# Evaluation of the Online and Offline Mixed Teaching Effect of MOOC Based upon the Deep Neural Network Model

Authors: Guangwei Wang; Xiaomei Chen
Journal: Wireless Communications and Mobile Computing
 (2022)

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## Abstract

This article is dedicated to discussing the online and offline mixed teaching evaluation of MOOC based on deep neural networks. Deep neural networks are an important means to solve various problems in various fields. It can evaluate the teaching attitude of teachers, the teaching content in the classroom, the teacher’s narrative ability, the teaching methods used by the teachers, and whether the teaching methods are rigorous. And it can train on a large number of datasets evaluated by students on a certain course and get results. This article first explains the advantages of the neural network model and explains the reasons for the emergence of MOOCs and the mixing with traditional classrooms. It also explains some deep neural network (DNN) models and algorithms, such as BP neural network model and algorithms. This model uses backpropagation. When there is an error between the output sample of the neural network and the target sample, the error can be backpropagated to adjust the threshold and weight to make the error reach the minimum. The algorithm steps include forward propagation and backpropagation and are substituted into the gradient descent method to obtain the weight change of the output layer and the hidden layer. It also explains the Gaussian model in DNNs. The given training data vector in the Gaussian mixture model and the configuration of GMM are used for expectation maximization training using an iterative algorithm, and the unsupervised clustering accuracy ACC is applied to evaluate its performance. Use pictures to describe the mixed-mode teaching mode in the MOOC environment. It is necessary to consider teaching practice conditions, time, location, curriculum resources, teaching methods and means, etc. It can cultivate students’ spatial imagination, engineering consciousness, creative design ability, drawing hand-made ability, and logical thinking abilities. It enables teachers to accept the fair and just evaluation of students. Finally, this article discusses the parallelization and optimization of GPU-based DNN models, splits the DNN models, and combines different models to calculate weight parameters. This article combines model training and data training in parallel to increase the processing speed under the same amount of data, increase the batch, increase the accuracy, and reduce the training shock. It can be concluded that its DNN model has greatly improved the training effect performance of the MOOC online and offline mixed course effect dataset. The calculation time is shortened, the convergence speed is accelerated, the accuracy rate is improved, and the acceleration ratio is increased, which compares with the same period of the previous year increase of more than 37.37%. The accuracy has increased, comparing with the same period of the previous year, an increase of more than 12.34%.


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## Body

## 1. Introduction

### 1.1. Background

DNN models have made continuous breakthroughs in various fields, and they are also accompanied by shadows in life, such as artificial intelligence. There is no shortage of participation of DNN models. Whether it is machine learning or pattern recognition, when people use primitive technology to process data, they will always suffer great limitations due to the complexity and size of the data. At this time, the arrival of the DNN model is undoubtedly a long-term drought. It can express the complexity and abstract state of data with extremely simplified nonlinearity and turn it into a quantity that can be understood by the machine. In the era of educational informationization, traditional classroom education models and pure online learning have their own shortcomings, and MOOCs have emerged from this. It can improve its teaching links. It has a wealth of teaching concepts, well-designed teaching links, high-quality teaching resources, effective learning models, and a strong teaching staff shining in the field of education. However, it also has certain shortcomings, such as students tend to lose self-discipline, miss classes, and fail to complete homework. Therefore, the MOOC and the classroom are mixed online and offline to make up for the shortcomings and limitations. When evaluating the teaching effect, there are many factors of teaching quality itself, and they bring different degrees of influence. In the era of DNN models, although there will be certain errors in the evaluation, it is more to optimize the evaluation model. It uses nonlinear mapping and dataset calculations to perform predictive analysis and calculations on its data to obtain the results. The optimized neural network model mentioned in this article will also improve the general DNN model while processing data. It uses parallel mode to improve accuracy and speed up convergence.


### 1.2. Significance

The purpose of this article is to study the application of DNNs in the evaluation of online and offline mixed teaching effects. Through the backpropagation network, the weights and thresholds of the network are continuously optimized, and the teaching mode is designed to cultivate students’ good spatial imagination, engineering awareness, creative design ability, drawing ability, and logical thinking ability, using Gaussian mixture model to fit the function to maximize the expectation. This paper takes the 2016 to 2020 online and offline MOOC evaluation data collected by the educational administration system of a university as a sample, using caffe and optimized parallel dual GPU model to process data and train. The three datasets were trained 120000 times, 8000 times, and 4000 times, respectively, which proved that the optimized DNN model is indeed superior in performance. It has better guiding significance in realizing large-scale data processing. The results of this experiment have a certain guiding role in the evaluation of the online and offline mixed teaching effect of MOOC and classroom with the DNN model, and it has both theoretical and practical meanings.


### 1.3. Related Work

The application of DNN models in various fields has extremely optimistic development prospects. All fields have never stopped the research and exploration of neural networks, to have a brighter development prospect for DNN models. Albericio observed that most of the calculations performed by DNNs are essentially invalid. Because they involve multiplication, one of the inputs is zero. This observation inspired Cnvlutin (CNV). This is a value-based hardware acceleration method that eliminates most of these invalid operations and improves the performance and energy of the most advanced accelerators without loss of accuracy [1]. Xue has developed a multitask deep convolutional network that simultaneously detects the existence of the target and the geometric properties (position and direction) of the target relative to the region of interest. Secondly, the structured visual inspection is carried out by using the cyclic neuron layer [2]. Gong proposed a multiobjective sparse feature learning model based on autoencoder. The parameters of the model are learned by optimizing the reconstruction error and the sparsity of hidden units at the same time, so as to automatically find a reasonable compromise between them. Based on the multiobjective evolutionary algorithm, he designed a multiobjective induction learning program for the model [3]. Cai proposed a new nonlinear and non-Gaussian process monitoring method using weighted KICA (WKICA) based on Gaussian mixture model (GMM). In particular, in WKICA, he first used GMM to estimate the probability of KIC extracted by KICA. Then, according to the estimated probability, the important KICs that reflect the main process changes are distinguished, and a larger weight is assigned to capture important information during online fault detection [4]. To construct a circular linear joint distribution containing appropriate correlation items, Parui uses a semiwrapped Gaussian distribution. In addition, he constructed this joint distributed hybrid model (called SWGMM) [5]. Then, Chen adopted a dual parallel method in the RF training process. Based on PRF’s parallel training process and the dependence of elastic distributed datasets, he created a task-directed acyclic graph (DAG) (RDD) object. Then, he calls different task schedulers for the tasks in the DAG [6]. Carrasco said that kernel-based formulas can be directly derived by applying duality theory. Each twin problem has the same SVR method structure, allowing fast training using efficient optimization algorithms. His duality theory provides a general formula for the twin SVR. Compared with the original TSVR, it brings better performance [7]. Although the research he cited has reached certain guiding conclusions, there are unavoidable errors that need to be further improved.


### 1.4. Innovation

This article analyzes the application of DNNs in the evaluation of MOOC’s online and offline mixed-mode teaching effects. Combining the BP neural network model and algorithm gradient descent method, this article discusses the DNN and the online and offline mixed-mode teaching of MOOC. The DNN model of GPU is optimized in parallel, and the DNN model of the dual GPU is obtained. Combining model training and data training to improve the performance of DNNs in parallel, the error formed by the test results is very small. Moreover, it has fast convergence and high accuracy, which can be well applied in dataset training.


