# Intelligent Psychology Teaching System Based on Adaptive Neural Network

Authors: Xiaojia Pang
Journal: Applied Bionics and Biomechanics
 (2022)

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## Abstract

In order to study the intelligent psychology system, this paper proposes the role of adaptive neural network based on it and uses the ICAP learning method to compare with it. Firstly, the basic structure of the neural network in the teaching system is introduced, the psychological teaching algorithm based on the adaptive neural network is introduced, the ideas are formulated, and the four learning methods and the design elements of the adaptive neural network are described. The corresponding relationship between the four learning methods and the adaptive neural network is explained. The most popular and advanced adaptive neural network module usage statistics are made. The network model on the right is more advanced than the left, and the classification accuracy is higher. The interactive learning elements used by the network model from left to right gradually increase, and the performances of the network model are gradually enhanced. Among them, the number of interactive learning elements inception modules used by the network models GoogLeNet, Inception-v2, Inception-v4, and Inception-ResNet-v2 are 9, 10, 14, and 20, respectively. Inception-v4 also employs 2 interactive learning element reduction modules. Inception-ResNet-v2 uses 2 interactive learning element reduction modules and 20 residual modules. The ICAP classification method is experimentally studied. The design of the experiment adopts passive method (P), active method (A), constructive method (C), and interactive method (I), respectively, to learn a short text in materials science. By analyzing the learning effect and comparing the data before and after the test, it can be concluded that the learning performance of the four learning methods gradually increased by 8%-10%, and the learning effect increased significantly. With the gradual increase of educational psychological learning elements in the adaptive neural network, the network learning level is continuously improved, and the classification accuracy is gradually improved.


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## Body

## 1. Introduction

With the development of network and computer technology, distance education based on Web has become a new form of education. Anyone who is not limited by space and time can learn through the network, realizing a real open education and lifelong education. Personalized adaptive distance teaching has become a hot issue in the application of distance teaching systems [1]. Using artificial intelligence technology can directly and scientifically understand students’ personality characteristics and learning progress and adjust their own strategies and plans in a timely manner to meet the needs of the educated objects. The running process does not need manual intervention and realizes the real resource sharing [2]. The main purpose of educational psychology research is to understand how people learn. The core of artificial intelligence neural network research is to explore how machines learn. The goal of artificial intelligence neural networks is to enable machines to have human-like (or even surpass) human-like learning and thinking abilities. It can be seen that the education of people and the education of “machines” have many similarities. In fact, the education of “machines” simulates many education methods and processes for people. After more than a century of development, educational psychology has many mature methods and theories. Therefore, using the theory and methods of educational psychology to guide the design of artificial intelligence neural networks theoretically, macroscopically, or even microscopically may become a direction worth exploring. Figure 1 shows the basic framework of educational psychology.Figure 1
Basic framework of educational psychology.


## 2. Literature Review

In response to this problem, Zhong et al. researched that the teaching of ideological and political courses in junior high schools should learn that students are masters, help themselves, and know themselves. As a teacher of basic education, it is his responsibility to defend himself as an educator in the new era, get rid of stereotyped indoctrination, and implement charming teaching [3]. Wei et al. emphasized that psychological teaching should follow the development of the heart, and the basis of all knowledge comes from feeling. Feeling is the original source and the most authentic ability. It advocates free-mind learning, so that students can realize that the intuitive teaching of objects is gradually developing to the teaching of abstract concepts and words [4]. Liang et al. believed that educational psychology requires that students’ psychological learning laws should be adapted to teaching in class, in the setting of teaching objectives, and in the achievement of objectives. In this way, stricter and more specific requirements are put forward for teachers’ personal quality and learning ability [5]. Gao, et al. paid attention to the development of quality education. Under the school education system, educational psychology has been paid increasingly attention [6]. In the practice teaching research by Peng et al., the improvement of Pei’s teaching steps and the learning of teaching concepts have gradually achieved concrete results. In actual education and teaching activities, summarizing the experience and focusing on coordination are the first steps to realize the psychology of teaching. Students’ psychological development must conform to the psychological laws of students’ growth, so that students can understand self-education. At the same time, intellectual development and emotional attitude values are also in the position of active learning [7]. Soteriou et al. had profound research on the idea of educational psychology. They believed that the education of ideological and political courses for middle school students can enhance the practicality of the “psychological” teaching theory and discussed how to use the psychological teaching mode in middle school ideological and political courses [8]. Grieu et al. implemented penetrating psychological teaching research, put forward precautions and teaching suggestions for middle school education, and summed up the current lack of humanization in middle school education based on the actual combat experience of front-line teachers. The ideological and political courses in middle school have the characteristics of dominant subjects. With the aid of the concept of educational psychology, it can change the boringness of previous middle school education and create the education of students’ hearts [9]. Xie et al. published the implementation of life education in ideological and political teaching, describing that the psychological development and growth laws of middle school students have a profound impact on teachers’ teaching guidance. Life education is the education of love and the educational thought of learning the brain, heart, and hands at the same time. This thought coincides with Pei’s educational thought. On this basis, it is more flexible to apply psychological teaching to the ideological and political classroom [10]. Yue et al. believe that in basic education, learning to learn is more worth thinking about than learning knowledge. Giving more attention to your children as they grow is paramount. Basic education is education on heart. School education is the most and most important education a child receives during his or her development. It is worthy of promotion that the education of each subject in the secondary school is permeated with the psychologicalized teaching method [11]. Kim et al. considered the disciplinary advantages and characteristics of ideological and political education in the proposition of psychologicalizing ideological and political education and analyzed the real situation of school education for middle school students under the current quality education. In practical teaching, the roles of teacher and student should be interchanged. The teaching of empathy, especially the teaching of ideological and political courses, requires teachers to make students active and lively in the classroom and cultivate a healthy and upward psychological growth environment [12]. Based on the current research, this paper proposes an experimental study on the ICAP classification method. The experimental design adopts passive method (P), active method (A), constructive method (C), and interactive method (I) to learn a short text in materials science. By analyzing the learning effect and comparing the data before and after the test, it can be concluded that the learning performance of the four learning methods gradually increased by 8%-10%, and the learning effect increased significantly. With the gradual increase of educational psychological learning elements in the adaptive neural network, the network learning level is continuously improved, and the classification accuracy is gradually improved.


