1 category are incorrectly classified as the c2 category. Since the losses caused by category classification are often different, it is necessary to adjust the classification rules. We define the action taken as ai:
(17)Rai∣x=∑j=1Nλijpci∣x.If we assume that the correct loss of classification is 0,(18)Rc1∣x=λ12pc1∣x.Then, the Bayesian decision rule becomes(19)cc1,pc1∣x<pc2∣x,c2,otherwise,We assume that the regionRn is a hypercube with side length h and d is the feature space dimension. To verify whether the training sample x belongs to the region Rn, each component value of the vector x−xk needs to be verified.To calculate the numberK of n training samples falling into Rn, the window function needs to be defined as follows:Gauss developed Parzen’s conclusion and proposed a special case of multivariate Gaussian kernel function. Figure3 shows the structure of the probabilistic neural network. There are four layers in the structure diagram. The hidden layer of the second layer is the radial base layer. The neuron node of each hidden layer receives input information from the input layer. The hidden layer is defined as follows:
(20)Φijx=12π1/2σde−x−xiTx−xi/2σ2.Figure 3
Structure diagram of a probabilistic neural network.In the formula,i=1,2,⋯,M. The calculation formula is as follows:
(21)vi=∑j=1LΦij/L.In the formula,vi represents the output of the ith category and L represents the number of neurons in the ith category.The maximum output value is obtained in the summation layer, and it is used as the classification category of the output layer:(22)y=argmaxvi.In the actual calculation, after the vector of the input layer is multiplied by the weight, the input radial basis function is calculated, as shown in the following formula:(23)Zi=xωi.After studying the PNN, it can be analyzed that the PNN has the following characteristics:The RBFNN has some similarities with the PNN. However, the next layer of the RBFNN model layer is a linear output layer and its outputyr is expressed as
(24)yr=∑i=1Kωigix.And the next layer of the PNN model layer is the summation layer, and its outputyp is expressed as
(25)yp=ypj=1M=∑i=1Kigixj=1M.Among them,Kj in formula (24) is the number of nodes in the RBFNN mode layer. K in formula (25) is the number of nodes of the jth type of the PNN. Generally speaking, K≤∑i=1MKi and M represents the number of categories of the PNN.Annealing temperature control solves the optimization direction of the process optimal value and receives the inferior solution through the probabilityexp−Δf/Tk.The fish school algorithm based on simulated annealing (SAFSA) applies the SA algorithm to the local search in the later stage of the AFSA algorithm to help the AFSA algorithm jump out of the local optimal solution. The basic idea is that in the early stage of search, artificial fish first seeks global optimization through various behaviors of artificial fish in each generation of optimization. In the later stage of the search, by selecting the state with the highest food concentration in the current state and using the abovementioned simulated annealing operation to perform a local search, the artificial fish body in the best solution state can be accurately searched. Then, local optimization is realized and the approximate precision extreme value of the local optimal solution is finally obtained.The flow chart of SAFSA is shown in Figure4.Figure 4
Flow chart of SAFSA.We chose the basic artificial fish school algorithm (AFSA), simulated annealing-based fish school algorithm (SAFSA), and the abovementioned artificial fish school algorithm with the adaptive field of view and step size (ISAFA), and the optimization experiment is carried out through two classic functions.Each algorithm independently performs 20 optimization simulations in each experiment. The simulation functions F1 and F2 use the same parameters. The artificial fish school scale isM=100, the sensing distance is Dvisual=2.5, the step length is step=0.1, the maximum number of detections is try_number=100, and the crowding factor is δ=0.618. The number of iterations for each temperature in the simulated annealing algorithm is L=40, the initial temperature is T=30, the end temperature is Tend=0.001, and the cooling coefficient is k=0.95. The simulated annealing algorithm accepts a solution that is worse than the current solution with a certain probability, so it may jump out of the local optimal solution and reach the global optimal solution.And the test function F1 is(26)maxfx,y=sinxx×sinyyx≤10,y≤10.Figure5 shows the three-dimensional image of the F1 function. It can be seen in Figure 5 that the global optimal solution of the F1 function at (0,0) is 1 and there are many local optimal solutions around it but there is a significant difference between the global optimal solution and the local optimal solution.Figure 5