## 1.1. Background

DNN models have made continuous breakthroughs in various fields, and they are also accompanied by shadows in life, such as artificial intelligence. There is no shortage of participation of DNN models. Whether it is machine learning or pattern recognition, when people use primitive technology to process data, they will always suffer great limitations due to the complexity and size of the data. At this time, the arrival of the DNN model is undoubtedly a long-term drought. It can express the complexity and abstract state of data with extremely simplified nonlinearity and turn it into a quantity that can be understood by the machine. In the era of educational informationization, traditional classroom education models and pure online learning have their own shortcomings, and MOOCs have emerged from this. It can improve its teaching links. It has a wealth of teaching concepts, well-designed teaching links, high-quality teaching resources, effective learning models, and a strong teaching staff shining in the field of education. However, it also has certain shortcomings, such as students tend to lose self-discipline, miss classes, and fail to complete homework. Therefore, the MOOC and the classroom are mixed online and offline to make up for the shortcomings and limitations. When evaluating the teaching effect, there are many factors of teaching quality itself, and they bring different degrees of influence. In the era of DNN models, although there will be certain errors in the evaluation, it is more to optimize the evaluation model. It uses nonlinear mapping and dataset calculations to perform predictive analysis and calculations on its data to obtain the results. The optimized neural network model mentioned in this article will also improve the general DNN model while processing data. It uses parallel mode to improve accuracy and speed up convergence.


## 1.2. Significance

The purpose of this article is to study the application of DNNs in the evaluation of online and offline mixed teaching effects. Through the backpropagation network, the weights and thresholds of the network are continuously optimized, and the teaching mode is designed to cultivate students’ good spatial imagination, engineering awareness, creative design ability, drawing ability, and logical thinking ability, using Gaussian mixture model to fit the function to maximize the expectation. This paper takes the 2016 to 2020 online and offline MOOC evaluation data collected by the educational administration system of a university as a sample, using caffe and optimized parallel dual GPU model to process data and train. The three datasets were trained 120000 times, 8000 times, and 4000 times, respectively, which proved that the optimized DNN model is indeed superior in performance. It has better guiding significance in realizing large-scale data processing. The results of this experiment have a certain guiding role in the evaluation of the online and offline mixed teaching effect of MOOC and classroom with the DNN model, and it has both theoretical and practical meanings.


## 1.3. Related Work

The application of DNN models in various fields has extremely optimistic development prospects. All fields have never stopped the research and exploration of neural networks, to have a brighter development prospect for DNN models. Albericio observed that most of the calculations performed by DNNs are essentially invalid. Because they involve multiplication, one of the inputs is zero. This observation inspired Cnvlutin (CNV). This is a value-based hardware acceleration method that eliminates most of these invalid operations and improves the performance and energy of the most advanced accelerators without loss of accuracy [1]. Xue has developed a multitask deep convolutional network that simultaneously detects the existence of the target and the geometric properties (position and direction) of the target relative to the region of interest. Secondly, the structured visual inspection is carried out by using the cyclic neuron layer [2]. Gong proposed a multiobjective sparse feature learning model based on autoencoder. The parameters of the model are learned by optimizing the reconstruction error and the sparsity of hidden units at the same time, so as to automatically find a reasonable compromise between them. Based on the multiobjective evolutionary algorithm, he designed a multiobjective induction learning program for the model [3]. Cai proposed a new nonlinear and non-Gaussian process monitoring method using weighted KICA (WKICA) based on Gaussian mixture model (GMM). In particular, in WKICA, he first used GMM to estimate the probability of KIC extracted by KICA. Then, according to the estimated probability, the important KICs that reflect the main process changes are distinguished, and a larger weight is assigned to capture important information during online fault detection [4]. To construct a circular linear joint distribution containing appropriate correlation items, Parui uses a semiwrapped Gaussian distribution. In addition, he constructed this joint distributed hybrid model (called SWGMM) [5]. Then, Chen adopted a dual parallel method in the RF training process. Based on PRF’s parallel training process and the dependence of elastic distributed datasets, he created a task-directed acyclic graph (DAG) (RDD) object. Then, he calls different task schedulers for the tasks in the DAG [6]. Carrasco said that kernel-based formulas can be directly derived by applying duality theory. Each twin problem has the same SVR method structure, allowing fast training using efficient optimization algorithms. His duality theory provides a general formula for the twin SVR. Compared with the original TSVR, it brings better performance [7]. Although the research he cited has reached certain guiding conclusions, there are unavoidable errors that need to be further improved.


## 1.4. Innovation

This article analyzes the application of DNNs in the evaluation of MOOC’s online and offline mixed-mode teaching effects. Combining the BP neural network model and algorithm gradient descent method, this article discusses the DNN and the online and offline mixed-mode teaching of MOOC. The DNN model of GPU is optimized in parallel, and the DNN model of the dual GPU is obtained. Combining model training and data training to improve the performance of DNNs in parallel, the error formed by the test results is very small. Moreover, it has fast convergence and high accuracy, which can be well applied in dataset training.


## 2. DNN Model and Algorithm and Mixed Curriculum Design

### 2.1. BP Neural Network Model and Algorithm

#### 2.1.1. BP Neural Network Model

BP neural network is a backpropagation network. Its application scenarios include function approximation, pattern recognition, classification, and data compression. It is a multilayer prefeedback neural network that can be trained according to the error reversal propagation algorithm. It can learn and store complex mapping relationships without expounding the mathematical equations of the input-output model mapping relationship in advance [8]. It can apply the steepest descent method and backpropagation to continuously optimize the weights and thresholds of the network so that the neural network reaches the least square error [9]. The topological structure of the BP neural network model is shown in Figure 1.Figure 1
BP neural network model.It can be seen that the BP neural network structure includes an input layer, a hidden layer, and an output layer.


#### 2.1.2. BP Neural Network Algorithm and Learning Rules

Suppose the input samples areA1,A2,⋯,An, the target samples are B1,B2,⋯,Bn, and the output layer of the BP neural network is O1,O2,⋯,On. When there is an error between the output sample of the neural network and the target sample, the error can be backpropagated to adjust the threshold and weight. It makes the error reach the minimum, and its algorithm steps include forward propagation and backpropagation [10].According to the forward transfer of its information, let the input layer bea1 to at, the hidden layer be b1x, and the output layer be b2j. It can be seen that the output of the xth neuron in the hidden layer is
(1)b1x=f1∑y=1twlxyay+c1x,x=1,2,⋯,s1.The difference between the output of thejth neuron in the output layer and the defined function is
(2)b2j=f2∑x=1s1w2jxax+c1j,j=1,2,⋯,s2,EW,B=12∑j=1s2Tj−b2j2.Then, substitute the gradient descent method to obtain the weight change of the output layer and the hidden layer and the backpropagation of the error.w2 represents the weight of the output layer, and the weight of the output layer changes as
(3)Δw2jx=−λμEμw2jx=−λμEμb2j⋅μb2jμw2jx=−λTj−b2jf2′b1x=λμjx.Among them,(4)μjx=Tj−b2jf2′=ejf2′,ej=Tj−b2j,Δc2jx=−λμEμc2jx=−λμEμb2j⋅μb2jμc2jx=−λTj−b2jf2′=λμjx.The weight change input to the hidden layer is(5)Δw1jx=−λμEμw12jx1=λμjxay,(6)Δcx=λμxy.The activation function forf1 being a logarithmic sigmoid is
(7)f1k=11+e−k,f1f1′k=0−e−k−11+e−k2=e−k1+e−k2=f1k1−f1k.Forf2 is a linear activation function,
(8)f2′k=k′=1.