## 3. Method

### 3.1. Neural Network Infrastructure in Teaching System

(i)
Input layer: the input layer is the basis of the study guide, including the students’ interest in learning (assuming there areI1 inputs), learning style (using Solomon’s 4 learning styles), and learning mastery (assuming there is I2 knowledge point and mastery) sequence even input, there are I1+ 4 + I2 inputs.(ii)
Output layer: the output layer is the result of instructions. It specifically refers to the learning unit and the learning method of further learning, and there are two outputs [13].(1)yjlt=φsjlt(b)
Nonlinear function:(2)φt=11+e−s(c)
Sigmoid function:(3)φs=tanhs2=1−e−s1+e−s


### 3.2. A Psychological Teaching Algorithm Based on Adaptive Neural Network

(a)
Forward propagation: input sample→input layer→each the hidden layer (processing layer) → output layer: if the actual output of the output layer does not match the expected output (teacher signal), the process of error back propagation is transferred(b)
(ii)Error back propagation: output error → hidden layer (layer by layer) → input layer [14]: Its main purpose is to pass the output error back and apportion the error to all units of each layer, to obtain the error signal of each layer unit, and then correct the weight of each unit (the process is a weight adjustment process). The process of weight adjustment is the learning and training process of the network. In the intelligent teaching model defined in this paper, the two processes are as follows:
(a)
Form a learning model for the learner (forward propagation process)Select a number of samples that are more accurate in the intelligent instruction of learners → inputI1+4+ I2 specific values of learner’s hobbies, learning style and learning mastery → form, three types of processing values of learner’s hobbies, learning style and learning mastery situation > output learning unit and the specific value of learning methods [15]
(b)
If the actual output of the output layer does not match the expected output (error back propagation process)The error between the expected learning unit and learning method → the three types of processing values of the learner’s hobby, learning style, and learning mastery formed →I1+4+ I2 specific value of the learner’s hobby, learning style, and learning mastery


### 3.3. Methods and Ideas

A: condition: the set of input samples are as follows:(4)xk=x1k,⋯,xnk,k=1,2,⋯,kAmong them,(5)K=I1+4+I2For eachXk, the desired output (target) is obtained
(6)Dk=d1k,⋯,dmkwhere m=2. It forms a set of input-output samples for
(7)Xk,Dk,k=1,2,⋯,KHope:Yk⟶Dk—reached by gradually changing the weights (learning training process).B: ideal outputDk: for input Xk, the system is expected to produce the ideal result


### 3.4. Four Learning Styles and Adaptive Neural Network Design Elements

Figure2 shows the comprehensive framework of the “ICAP Taxonomy of Learning Styles”.Figure 2
Learning style classification ICAP framework.


#### 3.4.1. Passive Learning and Forward Algorithm

Passive learning in educational psychology mainly refers to psychological activities such as paying attention to relevant information [16]. The forward algorithm of artificial neural network is to update the network parameters layer by layer from the first layer to the next layer, and the information is transmitted from to back without a closed loop [17]. This type of operation is similar to passive learning. Because in the forward calculation process, each network layer only passively accepts the network parameters passed in before and does not learn and update the relevant parameters.


#### 3.4.2. Active Learning and Feedback Neural Network

Active learning in educational psychology means actively participating in the process of learning and receiving information. It is different from passive learning in both mental activity and explicit activity [18]. Different from the forward algorithm, the feedback neural network in the adaptive neural network processes the input signal to generate the output signal and then updates and calculates each parameter in the network according to the deviation between the output signal and the actual signal. Such a process is similar to an active learning process. Therefore, the feedback neural network can be understood as active learning.


#### 3.4.3. Constructive Learning and Deep Learning Network

In educational psychology, constructive learning refers to transcending and generating new knowledge in the process of learning existing materials. Constructive learning involves more complex psychological and external activities, such as contrast or comparison, question initiation, hypothesis formation, proof provision, and explanation of knowledge by means of words or diagrams. Constructive learning contains active learning [19]. If active learning is to emphasize knowledge points by means of underlining, then constructive learning is to explain and explain the underlined part. Constructive learning requires independent insight beyond the original learning material itself. Therefore, such a learning method has obvious “construction” characteristics. In an adaptive deep learning network under unsupervised conditions, the input layer of the network directs the raw data into the network. The first hidden layer of the network that follows will perform preliminary learning on the original data, to obtain the most subtle features of the original data. Each subsequent hidden layer will abstractly extract data features at a higher abstraction level on the basis of the previous layer, that is, acquire new knowledge through learning. Therefore, such a learning style is similar to constructive learning.


#### 3.4.4. Interactive Learning and Residual/Recurrent Neural Network

Interactive learning refers to the mutual cooperation of two or more learning individuals to carry out interactive learning. It is a constructive learning activity such as activating ideas, inspiring ideas, and supplementing deficiencies among different learners. The specific methods include learning from all parties to jointly explain knowledge, conduct debates, synthesize the results of the discussion, and generate new conclusions, etc. Because the interaction is not only about the dialogue itself, but more importantly, it is also about the possibility to generate new knowledge and ideas through the interaction. Therefore, the interactive learning process has both active learning and constructive learning characteristics. The four construction types of interactive learning are self-construction (integrating the views of all learning parties), guiding construction (interactive communication with experts), sequential construction, and collaborative construction (the learning parties express their opinions sequentially or collaboratively). The four ways have different characteristics: self-construction emphasizes “approaching” and “acceptance,” guidance construction focuses on “choice” and “manipulation,” sequence construction focuses on “generation” or “generation,” and collaborative construction advocates “synergy,” “production,” and “innovation.” Different construction types imply different ways of learning activities and ultimately lead to different learning outcomes. Adaptive deep learning network contains many interactive learning elements, such as deep residual (ResNet) module and recurrent neural network (RNN). Figure3 shows the deep residual module, whose input is x and the output is Hx. This module does not learn a complete output Hx but learns the difference between the output and input Hx−x, that is, the residual. The residual is the result of interactive learning and belongs to self-construction [20].Figure 3
Depth residual module.Figure4 shows the logic diagram of the recurrent neural network. The left side of the equal sign is a simplified diagram, and the right side of the equal sign is an expanded diagram, which is a series of connected networks. Among them, A is the node, X is the input, and h is the output. The operation result h0 of the input X0 of the network will affect the operation result h1 of X1. And so on, the operation result ht−1 of Xt−1 will affect the operation of ht. Therefore, RNN is similar to the interactive learning of learning parties, which can be attributed to sequence construction and collaborative construction [21].Figure 4
Recurrent neural network (RNN) logic.