Three-dimensional image of F1 function.Figure6 shows the average optimization result of the F1 function.Figure 6
Average optimization result of the F1 function.It can be seen in Figure6 that all three algorithms can find the global optimal solution. The average optimization effect of the SAFSA algorithm is the best. The basic AFSA algorithm has the lowest initial accuracy. Through continuous iteration, it takes about 23 iterations to find the global optimal solution. The initial accuracy of IAFSA is similar to that of SAFSA, but in the later iterations, the searched optimal value is constantly close to the global optimal value and it takes about 36 iterations to reach the accurate global optimal solution. Table 1 shows the analysis of the average optimization result of the F1 function. The source of vocational education course evaluation data is simulated by algorithm, and the simulated annealing algorithm is used.Table 1
Analysis of the average optimization result of the F1 function.
Selection algorithmWorst valueThe optimal valueAverage valueVarianceAFSA0.9590.9990.9955.427IAFSA0.9951.0000.9990.658SAFSA1.0001.0001.0000.000It can be seen in Table1 that in 20 optimization experiments, the optimal value of the AFSA algorithm optimization result is greater than 0.99 and worse than 0.95. And the convergence speed is fast, which is obviously better than the previous two algorithms.(2) The test function F2 is(27)maxfx,y=sinx2+y2x2+y2+ecos2πx+cos2πy/2−2.718x≤5,y≤5.Figure7 is a three-dimensional image of the F2 function. It can be seen in Figure 7 that the function has many local maxima. In the optimization process, it is easy to fall into the local optimal solution and it is difficult to jump out and F2 has the global optimal solution 1 at (0,0). The simulation platform used in this paper is MATLAB, and the three-dimensional image of the F1 function is displayed through the construction of data, and then, the optimal solution is obtained.Figure 7
Three-dimensional image of the F2 function.Figure8 shows the average optimization result of the F2 function.Figure 8
Average optimization result of the F2 function.It can be seen in Figure8 that the SAFSA algorithm has the best optimization effect and the global optimal solution can be obtained in about 15 iterations. The optimal solution obtained by the IAFSA algorithm in the optimization process is gradually approaching the global optimal solution, but there are still errors in the end, because the optimization accuracy of this algorithm is not high. The optimal solution found by the AFSA algorithm is not the global optimal solution, because the optimal solution of the algorithm belongs to the local optimal solution, which means that the global optimal solution cannot be found in the optimization process. In the later iterations, the searched optimal value keeps approaching the global optimal value and it takes about 36 iterations to reach the accurate global optimal solution. Table 2 shows the analysis of the average optimization results of the F2 function.Table 2
Analysis of average optimization results of the F2 function.
Selection algorithmThe optimal valueWorst valueAccurate optimization success rateAFSA1.0000.80548.0%IAFSA1.0000.90889.0%SAFSA1.0001.000100.0%It can be seen in Table2 that in the 20 optimization experiments, the SAFSA can accurately obtain the global optimal value. The AFSA algorithm has a 48% chance that it will fall into the trap of local optimization and cannot escape.It can be seen from this that in order to prevent falling into the local optimum, the simulated annealing algorithm can be added to the artificial fish school algorithm to help the fish school algorithm jump out of the local optimum. The simulated annealing algorithm is derived from the principle of solid annealing. The solid is heated to a sufficient height and then slowly cooled. During heating, the internal particles of the solid become disordered with the increase in temperature and the internal energy increases, while the particles gradually cool when the temperature increases. It tends to be ordered, reaches equilibrium at each temperature, and finally reaches a certain stable state at normal temperature, the ground state, and the internal energy which is reduced to a minimum.