#### 2.1.3. Improvement of BP Algorithm

Use the additional momentum factor to adjust the BP neural network algorithm, letW be the weight matrix, A is the input vector of the neural network, α is the momentum coefficient of the algorithm, and α∈0,1. It is known that the direction of the standard BP neural network algorithm to adjust the weight is only the direction of the error gradient at time T. The previous error value has not been adjusted. This situation will cause its instability, oscillation, and slow convergence. Using an additional momentum factor to adjust, it can reduce the oscillation and speed up convergence [11]:
(9)ΔWT=λμA+αΔWT−1.KnowingΔw=−λμE/μw, the learning rate is positively correlated with the convergence speed, but it will cause oscillations and even divergence. To obtain stable convergence, the learning rate will become slow, and we can improve the error function. It is known that E=1/2∑a=1a∑j=1mlngja−pja2; as the number of learning increases, the value of gja−pja will gradually become smaller. This situation will slow down the approximation speed of the function. Formula (12) is the penalty term.
(10)E=12∑a=1a∑j=1Mln1+gja−pja2,(11)E=12∑a=1a∑j=1mgja−pja2+12∑a=1a∑j=1mgja−pja2⋅∑y=1HhRy−0.52,(12)E=12∑a=1a∑j=1mgja−pja2+aw,aw=βt∑xywxyt.


### 2.2. Design of Online and Offline Hybrid Teaching Mode in MOOC Environment

To design a teaching model, it is necessary to formulate goals in the cognitive domain, motor skill domain, and emotional domain to cultivate good spatial imagination, engineering awareness, creative design ability, drawing and handwriting ability, and logical thinking ability for students [12]. It needs to consider teaching practice conditions, time, location, curriculum resources, teaching methods and means, etc. It includes practice in class and evaluation after class. Flow is shown in Figure 2.Figure 2
Course operation procedures.Then, carry out the design of teaching evaluation, allowing students to participate in the evaluation of teachers anonymously. The school sets the dimensions and evaluation conditions. Although this method is single, the MOOC is extensive. It can be open and fair while requiring teaching quality and ability [13]. It can be anonymous or remove the highest and lowest evaluations and try to use the balance value to calculate. The mixed teaching mode in the MOOC environment is shown in Figure 3.Figure 3
The mixed teaching model in the MOOC environment.This design is based on the teaching survey function of a certain platform and uses a questionnaire survey mode to collect students’ opinions. The premise is that the authenticity of students’ evaluations of teachers is high, and no false evaluations are made. It advocates going out into the real world, avoiding shortcomings and accepting suggestions from students, thereby improving the quality of teaching [14].


### 2.3. Clustering Algorithm under DNN-Gaussian Mixture Model

Leta represent the Gaussian mixture model data vector of d-dimensional continuous values, I represent the number of models, and Gi be the probability of being selected for the i-th type in the sample set [15]. In this case, ∑i=1Iαi∘φaθi is the Gaussian distribution density, representing the i-th Gaussian mixture model component, and its probability distribution model is as follows:
(13)Paθ=∑i=1IGiφaθ,φaθi=12πσ2exp−a−μi22σi2.In this formula,θi=μi,σi2 and μi represent the average value, and σi2 represents the variance. Its mean vector, variance matrix, and mixed weights are unified to form parameters. This process is completed by a complete Gaussian mixture model, and the parameters are expressed as
(14)θ=μi,σi2,Gi,i=1,2,⋯,I.The given training data vector in the Gaussian mixture model and the configuration of GMM are used to perform expectation maximization training on it using an iterative algorithm. Estimate the parameters of GMM and make the distribution of the feature vector trained by the Gaussian mixture model the best match under certain circumstances [16]. For M training vectors A=a1,a2,⋯,aM, assuming that each vector is independent of each other, the GMM likelihood can be expressed as
(15)P=Aθ=∏m=1MP=Amθ.The value of its likelihood function is calculated by the logarithm, and the expression of the likelihood function that maximizes its logarithm is as follows:(16)maxlnpAθ=∑m=1Mln∑i=1IGiφAθi.The formula is a nonlinear function of the parameters of the Gaussian mixture model. It is impossible to directly maximize the expectation of the function. Iterative method is used to maximize its likelihood to obtain an expectation maximization algorithm under a certain state [17]. Then, apply the EM algorithm, the principle of which is to estimate a new parameter from the original parameter, let its parameter be θ¯, and its value is greater than the original parameter. The new parameters can be used as the initial parameters of the next iteration, and this operation is continued to be repeated to guide the acquisition of the best convergence threshold. This initial parameter can be estimated through some form of binary vector quantization. The number of clusters that has been set initially in the cluster is I, and the introduced variable represents the probability that the m-th training data comes from the i-th Gaussian mixture model. The probability can be calculated as
(17)Priam,θ=Giφxθi∑m=1IGiφaθi.In each iteration of the EM algorithm, in order to ensure the monotonic increase of the parameter likelihood value, the likelihood value can be recalculated using the following formula:(18)Gi¯=1M∑m=1MPriam,θ,μi¯=∑m=1MPriam,θam∑m=1MPriam,θ,σi2¯=∑m=1MPriam,θam∑m=1MPriam,θ−μi2¯.In this formula, its iterative algorithm points to any element in the vector, respectively. Cluster analysis algorithms play an important role in various fields. Its effectiveness is formed by three different standards, namely, internal, external, and relative. The internal evaluation criterion is that the structure of the clustering algorithm and the performance characteristics of the dataset are regarded as the evaluation result. The data structure is in an unknown state. The relative evaluation criteria of clustering algorithms are formed by a variety of structures and manifested in different algorithms. The external clustering algorithm evaluation criterion is to select the best dataset clustering method through prior information, such as the number of clusters in the best state. The internal clustering algorithm can use the best clustering algorithm and dataset without additional information [18]. Cross-validation is widely used in supervised learning, and cross-validation can also be used for accuracy prediction in unsupervised learning. Obtain a Gaussian mixture model from a certain set of data, and calculate the number of clusters i. This article uses unsupervised learning evaluation criteria and other algorithm tests and uses the unsupervised clustering accuracy ACC to evaluate its performance:
(19)ACC=maxn∑k=1m1lk=nwkM.In this formula,M represents the number of sample data, lk represents the true label of the k-th data, the predicted value of the k-th data generated by the clustering algorithm is wk, and n represents the prediction area between the cluster and the label.


## 2.1. BP Neural Network Model and Algorithm

### 2.1.1. BP Neural Network Model

BP neural network is a backpropagation network. Its application scenarios include function approximation, pattern recognition, classification, and data compression. It is a multilayer prefeedback neural network that can be trained according to the error reversal propagation algorithm. It can learn and store complex mapping relationships without expounding the mathematical equations of the input-output model mapping relationship in advance [8]. It can apply the steepest descent method and backpropagation to continuously optimize the weights and thresholds of the network so that the neural network reaches the least square error [9]. The topological structure of the BP neural network model is shown in Figure 1.Figure 1
BP neural network model.It can be seen that the BP neural network structure includes an input layer, a hidden layer, and an output layer.