#### 3.4.5. Knowledge Change and Calculation Process of Adaptive Neural Network

In fact, the design of adaptive neural network contains rich educational psychology ideas, and the calculation process of adaptive neural network is essentially the process of data storage, integration, inference, and coinference. First, the adaptive neural network needs to solve the problem of data (knowledge) storage; secondly, the adaptive neural network needs to extract, select, and activate the original stored data to mine the hidden features of the original data, and based on the processing results of the previous layer continuously extract, select and activate to achieve abstraction, reasoning, and integration of data (knowledge). Then, if the cognitive results are compared with the actual results and do not meet the cognitive expectations, the adaptive neural network will repeat the above learning and cognitive processes, until the expected results are obtained and a parameter learning set or network model based on the network is formed (the adaptive neural network training ends). Finally, based on the results of cognitive training (the network structure and related parameters are saved, that is, “memory”), the adaptive neural network can perform effective reasoning and judgment on other unlearned data (knowledge) (“application” processing) and can also perform effective reasoning and judgment on similar data (knowledge) (“transfer” processing). Based on the learning (cognitive) results of different adaptive neural networks on different data (knowledge), more complex data or knowledge can be recognized and inferred (“cocreation”) [22].


### 3.5. Correspondence between Four Learning Methods and Adaptive Neural Network

The learning style taxonomy conjecture holds that the knowledge changes or learning processes of learning activities with different styles or categories are consistent with the explicit behaviors corresponding to the learning activities. As shown in Figure5, in the same knowledge change process, differences in learning methods will lead to different learning effects, and the learning level will gradually increase in the direction of passive, active, constructive, and interactive. Meanwhile, passive learning means acceptance, active learning means manipulation, constructive learning means generation, and interactive learning means collaboration [23].Figure 5
For learning styles and their evolution.In fact, the evolution process of adaptive neural networks is similar to the “evolution” process of learning taxonomy, from low-level to high-level, simple to complex, shallow to deep, low-accuracy to high-accuracy, and inefficient to high efficiency. The initial shallow learning neural network has only one or several layers, and the current deep neural network has reached hundreds or even thousands of layers, and the computational accuracy in some fields has reached or even exceeded the level of human cognition [24].The autoencoder compared between the four learning methods and the adaptive neural network is an unsupervised feature learning network. It uses the back-propagation algorithm to make the target output value equal to the input value, which is a kind of passive learning. Network combined with active learning. Convolutional Neural Network (CNN) mainly reduces the number of network parameters to the greatest extent through methods, such as local receptive field, weight sharing, and temporal or spatial subsampling, and achieves the characteristics of scale, displacement, and deformation to a certain extent. It can effectively extract the hidden features in the data under unsupervised conditions and can describe the target object efficiently and accurately. It is a network structure with constructive learning features [25].


#### 3.5.1. Correspondence between the Four Learning Methods and the Elements of Adaptive Neural Network Design

According to the corresponding characteristics of the four classification methods of ICAP and the adaptive neural network model, the corresponding relationship as shown in Table1 is established. Among them, the adaptive neural network forward calculation algorithm and the fully connected module are mainly passive learning data (knowledge) and have passive learning characteristics. Modules such as the back-propagation algorithm, loss function, and random network node failure (dropout) to prevent overfitting of the adaptive neural network mainly perform operations such as revising the passively learned data and have active learning characteristics. The modules of convolution, pooling, and activation of adaptive neural network can discover and transform the hidden features and knowledge in the data and have the characteristics of constructive learning. The modules of inception, residual reduction, batch normalization, and network-in-network of the adaptive neural network are both constructive and interactive. It has interactive learning features. Among them, constructive learning and interactive learning contain significantly more adaptive neural network components, which shows that constructive learning and interactive learning are relatively complex and creative [26].Table 1
Correspondence between four learning methods and adaptive neural network elements.
ICAP classificationComponents of artificial intelligence neural networkPassive learningForward calculation algorithm, fully connected moduleActive learningBackpropagation algorithm, loss function, node failure moduleConstructive learningConvolution module, pooling module, activation moduleInteractive learningEmbedding module, residual module, reduction module, batch regularization module, network-in-network moduleIn view of the large number of elements in the adaptive neural network in Table1, it is impossible to describe them in detail. Only the loss function and batch regularization module are briefly described here. The function of the loss function is to calculate the loss between the actual value and the predicted value output by the network model, which is an important basis for the correctness and effectiveness of learning, similar to the active learning of ICAP. The principle of the batch regularization module is to simultaneously analyze a small batch of data. The normalization of knowledge is a typical interactive learning element. This paper makes statistics on the usage of several of the most popular and advanced adaptive neural network modules, as shown in Table 2. The network model on the right side of the table is more than the left one. The side is more advanced and the classification accuracy is higher. The interactive learning elements used by the network model from left to right gradually increase, and the performances of the network model are gradually enhanced. Among them are the network models GoogLeNet, Inception-v2, Inception-v4, and Inception-ResNet-v2. The number of interactive learning element inception modules used by v2 is 9, 10, 14, and 20. Among them, Inception-v4 also uses 2 interactive learning element reduction modules, and Inception-ResNet-v2 uses 2 interactive learning element reduction modules and 20 residual modules.Table 2
Usage statistics of adaptive neural network module (constituent elements).
ICAPModuleName of network modelGoogLeNetInception-v2Inception-v4Inception-ResNet-v2Passive learningForward calculation algorithm1111Fully connected module1111Active learningBackpropagation algorithm1111Node failure module1—11Constructive learningConvolution module2+6+6+6+Pooling module5+2+1+1+Activate module1111Interactive learningEmbedded module9101420Residual module———20Reduction module——22Batch regularization1111Net in net———1


#### 3.5.2. Knowledge Change and Adaptive Neural Network Design

Figure6 shows the knowledge change process analyzed at the microlevel. The first is to initialize the weight parameters in all network layers, which is a passive learning process (“memory”); then, the forward propagation algorithm (passive learning) is used to update the weights of each layer in the network (“application”) and realize the forward propagation of data signals (knowledge) through the network. When it propagates to the last output layer, the output data (knowledge) is compared with the actual target data (knowledge) to calculate the judgment error. If the error converges to the expected value, it means that the current round of learning has reached the expected goal, and the learning can be stopped, otherwise continue with the next round of learning to “transfer”. Before starting the next round of learning, the back-propagation algorithm (active learning) is used to update the network weights from the output layer layer by layer until the weights of all layers are updated. After that, the next round of learning (“cocreation”) begins.Figure 6
Neural network learning and training process.The “transfer” learning in the adaptive neural network is to apply the network model and parameter set generated by the data (knowledge) learning in the A field to the relevant B field to recognize the data (knowledge) in the B field. This kind of learning is very suitable for situations where there is less knowledge to learn in the field. Figure7 is a schematic diagram of a neural network transfer learning model. Among them, in the source task (data in field A), the data (knowledge) for network learning is relatively abundant, and in the target task (data in field B), there is very little data (knowledge) for network learning. The network model and parameter set learned in the source task can be “transferred” and applied to the cognition of the target task.Figure 7
Transfer learning in neural network learning.