## 4. Prediction of Vocational Education Coursework Assessment Results Based on the Simulated Annealing Neural Network

The simulated annealing process is equivalent to shaking the tray horizontally, and high temperature means that the shaking amplitude is large, and the pellets will definitely jump out of any valley and fall into another valley. According to the analysis of system function requirements, the system adopts the B/S architecture as the development and design structure model. The system network structure is shown in Figure9. The system is composed of the database server, server server, candidate registration terminal, and test machine.Figure 9
System B/S structure diagram.The organizational structure of the examination area has a very important guiding role in the development and application of the system. Therefore, the connections between users involved in the application of this system are analyzed and permissions are set. The hybrid simulated annealing algorithm is a combination of the genetic algorithm and simulated annealing algorithm. In the hybrid simulated annealing algorithm, a large number of samples are used as possible solutions to a problem instead of a single sample as a possible solution to a problem. The concept of adaptation in the genetic algorithm is improved accordingly. According to the form of the examination organization organized by the examination institution, the user organization structure diagram related to the system is drawn, as shown in Figure10:Figure 10
System organization structure diagram.The system constructed in this paper can not only be used as a platform for the assessment of vocational education but also can predict the assessment of vocational education. Therefore, this article designs an experiment to evaluate the use effect of the vocational education coursework assessment and prediction platform; during the experiment, the random population’s evaluation of the system was counted in the form of a questionnaire. And the results are shown in Tables3 and 4.Table 3
The prediction effect evaluation of the vocational education coursework assessment result prediction system based on the simulated annealing neural network.
No.Predictive effectNo.Predictive effectNo.Predictive effect179.181483.002771.06275.401582.272871.54373.931672.602980.99470.211772.583072.86571.471879.603183.05676.081978.503270.87771.902083.433378.19875.402180.113476.01969.642282.533573.911075.312382.003673.201169.042482.423773.261273.112573.323874.811375.942680.183978.31Table 4
The assessment effect evaluation of the vocational education coursework assessment result prediction system based on the simulated annealing neural network.
NoTest effectNoTest effectNoTest effect177.721477.152790.37286.921575.862878.99375.681688.822980.67474.431788.773080.34573.931876.273179.50676.681973.253277.82787.422081.603376.17873.882179.753474.18986.852279.053580.341083.082390.903687.541177.552487.093786.671274.702573.133881.571386.412676.733974.10It can be seen from the above experimental analysis that the average evaluation test results for vocational education courses can reach more than 78; it can be seen that the vocational education coursework assessment result prediction system based on the simulated annealing neural network proposed in this paper meets the actual needs of the current vocational education and the system can be used in subsequent research for assessment evaluation and prediction evaluation.


## 5. Conclusion

As the aging of the population has accelerated significantly in recent years, there has been a shortage of skilled workers of varying degrees in some highly specialized positions in the production of some enterprises. The high-level technical blue-collar workers in all parts of the country are very scarce, which has seriously affected the speed of industrialization and the development space of enterprises themselves. It is necessary to strengthen school-enterprise cooperation, improve the student internship system of vocational colleges, promote the establishment of vocational education groups by enterprises and vocational colleges, and propose a series of targeted solutions to focus on the cultivation of talent skills. The establishment of the vocational education group can make the teaching goals of vocational colleges targeted and targeted to train the required skilled talents for enterprises. This not only solves the problem of student employment but also enables a steady stream of skilled talents to be injected into the production work of the enterprise. Enterprise employees explore new work ideas in their work practices and combine with colleges and universities to develop new production models to form an innovative enterprise development trend and promote the development of the entire social productivity. This paper constructs a vocational education coursework assessment result prediction system through the simulated annealing neural network algorithm, which provides theoretical support for improving the quality of teaching and assessment of vocational education. Future research on vocational education will be more in-depth.



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