### 2.1.2. BP Neural Network Algorithm and Learning Rules

Suppose the input samples areA1,A2,⋯,An, the target samples are B1,B2,⋯,Bn, and the output layer of the BP neural network is O1,O2,⋯,On. When there is an error between the output sample of the neural network and the target sample, the error can be backpropagated to adjust the threshold and weight. It makes the error reach the minimum, and its algorithm steps include forward propagation and backpropagation [10].According to the forward transfer of its information, let the input layer bea1 to at, the hidden layer be b1x, and the output layer be b2j. It can be seen that the output of the xth neuron in the hidden layer is
(1)b1x=f1∑y=1twlxyay+c1x,x=1,2,⋯,s1.The difference between the output of thejth neuron in the output layer and the defined function is
(2)b2j=f2∑x=1s1w2jxax+c1j,j=1,2,⋯,s2,EW,B=12∑j=1s2Tj−b2j2.Then, substitute the gradient descent method to obtain the weight change of the output layer and the hidden layer and the backpropagation of the error.w2 represents the weight of the output layer, and the weight of the output layer changes as
(3)Δw2jx=−λμEμw2jx=−λμEμb2j⋅μb2jμw2jx=−λTj−b2jf2′b1x=λμjx.Among them,(4)μjx=Tj−b2jf2′=ejf2′,ej=Tj−b2j,Δc2jx=−λμEμc2jx=−λμEμb2j⋅μb2jμc2jx=−λTj−b2jf2′=λμjx.The weight change input to the hidden layer is(5)Δw1jx=−λμEμw12jx1=λμjxay,(6)Δcx=λμxy.The activation function forf1 being a logarithmic sigmoid is
(7)f1k=11+e−k,f1f1′k=0−e−k−11+e−k2=e−k1+e−k2=f1k1−f1k.Forf2 is a linear activation function,
(8)f2′k=k′=1.


### 2.1.3. Improvement of BP Algorithm

Use the additional momentum factor to adjust the BP neural network algorithm, letW be the weight matrix, A is the input vector of the neural network, α is the momentum coefficient of the algorithm, and α∈0,1. It is known that the direction of the standard BP neural network algorithm to adjust the weight is only the direction of the error gradient at time T. The previous error value has not been adjusted. This situation will cause its instability, oscillation, and slow convergence. Using an additional momentum factor to adjust, it can reduce the oscillation and speed up convergence [11]:
(9)ΔWT=λμA+αΔWT−1.KnowingΔw=−λμE/μw, the learning rate is positively correlated with the convergence speed, but it will cause oscillations and even divergence. To obtain stable convergence, the learning rate will become slow, and we can improve the error function. It is known that E=1/2∑a=1a∑j=1mlngja−pja2; as the number of learning increases, the value of gja−pja will gradually become smaller. This situation will slow down the approximation speed of the function. Formula (12) is the penalty term.
(10)E=12∑a=1a∑j=1Mln1+gja−pja2,(11)E=12∑a=1a∑j=1mgja−pja2+12∑a=1a∑j=1mgja−pja2⋅∑y=1HhRy−0.52,(12)E=12∑a=1a∑j=1mgja−pja2+aw,aw=βt∑xywxyt.


## 2.1.1. BP Neural Network Model

BP neural network is a backpropagation network. Its application scenarios include function approximation, pattern recognition, classification, and data compression. It is a multilayer prefeedback neural network that can be trained according to the error reversal propagation algorithm. It can learn and store complex mapping relationships without expounding the mathematical equations of the input-output model mapping relationship in advance [8]. It can apply the steepest descent method and backpropagation to continuously optimize the weights and thresholds of the network so that the neural network reaches the least square error [9]. The topological structure of the BP neural network model is shown in Figure 1.Figure 1
BP neural network model.It can be seen that the BP neural network structure includes an input layer, a hidden layer, and an output layer.


## 2.1.2. BP Neural Network Algorithm and Learning Rules

Suppose the input samples areA1,A2,⋯,An, the target samples are B1,B2,⋯,Bn, and the output layer of the BP neural network is O1,O2,⋯,On. When there is an error between the output sample of the neural network and the target sample, the error can be backpropagated to adjust the threshold and weight. It makes the error reach the minimum, and its algorithm steps include forward propagation and backpropagation [10].According to the forward transfer of its information, let the input layer bea1 to at, the hidden layer be b1x, and the output layer be b2j. It can be seen that the output of the xth neuron in the hidden layer is
(1)b1x=f1∑y=1twlxyay+c1x,x=1,2,⋯,s1.The difference between the output of thejth neuron in the output layer and the defined function is
(2)b2j=f2∑x=1s1w2jxax+c1j,j=1,2,⋯,s2,EW,B=12∑j=1s2Tj−b2j2.Then, substitute the gradient descent method to obtain the weight change of the output layer and the hidden layer and the backpropagation of the error.w2 represents the weight of the output layer, and the weight of the output layer changes as
(3)Δw2jx=−λμEμw2jx=−λμEμb2j⋅μb2jμw2jx=−λTj−b2jf2′b1x=λμjx.Among them,(4)μjx=Tj−b2jf2′=ejf2′,ej=Tj−b2j,Δc2jx=−λμEμc2jx=−λμEμb2j⋅μb2jμc2jx=−λTj−b2jf2′=λμjx.The weight change input to the hidden layer is(5)Δw1jx=−λμEμw12jx1=λμjxay,(6)Δcx=λμxy.The activation function forf1 being a logarithmic sigmoid is
(7)f1k=11+e−k,f1f1′k=0−e−k−11+e−k2=e−k1+e−k2=f1k1−f1k.Forf2 is a linear activation function,
(8)f2′k=k′=1.


## 2.1.3. Improvement of BP Algorithm

Use the additional momentum factor to adjust the BP neural network algorithm, letW be the weight matrix, A is the input vector of the neural network, α is the momentum coefficient of the algorithm, and α∈0,1. It is known that the direction of the standard BP neural network algorithm to adjust the weight is only the direction of the error gradient at time T. The previous error value has not been adjusted. This situation will cause its instability, oscillation, and slow convergence. Using an additional momentum factor to adjust, it can reduce the oscillation and speed up convergence [11]:
(9)ΔWT=λμA+αΔWT−1.KnowingΔw=−λμE/μw, the learning rate is positively correlated with the convergence speed, but it will cause oscillations and even divergence. To obtain stable convergence, the learning rate will become slow, and we can improve the error function. It is known that E=1/2∑a=1a∑j=1mlngja−pja2; as the number of learning increases, the value of gja−pja will gradually become smaller. This situation will slow down the approximation speed of the function. Formula (12) is the penalty term.
(10)E=12∑a=1a∑j=1Mln1+gja−pja2,(11)E=12∑a=1a∑j=1mgja−pja2+12∑a=1a∑j=1mgja−pja2⋅∑y=1HhRy−0.52,(12)E=12∑a=1a∑j=1mgja−pja2+aw,aw=βt∑xywxyt.


## 2.2. Design of Online and Offline Hybrid Teaching Mode in MOOC Environment

To design a teaching model, it is necessary to formulate goals in the cognitive domain, motor skill domain, and emotional domain to cultivate good spatial imagination, engineering awareness, creative design ability, drawing and handwriting ability, and logical thinking ability for students [12]. It needs to consider teaching practice conditions, time, location, curriculum resources, teaching methods and means, etc. It includes practice in class and evaluation after class. Flow is shown in Figure 2.Figure 2
Course operation procedures.Then, carry out the design of teaching evaluation, allowing students to participate in the evaluation of teachers anonymously. The school sets the dimensions and evaluation conditions. Although this method is single, the MOOC is extensive. It can be open and fair while requiring teaching quality and ability [13]. It can be anonymous or remove the highest and lowest evaluations and try to use the balance value to calculate. The mixed teaching mode in the MOOC environment is shown in Figure 3.Figure 3
The mixed teaching model in the MOOC environment.This design is based on the teaching survey function of a certain platform and uses a questionnaire survey mode to collect students’ opinions. The premise is that the authenticity of students’ evaluations of teachers is high, and no false evaluations are made. It advocates going out into the real world, avoiding shortcomings and accepting suggestions from students, thereby improving the quality of teaching [14].