## 3.1. Neural Network Infrastructure in Teaching System

(i)
Input layer: the input layer is the basis of the study guide, including the students’ interest in learning (assuming there areI1 inputs), learning style (using Solomon’s 4 learning styles), and learning mastery (assuming there is I2 knowledge point and mastery) sequence even input, there are I1+ 4 + I2 inputs.(ii)
Output layer: the output layer is the result of instructions. It specifically refers to the learning unit and the learning method of further learning, and there are two outputs [13].(1)yjlt=φsjlt(b)
Nonlinear function:(2)φt=11+e−s(c)
Sigmoid function:(3)φs=tanhs2=1−e−s1+e−s


## 3.2. A Psychological Teaching Algorithm Based on Adaptive Neural Network

(a)
Forward propagation: input sample→input layer→each the hidden layer (processing layer) → output layer: if the actual output of the output layer does not match the expected output (teacher signal), the process of error back propagation is transferred(b)
(ii)Error back propagation: output error → hidden layer (layer by layer) → input layer [14]: Its main purpose is to pass the output error back and apportion the error to all units of each layer, to obtain the error signal of each layer unit, and then correct the weight of each unit (the process is a weight adjustment process). The process of weight adjustment is the learning and training process of the network. In the intelligent teaching model defined in this paper, the two processes are as follows:
(a)
Form a learning model for the learner (forward propagation process)Select a number of samples that are more accurate in the intelligent instruction of learners → inputI1+4+ I2 specific values of learner’s hobbies, learning style and learning mastery → form, three types of processing values of learner’s hobbies, learning style and learning mastery situation > output learning unit and the specific value of learning methods [15]
(b)
If the actual output of the output layer does not match the expected output (error back propagation process)The error between the expected learning unit and learning method → the three types of processing values of the learner’s hobby, learning style, and learning mastery formed →I1+4+ I2 specific value of the learner’s hobby, learning style, and learning mastery


## 3.3. Methods and Ideas

A: condition: the set of input samples are as follows:(4)xk=x1k,⋯,xnk,k=1,2,⋯,kAmong them,(5)K=I1+4+I2For eachXk, the desired output (target) is obtained
(6)Dk=d1k,⋯,dmkwhere m=2. It forms a set of input-output samples for
(7)Xk,Dk,k=1,2,⋯,KHope:Yk⟶Dk—reached by gradually changing the weights (learning training process).B: ideal outputDk: for input Xk, the system is expected to produce the ideal result


## 3.4. Four Learning Styles and Adaptive Neural Network Design Elements

Figure2 shows the comprehensive framework of the “ICAP Taxonomy of Learning Styles”.Figure 2
Learning style classification ICAP framework.


### 3.4.1. Passive Learning and Forward Algorithm

Passive learning in educational psychology mainly refers to psychological activities such as paying attention to relevant information [16]. The forward algorithm of artificial neural network is to update the network parameters layer by layer from the first layer to the next layer, and the information is transmitted from to back without a closed loop [17]. This type of operation is similar to passive learning. Because in the forward calculation process, each network layer only passively accepts the network parameters passed in before and does not learn and update the relevant parameters.


### 3.4.2. Active Learning and Feedback Neural Network

Active learning in educational psychology means actively participating in the process of learning and receiving information. It is different from passive learning in both mental activity and explicit activity [18]. Different from the forward algorithm, the feedback neural network in the adaptive neural network processes the input signal to generate the output signal and then updates and calculates each parameter in the network according to the deviation between the output signal and the actual signal. Such a process is similar to an active learning process. Therefore, the feedback neural network can be understood as active learning.


### 3.4.3. Constructive Learning and Deep Learning Network

In educational psychology, constructive learning refers to transcending and generating new knowledge in the process of learning existing materials. Constructive learning involves more complex psychological and external activities, such as contrast or comparison, question initiation, hypothesis formation, proof provision, and explanation of knowledge by means of words or diagrams. Constructive learning contains active learning [19]. If active learning is to emphasize knowledge points by means of underlining, then constructive learning is to explain and explain the underlined part. Constructive learning requires independent insight beyond the original learning material itself. Therefore, such a learning method has obvious “construction” characteristics. In an adaptive deep learning network under unsupervised conditions, the input layer of the network directs the raw data into the network. The first hidden layer of the network that follows will perform preliminary learning on the original data, to obtain the most subtle features of the original data. Each subsequent hidden layer will abstractly extract data features at a higher abstraction level on the basis of the previous layer, that is, acquire new knowledge through learning. Therefore, such a learning style is similar to constructive learning.


### 3.4.4. Interactive Learning and Residual/Recurrent Neural Network

Interactive learning refers to the mutual cooperation of two or more learning individuals to carry out interactive learning. It is a constructive learning activity such as activating ideas, inspiring ideas, and supplementing deficiencies among different learners. The specific methods include learning from all parties to jointly explain knowledge, conduct debates, synthesize the results of the discussion, and generate new conclusions, etc. Because the interaction is not only about the dialogue itself, but more importantly, it is also about the possibility to generate new knowledge and ideas through the interaction. Therefore, the interactive learning process has both active learning and constructive learning characteristics. The four construction types of interactive learning are self-construction (integrating the views of all learning parties), guiding construction (interactive communication with experts), sequential construction, and collaborative construction (the learning parties express their opinions sequentially or collaboratively). The four ways have different characteristics: self-construction emphasizes “approaching” and “acceptance,” guidance construction focuses on “choice” and “manipulation,” sequence construction focuses on “generation” or “generation,” and collaborative construction advocates “synergy,” “production,” and “innovation.” Different construction types imply different ways of learning activities and ultimately lead to different learning outcomes. Adaptive deep learning network contains many interactive learning elements, such as deep residual (ResNet) module and recurrent neural network (RNN). Figure3 shows the deep residual module, whose input is x and the output is Hx. This module does not learn a complete output Hx but learns the difference between the output and input Hx−x, that is, the residual. The residual is the result of interactive learning and belongs to self-construction [20].Figure 3
Depth residual module.Figure4 shows the logic diagram of the recurrent neural network. The left side of the equal sign is a simplified diagram, and the right side of the equal sign is an expanded diagram, which is a series of connected networks. Among them, A is the node, X is the input, and h is the output. The operation result h0 of the input X0 of the network will affect the operation result h1 of X1. And so on, the operation result ht−1 of Xt−1 will affect the operation of ht. Therefore, RNN is similar to the interactive learning of learning parties, which can be attributed to sequence construction and collaborative construction [21].Figure 4
Recurrent neural network (RNN) logic.