## 2.3. Clustering Algorithm under DNN-Gaussian Mixture Model

Leta represent the Gaussian mixture model data vector of d-dimensional continuous values, I represent the number of models, and Gi be the probability of being selected for the i-th type in the sample set [15]. In this case, ∑i=1Iαi∘φaθi is the Gaussian distribution density, representing the i-th Gaussian mixture model component, and its probability distribution model is as follows:
(13)Paθ=∑i=1IGiφaθ,φaθi=12πσ2exp−a−μi22σi2.In this formula,θi=μi,σi2 and μi represent the average value, and σi2 represents the variance. Its mean vector, variance matrix, and mixed weights are unified to form parameters. This process is completed by a complete Gaussian mixture model, and the parameters are expressed as
(14)θ=μi,σi2,Gi,i=1,2,⋯,I.The given training data vector in the Gaussian mixture model and the configuration of GMM are used to perform expectation maximization training on it using an iterative algorithm. Estimate the parameters of GMM and make the distribution of the feature vector trained by the Gaussian mixture model the best match under certain circumstances [16]. For M training vectors A=a1,a2,⋯,aM, assuming that each vector is independent of each other, the GMM likelihood can be expressed as
(15)P=Aθ=∏m=1MP=Amθ.The value of its likelihood function is calculated by the logarithm, and the expression of the likelihood function that maximizes its logarithm is as follows:(16)maxlnpAθ=∑m=1Mln∑i=1IGiφAθi.The formula is a nonlinear function of the parameters of the Gaussian mixture model. It is impossible to directly maximize the expectation of the function. Iterative method is used to maximize its likelihood to obtain an expectation maximization algorithm under a certain state [17]. Then, apply the EM algorithm, the principle of which is to estimate a new parameter from the original parameter, let its parameter be θ¯, and its value is greater than the original parameter. The new parameters can be used as the initial parameters of the next iteration, and this operation is continued to be repeated to guide the acquisition of the best convergence threshold. This initial parameter can be estimated through some form of binary vector quantization. The number of clusters that has been set initially in the cluster is I, and the introduced variable represents the probability that the m-th training data comes from the i-th Gaussian mixture model. The probability can be calculated as
(17)Priam,θ=Giφxθi∑m=1IGiφaθi.In each iteration of the EM algorithm, in order to ensure the monotonic increase of the parameter likelihood value, the likelihood value can be recalculated using the following formula:(18)Gi¯=1M∑m=1MPriam,θ,μi¯=∑m=1MPriam,θam∑m=1MPriam,θ,σi2¯=∑m=1MPriam,θam∑m=1MPriam,θ−μi2¯.In this formula, its iterative algorithm points to any element in the vector, respectively. Cluster analysis algorithms play an important role in various fields. Its effectiveness is formed by three different standards, namely, internal, external, and relative. The internal evaluation criterion is that the structure of the clustering algorithm and the performance characteristics of the dataset are regarded as the evaluation result. The data structure is in an unknown state. The relative evaluation criteria of clustering algorithms are formed by a variety of structures and manifested in different algorithms. The external clustering algorithm evaluation criterion is to select the best dataset clustering method through prior information, such as the number of clusters in the best state. The internal clustering algorithm can use the best clustering algorithm and dataset without additional information [18]. Cross-validation is widely used in supervised learning, and cross-validation can also be used for accuracy prediction in unsupervised learning. Obtain a Gaussian mixture model from a certain set of data, and calculate the number of clusters i. This article uses unsupervised learning evaluation criteria and other algorithm tests and uses the unsupervised clustering accuracy ACC to evaluate its performance:
(19)ACC=maxn∑k=1m1lk=nwkM.In this formula,M represents the number of sample data, lk represents the true label of the k-th data, the predicted value of the k-th data generated by the clustering algorithm is wk, and n represents the prediction area between the cluster and the label.


## 3. The Parallelism of GPU-Based DNN Model and Application in MOOC Mixed Teaching Effect Dataset

### 3.1. Parallel and Optimization of GPU-Based DNN Models

#### 3.1.1. Parallel Optimization Design Ideas

The pipelined BP algorithm is based on the division of training layers. The GPU in this algorithm is to define prebuf, curbef, and resbuf; curbef stores the current training dataset, and prebuf stores the dataset required for the next iteration. The previous iteration layer calculates, stores the calculation result in resbuf, and plays the role of transferring data [19]. Its model can be layered into multiple GPUs. When the dataset faced by DNNs is getting larger and larger, only the model cannot satisfy its training, and the data needs to be trained in batches. At this point, it can use minibatch to divide the training data to complete parallel training. The process is shown in Figure 4.Figure 4
Minibatch parallel training data.The data is in the training process; it is best to reduce the total communication overhead and parameter exchange period. It can reversely distribute the data trained by the GPU when shards are merged [20].


#### 3.1.2. Parallel Optimization of Multiple Models

First, the DNN model can be split. When it is difficult to concentrate training on big data, it can be divided into small model blocks. Combining different models to calculate the weight parameters, in this state, the GPU will generate a relative calculation time, and the GPU only runs a part of the model. The entire system will mobilize the model operation engine to train the model. In this process, the data needs to be consistent to prevent deviations in the final results. Compared with data parallelism, model parallelism will produce a lot of consumption, resulting in poor acceleration effect [21]. Model and data parallelism have certain limitations on the GPU, and the learning rate needs to be reduced to achieve a higher accuracy rate. Therefore, this article mixes the two training methods, as shown in Figure 5.Figure 5
Hybrid design of two parallel methods.In a DNN, each level has a specific connection method. In general, full connections only exist in certain computational neural layers in the neural network structure, and there are parts that are independent of other connections. Training can be split on multiple GPUs. This behavior can reduce the forward and backward propagation time of training .


#### 3.1.3. Minibatch Parallel Optimization Training

The three-level thread structure of GPU has a large number of computing cores, which is suitable for intensive training such as DNN models. The use of GPU training can improve training efficiency. For the wear resistance of DNNs, the characteristics can be optimized. DNNs favor gradient descent algorithms for learning. It can be seen that when the gradient of the batch data is calculated, the network parameters need to be updated. Using batch processing will increase the GPU running speed. Using its characteristics of good floating point operations, the matrix and vector algorithms of DNNs are transformed into matrix and matrix algorithms. When selecting batch data, select the set composed of training data randomly. DNNs do not require ordered data, so they can meet their characteristics. When training GPU, we follow the principle of double GPU twice as single GPU. Under certain circumstances, increasing the data batch will form certain advantages. The graphics memory and computing core on the GPU can be used to improve the efficiency of convolution calculations. The decrease in the number of data iterations during training will increase the processing speed for the same amount of data, resulting in larger batches and higher accuracy, resulting in smaller training shocks. The premise is that the batch size is too large to reduce the test performance. It can be seen that this improved parallel method can improve the performance during model training.


### 3.2. Experimental Preparation and the Optimized Model’s Effect on Big Data Processing

This article uses GCC compiler to compile C++ code, NVCC compiler to compile GPU code, the number of single GPU graphics card cores is 2880, the basic frequency is 710 MHz, the dynamic speedup frequency is 876 MHz, and the memory capacity is 12 GB. Obtain online and offline MOOC evaluation data from 2016 to 2020 from the educational administration system of a university. Dataset format (a1,a2,⋯,a22,b), a total of 20880 sample data. Remove the higher and lower ones and those that are inconsistent with the facts, leaving 16962 sample data. This experiment is improved based on caffe and compared with the improved caffe. The GPU library for accelerating machine learning with cuDNN mainly highlights the performance and memory overhead. In this experiment, cuDNN is used to accelerate the GPU matrix. After normalization of some samples, Tables 1 and 2 can be obtained.Table 1
Example of data after normalized sample 1.
Index/sample12345678a10.830.690.850.890.790.970.960.82a20.940.930.680.710.910.720.710.85a30.960.910.720.990.740.960.740.92a40.680.990.920.750.840.710.750.92a50.740.990.870.990.790.940.780.82a60.860.70.920.690.810.950.920.7a70.90.830.810.710.730.880.70.84a80.860.870.690.750.850.810.950.88a90.920.810.980.990.960.880.750.67a100.990.850.790.90.730.730.70.94a110.840.730.820.730.720.810.940.77a120.790.750.790.80.730.880.980.71a130.810.790.910.980.790.860.950.94a140.80.790.930.970.880.680.810.96a150.770.80.950.870.680.720.780.81a160.80.870.740.810.90.980.870.9a170.880.890.750.90.680.820.920.92a180.990.830.690.940.690.670.670.92a190.860.670.930.870.680.770.850.76a200.850.820.970.890.750.850.940.85a210.930.760.990.70.920.950.70.86a220.980.730.670.780.70.940.870.96a230.910.70.780.760.850.980.90.8b0.920.730.740.860.840.790.70.79Table 2
Examples of data after normalized sample 2.
Index/sample12345678x10.810.890.70.760.970.840.760.86x20.860.760.710.90.850.850.950.96x30.70.80.950.680.710.880.960.92x40.980.950.840.870.930.770.80.99x50.810.880.690.750.730.760.850.75x60.690.830.920.950.830.870.730.86x70.90.690.990.90.890.990.870.87x80.830.880.780.940.90.80.840.85x90.830.740.970.680.930.830.70.79x100.840.830.940.840.710.840.810.8x110.690.930.980.720.950.850.760.94x120.730.930.720.90.820.690.980.8x130.670.730.950.890.790.670.760.83x140.840.970.710.850.980.820.90.76x150.960.670.990.740.790.960.870.78x160.920.750.830.890.750.760.70.68x170.730.860.90.880.850.70.710.86x180.750.820.820.710.940.70.820.96x190.690.710.70.70.890.670.960.87x200.830.980.720.670.970.810.830.73x210.960.850.860.910.910.990.950.82x220.930.950.940.710.680.830.680.99x230.860.910.990.90.840.880.860.91y0.950.790.750.90.790.870.870.8The experimental dataset is divided into three parts: A, B, and C, and the three parts have the same amount of data. These datasets have been preprocessed during the collection process and can be directly used for DNN learning. The test network structure is a comparative test based on the network structures of LeNet, CaffeNet, and GoogleNet. Use the A dataset for experiments on the LeNet network structure, while CaffeNet corresponds to B and GoogleNet corresponds to C. The CaffeNet network structure is shown in Figure6.Figure 6
CaffeNet network structure.Caffe does have advantages when dealing with small-scale datasets. However, in the case of large dataset processing, there is no better effect. Here, we select a large-scale MOOC online and offline mixed curriculum effect evaluation dataset and use the optimized DNN model for testing.


### 3.3. Experimental Test and Result Analysis

We will perform performance testing and comparison. In this experiment, 120000, 8000, and 4000 iterations of training were performed on the three datasets, respectively, and the training time comparison and training accuracy comparison of the improved caffe and improved caffe were compared. The test result is shown in Figure7.Figure 7
Comparison of the time and accuracy required to complete training before and after optimization.When testing the A group of data, it can be seen that with 120000 iterations, the time required for caffe to complete takes 19.8 seconds. The parallel solution based on the dual GPU model only takes 12.4 seconds, and its speedup ratio reaches 37.37%. The accuracy rate is also as high as 99.12%, a year-to-year increase of 12.34% in the DNN model training. In the iterative training process, this article will further analyze the accuracy and convergence of the algorithm on the A dataset for continuous DNN learning. The results are shown in Figure8.Figure 8
Comparison of accuracy and loss before and after optimization of A dataset training.The figure shows that the accuracy of parallel training rises faster and the convergence is faster, while the convergence and accuracy rise rate of caffe is smaller. At the end of the training, the loss value of the optimized DNN algorithm approaches 0 infinitely.When testing the data of group B, it can be seen that with 8000 iterations, the time required for caffe to complete takes 36.2 seconds. The parallel scheme based on the dual GPU model only takes 28.8 seconds, its speedup ratio reaches 20.44%, and the accuracy rate is as high as 99.25%. In the DNN model training, the year-to-year increase was 0.50%. During the iterative training process, this article will further analyze the accuracy and convergence of the continuous DNN learning algorithm on the B dataset. The result is shown in Figure9.Figure 9
Comparison of accuracy and loss before and after optimization of B dataset training.The model in this figure converges faster than caffe from beginning to end in the iterative process. It can be seen that the parallel model has an excellent effect.When testing the C group data, it can be seen that in the case of 4000 iterations, the time required for caffe to complete takes 55.4 seconds. The parallel solution based on the dual GPU model only takes 40.6 seconds, its speedup ratio reaches 26.71%, and the accuracy rate is as high as 82.36%. In the DNN model training, an increase of 6.30% year on year. During the iterative training process, this article will further analyze the accuracy and convergence of the continuous DNN learning algorithm on the C dataset. The results are shown in Figure10.Figure 10
Comparison of accuracy and loss before and after optimization of C dataset training.The model in the figure also converges faster than caffe from beginning to end in the iterative process, and the accuracy is significantly higher. It can be seen that the effect of the parallel model is excellent.Then, it can be trained to obtain the comparative error of the actual evaluation result of the experimental data evaluation results, as shown in Table3.Table 3
Comparison of experimental optimization model, test data, and actual data.
12345678Actual evaluation results0.9210.8110.7850.6230.8300.8460.6720.859Model test results after optimization0.9230.8120.7860.6250.8270.8470.6750.862Deviation0.0020.0010.0010.0020.0030.0010.0030.003It can be seen that the error has been very small and is completely within the acceptable range.It can be seen that after applying the dual GPU model parallelism, its DNN model has greatly improved the training effect of the MOOC online and offline mixed course effect dataset. The calculation time is shortened, the convergence speed is accelerated, and the accuracy rate is improved. Its optimization model parallel method has increased the speedup ratio by more than 37.37%, and the accuracy can be increased by more than 12.34%.