### 3.4.5. Knowledge Change and Calculation Process of Adaptive Neural Network

In fact, the design of adaptive neural network contains rich educational psychology ideas, and the calculation process of adaptive neural network is essentially the process of data storage, integration, inference, and coinference. First, the adaptive neural network needs to solve the problem of data (knowledge) storage; secondly, the adaptive neural network needs to extract, select, and activate the original stored data to mine the hidden features of the original data, and based on the processing results of the previous layer continuously extract, select and activate to achieve abstraction, reasoning, and integration of data (knowledge). Then, if the cognitive results are compared with the actual results and do not meet the cognitive expectations, the adaptive neural network will repeat the above learning and cognitive processes, until the expected results are obtained and a parameter learning set or network model based on the network is formed (the adaptive neural network training ends). Finally, based on the results of cognitive training (the network structure and related parameters are saved, that is, “memory”), the adaptive neural network can perform effective reasoning and judgment on other unlearned data (knowledge) (“application” processing) and can also perform effective reasoning and judgment on similar data (knowledge) (“transfer” processing). Based on the learning (cognitive) results of different adaptive neural networks on different data (knowledge), more complex data or knowledge can be recognized and inferred (“cocreation”) [22].


## 3.4.1. Passive Learning and Forward Algorithm

Passive learning in educational psychology mainly refers to psychological activities such as paying attention to relevant information [16]. The forward algorithm of artificial neural network is to update the network parameters layer by layer from the first layer to the next layer, and the information is transmitted from to back without a closed loop [17]. This type of operation is similar to passive learning. Because in the forward calculation process, each network layer only passively accepts the network parameters passed in before and does not learn and update the relevant parameters.


## 3.4.2. Active Learning and Feedback Neural Network

Active learning in educational psychology means actively participating in the process of learning and receiving information. It is different from passive learning in both mental activity and explicit activity [18]. Different from the forward algorithm, the feedback neural network in the adaptive neural network processes the input signal to generate the output signal and then updates and calculates each parameter in the network according to the deviation between the output signal and the actual signal. Such a process is similar to an active learning process. Therefore, the feedback neural network can be understood as active learning.


## 3.4.3. Constructive Learning and Deep Learning Network

In educational psychology, constructive learning refers to transcending and generating new knowledge in the process of learning existing materials. Constructive learning involves more complex psychological and external activities, such as contrast or comparison, question initiation, hypothesis formation, proof provision, and explanation of knowledge by means of words or diagrams. Constructive learning contains active learning [19]. If active learning is to emphasize knowledge points by means of underlining, then constructive learning is to explain and explain the underlined part. Constructive learning requires independent insight beyond the original learning material itself. Therefore, such a learning method has obvious “construction” characteristics. In an adaptive deep learning network under unsupervised conditions, the input layer of the network directs the raw data into the network. The first hidden layer of the network that follows will perform preliminary learning on the original data, to obtain the most subtle features of the original data. Each subsequent hidden layer will abstractly extract data features at a higher abstraction level on the basis of the previous layer, that is, acquire new knowledge through learning. Therefore, such a learning style is similar to constructive learning.


## 3.4.4. Interactive Learning and Residual/Recurrent Neural Network

Interactive learning refers to the mutual cooperation of two or more learning individuals to carry out interactive learning. It is a constructive learning activity such as activating ideas, inspiring ideas, and supplementing deficiencies among different learners. The specific methods include learning from all parties to jointly explain knowledge, conduct debates, synthesize the results of the discussion, and generate new conclusions, etc. Because the interaction is not only about the dialogue itself, but more importantly, it is also about the possibility to generate new knowledge and ideas through the interaction. Therefore, the interactive learning process has both active learning and constructive learning characteristics. The four construction types of interactive learning are self-construction (integrating the views of all learning parties), guiding construction (interactive communication with experts), sequential construction, and collaborative construction (the learning parties express their opinions sequentially or collaboratively). The four ways have different characteristics: self-construction emphasizes “approaching” and “acceptance,” guidance construction focuses on “choice” and “manipulation,” sequence construction focuses on “generation” or “generation,” and collaborative construction advocates “synergy,” “production,” and “innovation.” Different construction types imply different ways of learning activities and ultimately lead to different learning outcomes. Adaptive deep learning network contains many interactive learning elements, such as deep residual (ResNet) module and recurrent neural network (RNN). Figure3 shows the deep residual module, whose input is x and the output is Hx. This module does not learn a complete output Hx but learns the difference between the output and input Hx−x, that is, the residual. The residual is the result of interactive learning and belongs to self-construction [20].Figure 3
Depth residual module.Figure4 shows the logic diagram of the recurrent neural network. The left side of the equal sign is a simplified diagram, and the right side of the equal sign is an expanded diagram, which is a series of connected networks. Among them, A is the node, X is the input, and h is the output. The operation result h0 of the input X0 of the network will affect the operation result h1 of X1. And so on, the operation result ht−1 of Xt−1 will affect the operation of ht. Therefore, RNN is similar to the interactive learning of learning parties, which can be attributed to sequence construction and collaborative construction [21].Figure 4
Recurrent neural network (RNN) logic.


## 3.4.5. Knowledge Change and Calculation Process of Adaptive Neural Network

In fact, the design of adaptive neural network contains rich educational psychology ideas, and the calculation process of adaptive neural network is essentially the process of data storage, integration, inference, and coinference. First, the adaptive neural network needs to solve the problem of data (knowledge) storage; secondly, the adaptive neural network needs to extract, select, and activate the original stored data to mine the hidden features of the original data, and based on the processing results of the previous layer continuously extract, select and activate to achieve abstraction, reasoning, and integration of data (knowledge). Then, if the cognitive results are compared with the actual results and do not meet the cognitive expectations, the adaptive neural network will repeat the above learning and cognitive processes, until the expected results are obtained and a parameter learning set or network model based on the network is formed (the adaptive neural network training ends). Finally, based on the results of cognitive training (the network structure and related parameters are saved, that is, “memory”), the adaptive neural network can perform effective reasoning and judgment on other unlearned data (knowledge) (“application” processing) and can also perform effective reasoning and judgment on similar data (knowledge) (“transfer” processing). Based on the learning (cognitive) results of different adaptive neural networks on different data (knowledge), more complex data or knowledge can be recognized and inferred (“cocreation”) [22].