## 3.1. Parallel and Optimization of GPU-Based DNN Models

### 3.1.1. Parallel Optimization Design Ideas

The pipelined BP algorithm is based on the division of training layers. The GPU in this algorithm is to define prebuf, curbef, and resbuf; curbef stores the current training dataset, and prebuf stores the dataset required for the next iteration. The previous iteration layer calculates, stores the calculation result in resbuf, and plays the role of transferring data [19]. Its model can be layered into multiple GPUs. When the dataset faced by DNNs is getting larger and larger, only the model cannot satisfy its training, and the data needs to be trained in batches. At this point, it can use minibatch to divide the training data to complete parallel training. The process is shown in Figure 4.Figure 4
Minibatch parallel training data.The data is in the training process; it is best to reduce the total communication overhead and parameter exchange period. It can reversely distribute the data trained by the GPU when shards are merged [20].


### 3.1.2. Parallel Optimization of Multiple Models

First, the DNN model can be split. When it is difficult to concentrate training on big data, it can be divided into small model blocks. Combining different models to calculate the weight parameters, in this state, the GPU will generate a relative calculation time, and the GPU only runs a part of the model. The entire system will mobilize the model operation engine to train the model. In this process, the data needs to be consistent to prevent deviations in the final results. Compared with data parallelism, model parallelism will produce a lot of consumption, resulting in poor acceleration effect [21]. Model and data parallelism have certain limitations on the GPU, and the learning rate needs to be reduced to achieve a higher accuracy rate. Therefore, this article mixes the two training methods, as shown in Figure 5.Figure 5
Hybrid design of two parallel methods.In a DNN, each level has a specific connection method. In general, full connections only exist in certain computational neural layers in the neural network structure, and there are parts that are independent of other connections. Training can be split on multiple GPUs. This behavior can reduce the forward and backward propagation time of training .


### 3.1.3. Minibatch Parallel Optimization Training

The three-level thread structure of GPU has a large number of computing cores, which is suitable for intensive training such as DNN models. The use of GPU training can improve training efficiency. For the wear resistance of DNNs, the characteristics can be optimized. DNNs favor gradient descent algorithms for learning. It can be seen that when the gradient of the batch data is calculated, the network parameters need to be updated. Using batch processing will increase the GPU running speed. Using its characteristics of good floating point operations, the matrix and vector algorithms of DNNs are transformed into matrix and matrix algorithms. When selecting batch data, select the set composed of training data randomly. DNNs do not require ordered data, so they can meet their characteristics. When training GPU, we follow the principle of double GPU twice as single GPU. Under certain circumstances, increasing the data batch will form certain advantages. The graphics memory and computing core on the GPU can be used to improve the efficiency of convolution calculations. The decrease in the number of data iterations during training will increase the processing speed for the same amount of data, resulting in larger batches and higher accuracy, resulting in smaller training shocks. The premise is that the batch size is too large to reduce the test performance. It can be seen that this improved parallel method can improve the performance during model training.


## 3.1.1. Parallel Optimization Design Ideas

The pipelined BP algorithm is based on the division of training layers. The GPU in this algorithm is to define prebuf, curbef, and resbuf; curbef stores the current training dataset, and prebuf stores the dataset required for the next iteration. The previous iteration layer calculates, stores the calculation result in resbuf, and plays the role of transferring data [19]. Its model can be layered into multiple GPUs. When the dataset faced by DNNs is getting larger and larger, only the model cannot satisfy its training, and the data needs to be trained in batches. At this point, it can use minibatch to divide the training data to complete parallel training. The process is shown in Figure 4.Figure 4
Minibatch parallel training data.The data is in the training process; it is best to reduce the total communication overhead and parameter exchange period. It can reversely distribute the data trained by the GPU when shards are merged [20].


## 3.1.2. Parallel Optimization of Multiple Models

First, the DNN model can be split. When it is difficult to concentrate training on big data, it can be divided into small model blocks. Combining different models to calculate the weight parameters, in this state, the GPU will generate a relative calculation time, and the GPU only runs a part of the model. The entire system will mobilize the model operation engine to train the model. In this process, the data needs to be consistent to prevent deviations in the final results. Compared with data parallelism, model parallelism will produce a lot of consumption, resulting in poor acceleration effect [21]. Model and data parallelism have certain limitations on the GPU, and the learning rate needs to be reduced to achieve a higher accuracy rate. Therefore, this article mixes the two training methods, as shown in Figure 5.Figure 5
Hybrid design of two parallel methods.In a DNN, each level has a specific connection method. In general, full connections only exist in certain computational neural layers in the neural network structure, and there are parts that are independent of other connections. Training can be split on multiple GPUs. This behavior can reduce the forward and backward propagation time of training .


## 3.1.3. Minibatch Parallel Optimization Training

The three-level thread structure of GPU has a large number of computing cores, which is suitable for intensive training such as DNN models. The use of GPU training can improve training efficiency. For the wear resistance of DNNs, the characteristics can be optimized. DNNs favor gradient descent algorithms for learning. It can be seen that when the gradient of the batch data is calculated, the network parameters need to be updated. Using batch processing will increase the GPU running speed. Using its characteristics of good floating point operations, the matrix and vector algorithms of DNNs are transformed into matrix and matrix algorithms. When selecting batch data, select the set composed of training data randomly. DNNs do not require ordered data, so they can meet their characteristics. When training GPU, we follow the principle of double GPU twice as single GPU. Under certain circumstances, increasing the data batch will form certain advantages. The graphics memory and computing core on the GPU can be used to improve the efficiency of convolution calculations. The decrease in the number of data iterations during training will increase the processing speed for the same amount of data, resulting in larger batches and higher accuracy, resulting in smaller training shocks. The premise is that the batch size is too large to reduce the test performance. It can be seen that this improved parallel method can improve the performance during model training.