## 3.5. Correspondence between Four Learning Methods and Adaptive Neural Network

The learning style taxonomy conjecture holds that the knowledge changes or learning processes of learning activities with different styles or categories are consistent with the explicit behaviors corresponding to the learning activities. As shown in Figure5, in the same knowledge change process, differences in learning methods will lead to different learning effects, and the learning level will gradually increase in the direction of passive, active, constructive, and interactive. Meanwhile, passive learning means acceptance, active learning means manipulation, constructive learning means generation, and interactive learning means collaboration [23].Figure 5
For learning styles and their evolution.In fact, the evolution process of adaptive neural networks is similar to the “evolution” process of learning taxonomy, from low-level to high-level, simple to complex, shallow to deep, low-accuracy to high-accuracy, and inefficient to high efficiency. The initial shallow learning neural network has only one or several layers, and the current deep neural network has reached hundreds or even thousands of layers, and the computational accuracy in some fields has reached or even exceeded the level of human cognition [24].The autoencoder compared between the four learning methods and the adaptive neural network is an unsupervised feature learning network. It uses the back-propagation algorithm to make the target output value equal to the input value, which is a kind of passive learning. Network combined with active learning. Convolutional Neural Network (CNN) mainly reduces the number of network parameters to the greatest extent through methods, such as local receptive field, weight sharing, and temporal or spatial subsampling, and achieves the characteristics of scale, displacement, and deformation to a certain extent. It can effectively extract the hidden features in the data under unsupervised conditions and can describe the target object efficiently and accurately. It is a network structure with constructive learning features [25].


### 3.5.1. Correspondence between the Four Learning Methods and the Elements of Adaptive Neural Network Design

According to the corresponding characteristics of the four classification methods of ICAP and the adaptive neural network model, the corresponding relationship as shown in Table1 is established. Among them, the adaptive neural network forward calculation algorithm and the fully connected module are mainly passive learning data (knowledge) and have passive learning characteristics. Modules such as the back-propagation algorithm, loss function, and random network node failure (dropout) to prevent overfitting of the adaptive neural network mainly perform operations such as revising the passively learned data and have active learning characteristics. The modules of convolution, pooling, and activation of adaptive neural network can discover and transform the hidden features and knowledge in the data and have the characteristics of constructive learning. The modules of inception, residual reduction, batch normalization, and network-in-network of the adaptive neural network are both constructive and interactive. It has interactive learning features. Among them, constructive learning and interactive learning contain significantly more adaptive neural network components, which shows that constructive learning and interactive learning are relatively complex and creative [26].Table 1
Correspondence between four learning methods and adaptive neural network elements.
ICAP classificationComponents of artificial intelligence neural networkPassive learningForward calculation algorithm, fully connected moduleActive learningBackpropagation algorithm, loss function, node failure moduleConstructive learningConvolution module, pooling module, activation moduleInteractive learningEmbedding module, residual module, reduction module, batch regularization module, network-in-network moduleIn view of the large number of elements in the adaptive neural network in Table1, it is impossible to describe them in detail. Only the loss function and batch regularization module are briefly described here. The function of the loss function is to calculate the loss between the actual value and the predicted value output by the network model, which is an important basis for the correctness and effectiveness of learning, similar to the active learning of ICAP. The principle of the batch regularization module is to simultaneously analyze a small batch of data. The normalization of knowledge is a typical interactive learning element. This paper makes statistics on the usage of several of the most popular and advanced adaptive neural network modules, as shown in Table 2. The network model on the right side of the table is more than the left one. The side is more advanced and the classification accuracy is higher. The interactive learning elements used by the network model from left to right gradually increase, and the performances of the network model are gradually enhanced. Among them are the network models GoogLeNet, Inception-v2, Inception-v4, and Inception-ResNet-v2. The number of interactive learning element inception modules used by v2 is 9, 10, 14, and 20. Among them, Inception-v4 also uses 2 interactive learning element reduction modules, and Inception-ResNet-v2 uses 2 interactive learning element reduction modules and 20 residual modules.Table 2
Usage statistics of adaptive neural network module (constituent elements).
ICAPModuleName of network modelGoogLeNetInception-v2Inception-v4Inception-ResNet-v2Passive learningForward calculation algorithm1111Fully connected module1111Active learningBackpropagation algorithm1111Node failure module1—11Constructive learningConvolution module2+6+6+6+Pooling module5+2+1+1+Activate module1111Interactive learningEmbedded module9101420Residual module———20Reduction module——22Batch regularization1111Net in net———1


### 3.5.2. Knowledge Change and Adaptive Neural Network Design

Figure6 shows the knowledge change process analyzed at the microlevel. The first is to initialize the weight parameters in all network layers, which is a passive learning process (“memory”); then, the forward propagation algorithm (passive learning) is used to update the weights of each layer in the network (“application”) and realize the forward propagation of data signals (knowledge) through the network. When it propagates to the last output layer, the output data (knowledge) is compared with the actual target data (knowledge) to calculate the judgment error. If the error converges to the expected value, it means that the current round of learning has reached the expected goal, and the learning can be stopped, otherwise continue with the next round of learning to “transfer”. Before starting the next round of learning, the back-propagation algorithm (active learning) is used to update the network weights from the output layer layer by layer until the weights of all layers are updated. After that, the next round of learning (“cocreation”) begins.Figure 6
Neural network learning and training process.The “transfer” learning in the adaptive neural network is to apply the network model and parameter set generated by the data (knowledge) learning in the A field to the relevant B field to recognize the data (knowledge) in the B field. This kind of learning is very suitable for situations where there is less knowledge to learn in the field. Figure7 is a schematic diagram of a neural network transfer learning model. Among them, in the source task (data in field A), the data (knowledge) for network learning is relatively abundant, and in the target task (data in field B), there is very little data (knowledge) for network learning. The network model and parameter set learned in the source task can be “transferred” and applied to the cognition of the target task.Figure 7
Transfer learning in neural network learning.


## 3.5.1. Correspondence between the Four Learning Methods and the Elements of Adaptive Neural Network Design

According to the corresponding characteristics of the four classification methods of ICAP and the adaptive neural network model, the corresponding relationship as shown in Table1 is established. Among them, the adaptive neural network forward calculation algorithm and the fully connected module are mainly passive learning data (knowledge) and have passive learning characteristics. Modules such as the back-propagation algorithm, loss function, and random network node failure (dropout) to prevent overfitting of the adaptive neural network mainly perform operations such as revising the passively learned data and have active learning characteristics. The modules of convolution, pooling, and activation of adaptive neural network can discover and transform the hidden features and knowledge in the data and have the characteristics of constructive learning. The modules of inception, residual reduction, batch normalization, and network-in-network of the adaptive neural network are both constructive and interactive. It has interactive learning features. Among them, constructive learning and interactive learning contain significantly more adaptive neural network components, which shows that constructive learning and interactive learning are relatively complex and creative [26].Table 1
Correspondence between four learning methods and adaptive neural network elements.
ICAP classificationComponents of artificial intelligence neural networkPassive learningForward calculation algorithm, fully connected moduleActive learningBackpropagation algorithm, loss function, node failure moduleConstructive learningConvolution module, pooling module, activation moduleInteractive learningEmbedding module, residual module, reduction module, batch regularization module, network-in-network moduleIn view of the large number of elements in the adaptive neural network in Table1, it is impossible to describe them in detail. Only the loss function and batch regularization module are briefly described here. The function of the loss function is to calculate the loss between the actual value and the predicted value output by the network model, which is an important basis for the correctness and effectiveness of learning, similar to the active learning of ICAP. The principle of the batch regularization module is to simultaneously analyze a small batch of data. The normalization of knowledge is a typical interactive learning element. This paper makes statistics on the usage of several of the most popular and advanced adaptive neural network modules, as shown in Table 2. The network model on the right side of the table is more than the left one. The side is more advanced and the classification accuracy is higher. The interactive learning elements used by the network model from left to right gradually increase, and the performances of the network model are gradually enhanced. Among them are the network models GoogLeNet, Inception-v2, Inception-v4, and Inception-ResNet-v2. The number of interactive learning element inception modules used by v2 is 9, 10, 14, and 20. Among them, Inception-v4 also uses 2 interactive learning element reduction modules, and Inception-ResNet-v2 uses 2 interactive learning element reduction modules and 20 residual modules.Table 2
Usage statistics of adaptive neural network module (constituent elements).
ICAPModuleName of network modelGoogLeNetInception-v2Inception-v4Inception-ResNet-v2Passive learningForward calculation algorithm1111Fully connected module1111Active learningBackpropagation algorithm1111Node failure module1—11Constructive learningConvolution module2+6+6+6+Pooling module5+2+1+1+Activate module1111Interactive learningEmbedded module9101420Residual module———20Reduction module——22Batch regularization1111Net in net———1


## 3.5.2. Knowledge Change and Adaptive Neural Network Design

Figure6 shows the knowledge change process analyzed at the microlevel. The first is to initialize the weight parameters in all network layers, which is a passive learning process (“memory”); then, the forward propagation algorithm (passive learning) is used to update the weights of each layer in the network (“application”) and realize the forward propagation of data signals (knowledge) through the network. When it propagates to the last output layer, the output data (knowledge) is compared with the actual target data (knowledge) to calculate the judgment error. If the error converges to the expected value, it means that the current round of learning has reached the expected goal, and the learning can be stopped, otherwise continue with the next round of learning to “transfer”. Before starting the next round of learning, the back-propagation algorithm (active learning) is used to update the network weights from the output layer layer by layer until the weights of all layers are updated. After that, the next round of learning (“cocreation”) begins.Figure 6
Neural network learning and training process.The “transfer” learning in the adaptive neural network is to apply the network model and parameter set generated by the data (knowledge) learning in the A field to the relevant B field to recognize the data (knowledge) in the B field. This kind of learning is very suitable for situations where there is less knowledge to learn in the field. Figure7 is a schematic diagram of a neural network transfer learning model. Among them, in the source task (data in field A), the data (knowledge) for network learning is relatively abundant, and in the target task (data in field B), there is very little data (knowledge) for network learning. The network model and parameter set learned in the source task can be “transferred” and applied to the cognition of the target task.Figure 7
Transfer learning in neural network learning.


## 4. Results and Analysis

### 4.1. From Basic Instructional Design to Adaptive Neural Network Design

#### 4.1.1. Basic Teaching Design Based on ICAP Classification

Adaptive neural network design method is explored through basic instructional design of listening, summarizing, and explaining. Figure8 shows the basic teaching design according to the ICAP classification. According to the taxonomy of learning styles, the basic instructional design is as follows: listening (teachers teach, students listen)—passive learning, summarization (students summarize the learning content) —active learning, and interpretation (students use what they have learned to understand and explain new knowledge)—constructive learning.Figure 8
Basic instructional design according to ICAP classification.


#### 4.1.2. Logic Diagram Design of Adaptive Neural Network

Using the elements in Table1, based on the instructional design process of ICAP classification, an adaptive neural network logic block diagram is designed, as shown in Figure 9. Firstly, passive learning elements (forward calculation algorithm and fully connected module) are used to achieve the effect of listening, and the initial learning setting of adaptive neural network parameters is realized; then, in the summary part, the active learning elements (loss function and backpropagation algorithm) are used to learn and modify the network parameters; finally, the construction learning elements (activation modules) are used to construct the existing knowledge in the explanation part. In fact, listening, summarizing, and explaining are operated in parallel at the same time. After obtaining the optimal confirmed network model and parameter values, the adaptive neural network can be used to recognize and infer new data (knowledge). In summary, in this network, the relationship between educational psychology design and adaptive neural network design is as follows: listening corresponds to learning, summarizing corresponds to training, and interpreting the corresponding predictions.Figure 9
Logic block diagram of adaptive neural network design based on basic teaching design.


#### 4.1.3. Simple Adaptive Neural Network Design

A simple adaptive neural network is designed according to the logic block diagram of Figure9, and Figure 10 is a simple neural network with one hidden layer. Layer a1 is the input layer, which contains 3 neurons; layer a2 is the hidden layer, which contains 2 neurons; the z layer is the output layer, which contains 2 neurons. W1 is a parameter for layer a1 neurons and is a parameter for layer a2 neurons.Figure 10
Simple neural network.


### 4.2. Experimental Data Analysis

The ICAP classification method is experimentally studied, and the results are shown in Figure11. The design of the experiment is to learn a short text in materials’ science by using passive method (P), active method (A), constructive method (C), and interactive method (I), respectively. By analyzing the learning effect and comparing the data before and after the test, it can be concluded that the learning performance of the four learning methods gradually increased by 8%-10%, and the learning effect increased significantly.Figure 11
Experimental study on the way of ICAP participation.To illustrate the problem intuitively, this paper integrates some experimental data of the proposed adaptive neural network model and displays it visually. The abscissas of Figures12 and 13 represent different adaptive neural network models, and the complexity of the models gradually increases from left to right (see Table 2 for the complexity of some models). The ordinate represents the error rate of classification. The two figures show that with the gradual increase of educational psychological learning elements in the adaptive neural network, the network learning level is continuously improved, and the classification accuracy is gradually improved.Figure 12
Top-5 error data map (top-5 indicates that the correct value is in the top 5 data with probability).Figure 13
Top-1 error data map (top-1 represents the data corresponding to the maximum probability of the correct value).By comparing the experimental research on the ICAP participation method and the complexity of the adaptive neural network model, it can be found that the two have the same changing trend in the learning effect. Although the data used in the two studies are different, the results are similar. The experimental data shows that human learning (ICAP) and machine learning (adaptive neural network) have certain similarities, and a certain corresponding reference relationship can be established between the two learning.