## 3.2. Experimental Preparation and the Optimized Model’s Effect on Big Data Processing

This article uses GCC compiler to compile C++ code, NVCC compiler to compile GPU code, the number of single GPU graphics card cores is 2880, the basic frequency is 710 MHz, the dynamic speedup frequency is 876 MHz, and the memory capacity is 12 GB. Obtain online and offline MOOC evaluation data from 2016 to 2020 from the educational administration system of a university. Dataset format (a1,a2,⋯,a22,b), a total of 20880 sample data. Remove the higher and lower ones and those that are inconsistent with the facts, leaving 16962 sample data. This experiment is improved based on caffe and compared with the improved caffe. The GPU library for accelerating machine learning with cuDNN mainly highlights the performance and memory overhead. In this experiment, cuDNN is used to accelerate the GPU matrix. After normalization of some samples, Tables 1 and 2 can be obtained.Table 1
Example of data after normalized sample 1.
Index/sample12345678a10.830.690.850.890.790.970.960.82a20.940.930.680.710.910.720.710.85a30.960.910.720.990.740.960.740.92a40.680.990.920.750.840.710.750.92a50.740.990.870.990.790.940.780.82a60.860.70.920.690.810.950.920.7a70.90.830.810.710.730.880.70.84a80.860.870.690.750.850.810.950.88a90.920.810.980.990.960.880.750.67a100.990.850.790.90.730.730.70.94a110.840.730.820.730.720.810.940.77a120.790.750.790.80.730.880.980.71a130.810.790.910.980.790.860.950.94a140.80.790.930.970.880.680.810.96a150.770.80.950.870.680.720.780.81a160.80.870.740.810.90.980.870.9a170.880.890.750.90.680.820.920.92a180.990.830.690.940.690.670.670.92a190.860.670.930.870.680.770.850.76a200.850.820.970.890.750.850.940.85a210.930.760.990.70.920.950.70.86a220.980.730.670.780.70.940.870.96a230.910.70.780.760.850.980.90.8b0.920.730.740.860.840.790.70.79Table 2
Examples of data after normalized sample 2.
Index/sample12345678x10.810.890.70.760.970.840.760.86x20.860.760.710.90.850.850.950.96x30.70.80.950.680.710.880.960.92x40.980.950.840.870.930.770.80.99x50.810.880.690.750.730.760.850.75x60.690.830.920.950.830.870.730.86x70.90.690.990.90.890.990.870.87x80.830.880.780.940.90.80.840.85x90.830.740.970.680.930.830.70.79x100.840.830.940.840.710.840.810.8x110.690.930.980.720.950.850.760.94x120.730.930.720.90.820.690.980.8x130.670.730.950.890.790.670.760.83x140.840.970.710.850.980.820.90.76x150.960.670.990.740.790.960.870.78x160.920.750.830.890.750.760.70.68x170.730.860.90.880.850.70.710.86x180.750.820.820.710.940.70.820.96x190.690.710.70.70.890.670.960.87x200.830.980.720.670.970.810.830.73x210.960.850.860.910.910.990.950.82x220.930.950.940.710.680.830.680.99x230.860.910.990.90.840.880.860.91y0.950.790.750.90.790.870.870.8The experimental dataset is divided into three parts: A, B, and C, and the three parts have the same amount of data. These datasets have been preprocessed during the collection process and can be directly used for DNN learning. The test network structure is a comparative test based on the network structures of LeNet, CaffeNet, and GoogleNet. Use the A dataset for experiments on the LeNet network structure, while CaffeNet corresponds to B and GoogleNet corresponds to C. The CaffeNet network structure is shown in Figure6.Figure 6
CaffeNet network structure.Caffe does have advantages when dealing with small-scale datasets. However, in the case of large dataset processing, there is no better effect. Here, we select a large-scale MOOC online and offline mixed curriculum effect evaluation dataset and use the optimized DNN model for testing.


## 3.3. Experimental Test and Result Analysis

We will perform performance testing and comparison. In this experiment, 120000, 8000, and 4000 iterations of training were performed on the three datasets, respectively, and the training time comparison and training accuracy comparison of the improved caffe and improved caffe were compared. The test result is shown in Figure7.Figure 7
Comparison of the time and accuracy required to complete training before and after optimization.When testing the A group of data, it can be seen that with 120000 iterations, the time required for caffe to complete takes 19.8 seconds. The parallel solution based on the dual GPU model only takes 12.4 seconds, and its speedup ratio reaches 37.37%. The accuracy rate is also as high as 99.12%, a year-to-year increase of 12.34% in the DNN model training. In the iterative training process, this article will further analyze the accuracy and convergence of the algorithm on the A dataset for continuous DNN learning. The results are shown in Figure8.Figure 8
Comparison of accuracy and loss before and after optimization of A dataset training.The figure shows that the accuracy of parallel training rises faster and the convergence is faster, while the convergence and accuracy rise rate of caffe is smaller. At the end of the training, the loss value of the optimized DNN algorithm approaches 0 infinitely.When testing the data of group B, it can be seen that with 8000 iterations, the time required for caffe to complete takes 36.2 seconds. The parallel scheme based on the dual GPU model only takes 28.8 seconds, its speedup ratio reaches 20.44%, and the accuracy rate is as high as 99.25%. In the DNN model training, the year-to-year increase was 0.50%. During the iterative training process, this article will further analyze the accuracy and convergence of the continuous DNN learning algorithm on the B dataset. The result is shown in Figure9.Figure 9
Comparison of accuracy and loss before and after optimization of B dataset training.The model in this figure converges faster than caffe from beginning to end in the iterative process. It can be seen that the parallel model has an excellent effect.When testing the C group data, it can be seen that in the case of 4000 iterations, the time required for caffe to complete takes 55.4 seconds. The parallel solution based on the dual GPU model only takes 40.6 seconds, its speedup ratio reaches 26.71%, and the accuracy rate is as high as 82.36%. In the DNN model training, an increase of 6.30% year on year. During the iterative training process, this article will further analyze the accuracy and convergence of the continuous DNN learning algorithm on the C dataset. The results are shown in Figure10.Figure 10
Comparison of accuracy and loss before and after optimization of C dataset training.The model in the figure also converges faster than caffe from beginning to end in the iterative process, and the accuracy is significantly higher. It can be seen that the effect of the parallel model is excellent.Then, it can be trained to obtain the comparative error of the actual evaluation result of the experimental data evaluation results, as shown in Table3.Table 3
Comparison of experimental optimization model, test data, and actual data.
12345678Actual evaluation results0.9210.8110.7850.6230.8300.8460.6720.859Model test results after optimization0.9230.8120.7860.6250.8270.8470.6750.862Deviation0.0020.0010.0010.0020.0030.0010.0030.003It can be seen that the error has been very small and is completely within the acceptable range.It can be seen that after applying the dual GPU model parallelism, its DNN model has greatly improved the training effect of the MOOC online and offline mixed course effect dataset. The calculation time is shortened, the convergence speed is accelerated, and the accuracy rate is improved. Its optimization model parallel method has increased the speedup ratio by more than 37.37%, and the accuracy can be increased by more than 12.34%.


## 4. Discussion

This article focuses on the evaluation of the online and offline mixed teaching effect of MOOC based on the DNN model. This article first explains the related concepts of the deep network model. This article has already described the application of MOOC in the teaching field. Then, this article explains some DNN models and algorithms, such as BP neural network model and algorithms. BP neural network is a backpropagation network. Multilayer prefeedback neural network can be trained by error reversal propagation algorithm, including input layer, hidden layer, and output layer. The improved BP algorithm can use additional momentum factors to reduce oscillations and speed up convergence. This paper designs a mixed-mode teaching in a MOOC environment, including course operation procedures and teaching modes. This article explains the clustering algorithm based on DNN-Gaussian mixture model. It calculates the probability density function with Gaussian component density weighted sum and trains the function value with expect maximization. Finally, this paper discusses the parallelism of GPU-based DNN model and its application in MOOC mixed teaching effect dataset. In this paper, we can draw conclusions from parallel optimization ideas and processes with experimental testing of its optimization effects. The error caused by the parallel mode training dataset is very small, the speedup ratio before optimization is increased by more than 37.37%, and the accuracy can be increased by more than 12.34%. It can be seen that after the optimization of this DNN model, there will be a good development prospect in the evaluation of the online and offline mixed teaching effect of MOOC and other fields.


## 5. Conclusion

For the improvement of the deep network model to test the effect of online and offline teaching in MOOC, this article describes the design ideas of parallel optimization. GPU uses curbef to store the current training dataset, prebuf to store the dataset required for the next iteration, and resbuf to store the calculation results. Use minibatch to divide the training data and then optimize it. The model training and data training methods are mixed and designed to obtain an optimized deep network model, and then, the experiment design is carried out. Use the 2016 to 2020 online and offline MOOC evaluation data of a school to divide it into several datasets for testing. Test and analyze the model performance before and after optimization. The time required for caffe to complete is longer than that of the parallel solution based on the dual GPU model, and the accuracy is not enough. The convergence speed of the parallel scheme based on the dual GPU model is also faster. And by comparing with the actual data, it is found that the error formed by the test result is very small. It can be seen that this dual GPU model parallel scheme is very feasible.



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*Source: 2173005-2022-03-19.xml*