## 4.1. From Basic Instructional Design to Adaptive Neural Network Design

### 4.1.1. Basic Teaching Design Based on ICAP Classification

Adaptive neural network design method is explored through basic instructional design of listening, summarizing, and explaining. Figure8 shows the basic teaching design according to the ICAP classification. According to the taxonomy of learning styles, the basic instructional design is as follows: listening (teachers teach, students listen)—passive learning, summarization (students summarize the learning content) —active learning, and interpretation (students use what they have learned to understand and explain new knowledge)—constructive learning.Figure 8
Basic instructional design according to ICAP classification.


### 4.1.2. Logic Diagram Design of Adaptive Neural Network

Using the elements in Table1, based on the instructional design process of ICAP classification, an adaptive neural network logic block diagram is designed, as shown in Figure 9. Firstly, passive learning elements (forward calculation algorithm and fully connected module) are used to achieve the effect of listening, and the initial learning setting of adaptive neural network parameters is realized; then, in the summary part, the active learning elements (loss function and backpropagation algorithm) are used to learn and modify the network parameters; finally, the construction learning elements (activation modules) are used to construct the existing knowledge in the explanation part. In fact, listening, summarizing, and explaining are operated in parallel at the same time. After obtaining the optimal confirmed network model and parameter values, the adaptive neural network can be used to recognize and infer new data (knowledge). In summary, in this network, the relationship between educational psychology design and adaptive neural network design is as follows: listening corresponds to learning, summarizing corresponds to training, and interpreting the corresponding predictions.Figure 9
Logic block diagram of adaptive neural network design based on basic teaching design.


### 4.1.3. Simple Adaptive Neural Network Design

A simple adaptive neural network is designed according to the logic block diagram of Figure9, and Figure 10 is a simple neural network with one hidden layer. Layer a1 is the input layer, which contains 3 neurons; layer a2 is the hidden layer, which contains 2 neurons; the z layer is the output layer, which contains 2 neurons. W1 is a parameter for layer a1 neurons and is a parameter for layer a2 neurons.Figure 10
Simple neural network.


## 4.1.1. Basic Teaching Design Based on ICAP Classification

Adaptive neural network design method is explored through basic instructional design of listening, summarizing, and explaining. Figure8 shows the basic teaching design according to the ICAP classification. According to the taxonomy of learning styles, the basic instructional design is as follows: listening (teachers teach, students listen)—passive learning, summarization (students summarize the learning content) —active learning, and interpretation (students use what they have learned to understand and explain new knowledge)—constructive learning.Figure 8
Basic instructional design according to ICAP classification.


## 4.1.2. Logic Diagram Design of Adaptive Neural Network

Using the elements in Table1, based on the instructional design process of ICAP classification, an adaptive neural network logic block diagram is designed, as shown in Figure 9. Firstly, passive learning elements (forward calculation algorithm and fully connected module) are used to achieve the effect of listening, and the initial learning setting of adaptive neural network parameters is realized; then, in the summary part, the active learning elements (loss function and backpropagation algorithm) are used to learn and modify the network parameters; finally, the construction learning elements (activation modules) are used to construct the existing knowledge in the explanation part. In fact, listening, summarizing, and explaining are operated in parallel at the same time. After obtaining the optimal confirmed network model and parameter values, the adaptive neural network can be used to recognize and infer new data (knowledge). In summary, in this network, the relationship between educational psychology design and adaptive neural network design is as follows: listening corresponds to learning, summarizing corresponds to training, and interpreting the corresponding predictions.Figure 9
Logic block diagram of adaptive neural network design based on basic teaching design.


## 4.1.3. Simple Adaptive Neural Network Design

A simple adaptive neural network is designed according to the logic block diagram of Figure9, and Figure 10 is a simple neural network with one hidden layer. Layer a1 is the input layer, which contains 3 neurons; layer a2 is the hidden layer, which contains 2 neurons; the z layer is the output layer, which contains 2 neurons. W1 is a parameter for layer a1 neurons and is a parameter for layer a2 neurons.Figure 10
Simple neural network.


## 4.2. Experimental Data Analysis

The ICAP classification method is experimentally studied, and the results are shown in Figure11. The design of the experiment is to learn a short text in materials’ science by using passive method (P), active method (A), constructive method (C), and interactive method (I), respectively. By analyzing the learning effect and comparing the data before and after the test, it can be concluded that the learning performance of the four learning methods gradually increased by 8%-10%, and the learning effect increased significantly.Figure 11
Experimental study on the way of ICAP participation.To illustrate the problem intuitively, this paper integrates some experimental data of the proposed adaptive neural network model and displays it visually. The abscissas of Figures12 and 13 represent different adaptive neural network models, and the complexity of the models gradually increases from left to right (see Table 2 for the complexity of some models). The ordinate represents the error rate of classification. The two figures show that with the gradual increase of educational psychological learning elements in the adaptive neural network, the network learning level is continuously improved, and the classification accuracy is gradually improved.Figure 12
Top-5 error data map (top-5 indicates that the correct value is in the top 5 data with probability).Figure 13
Top-1 error data map (top-1 represents the data corresponding to the maximum probability of the correct value).By comparing the experimental research on the ICAP participation method and the complexity of the adaptive neural network model, it can be found that the two have the same changing trend in the learning effect. Although the data used in the two studies are different, the results are similar. The experimental data shows that human learning (ICAP) and machine learning (adaptive neural network) have certain similarities, and a certain corresponding reference relationship can be established between the two learning.


## 5. Conclusion

The paper proposes a research on an intelligent psychology teaching systems based on adaptive neural network and uses the basic teaching design of listening, summarizing, and explaining to explore the design method of adaptive neural networks. According to the taxonomy of learning styles, the basic teaching design is as follows: listening (teacher teaching, students listening)—passive learning, summarization (students summarize the learning content)—active learning, and interpretation (students use what they have learned to understand and explain new knowledge)—constructive learning. By comparing the experimental research on the ICAP participation method and the complexity of the adaptive neural network model, it can be found that the two have the same changing trend in the learning effect. Although the data used in the two studies are different, the results are similar. The experimental data point of view shows that human learning (ICAP) and machine learning (adaptive neural network) have certain similarities, and a corresponding reference relationship can be established between the two learning. It expands new ideas for the research of adaptive neural networks and pedagogy systems. Future research can introduce more pedagogical principles and methods, such as deep learning and beyond fragmented learning, to further confirm the feasibility of adaptive neural network design based on educational psychology.



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*Source: 6248095-2022-04-04.xml*