# Application of Sandwich Teaching Method Based on Network Platform in Rehabilitation Nursing Teaching in Postepidemic Era

Authors: Weiwei Zhang; Tian Yao; Guanzhong Dong
Journal: BioMed Research International
 (2022)

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## Abstract

Rehabilitation nursing teaching is one of the key points of nursing teaching. The quality of rehabilitation nursing teaching directly affects the comprehensive ability of clinical interns. Therefore, teachers must further deepen the reform of clinical nursing teaching, improve the teaching system, and explore effective clinical nursing teaching methods. This paper introduces the application of the network teaching mode based on sandwich in rehabilitation nursing teaching, which provides guidance and help for the clinical nursing teaching mode. Through sandwich teaching activities for third year students of the nursing department, taking mastering theoretical knowledge, changing critical thinking ability, and changing learning initiative as indicators, this paper analyzes the sandwich teaching mode and discusses the cultivation of nursing students’ ability. The experimental results showed that after the intervention, there was a statistically significant difference in the learning initiative of students in the experimental group (t=−29.713,p≤0.001). After the intervention, there was a significant difference in the learning initiative of the control group (t=−3.368,p≤0.001).


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## Body

## 1. Introduction

At present, the basic theoretical teaching of nursing is mainly based on course teaching, starting from teaching objectives, key points and difficulties, combined with relevant knowledge and clinical practice, and a teacher-led teaching mode. The sandwich education model originated from the Sunderland Institute of Technology in the United Kingdom in the 19th century. At that time, in order to meet the needs of new talents in social development, the school proposed that students should have certain work experience while learning theoretical knowledge in school. Therefore, the education model with job training in the teaching process began, and a talent training model of “learning-practice-learning” was gradually formed, which people called the “sandwich” education model vividly. Later, Heidelberg University in Germany firstly introduced the “sandwich” education model into classroom teaching, and in every classroom, the concept of talent ability training of learning-practice-relearning should be reflected. Since then, sandwich has become a classroom teaching method.The sandwich showing technique permits understudies to partake in the homeroom well overall, gives full play to the abstract drive of understudies, and enormously works on the productivity of the homeroom, and educators are in the place of directing and helping understudies in the whole instructing process. This technique compensates for the deficiencies of conventional homeroom instructing that understudies’ interest is not high and the showing impact is not moved along. Bringing the sandwich showing strategy into the homeroom, educators effectively change the showing content and cautiously plan the instructing study hall. It extraordinarily works on the understudies’ advantage in learning and significantly further develops the educating impact. The advancement of this paper is to examine the utilization of sandwich showing technique in view of the organization stage in restoration nursing showing in the postpandemic period, which is creative and useful.


## 2. Related Work

With the improvement of science and innovation, recovery nursing educating has infiltrated all parts of individuals’ life, and that is only the tip of the iceberg, and more researchers are concentrating on it. Experiential learning strategies allowed students to interact directly with community patients participating in psychological rehabilitation programs as discussed by Wu. At the point when patients shared their sickness process in class, nursing understudies were better ready to encounter the aggravation of these patients, subsequently better explaining pertinent information, creating compassion, and lightening self-saw tension prior to leaving on their mental clinical practice [1]. Cai used a checklist to determine the academic year, course type, and credits for the theoretical and practical teaching of community health nursing (CHN) courses [2]. The essential target of Kumudah et al.’s review was to evaluate attendants’ information, perspectives, and view of heart restoration programs (CRP) and to associate the connection between sociodemographic attributes, characteristics, and long periods of involvement in their insight, perspectives, and impression of CRP [3]. Wehieda intended to decide the effect of executing a showing module on physiological and mental boundaries on the normal clinical results of patients with persistent obstructive pneumonic illness [4]. In any case, the weaknesses of these examinations are that the thought factors are not sufficiently thorough, there is extraordinary vulnerability, and the common sense should be additionally explored.With the development of social economy, more and more scholars have studied the sandwich teaching method. Adetuyi zeroed in on the relationship of bilingualism in sandwich-focused English language improvement and utilized a semiexploratory plan with a3×2×2 factorial network, that is, the pretest and posttest control bunch [5]. Wang et al. discussed the role of sandwich learning in the theoretical teaching of histoembryology [6]. Manu and Owusu-Ansa’s examination planned to decide the effect of segment factors on preadministration educators’ impression of educating and learning in instructive exploration courses at a confidential college in Ghana throughout the fall 2017 semester [7]. In any case, the deficiencies of these examinations are that the model development is not logical and adequately sensible; the information should be gotten to the next level.


## 3. Sandwich Teaching Method and Related Methods Based on Network Platform

### 3.1. Sandwich Teaching Method

#### 3.1.1. The Concept of Sandwich Teaching Method

The “sandwich” teaching method originated from the Sunderland Institute of Technology in the United Kingdom. Later, the School of Medicine of Heidelberg University in Germany applied the sandwich teaching concept to small medical class teaching and established the sandwich medical teaching method. The sandwich medical teaching method is a new type of teaching method, which enables teachers and students to communicate continuously, stimulates students’ enthusiasm for active learning, and improves students’ ability of autonomous learning, reflection, and inquiry [8, 9]. In small class teaching, students’ new knowledge is cultivated through group discussions, student cross-study, and student study reports. After practical application by students, this method has been adopted by majors in most colleges and universities and has been gradually promoted in medical colleges in other countries (Figure 1).Figure 1
The basic flow of the sandwich pedagogy.


#### 3.1.2. Humanistic Psychology Perspective

Humanistic philosophy basically believes that everyone has the value and the right to achieve self-realization and rational thinking. Humanistic psychologists postulate that an individual’s actions are linked to their inner feelings and self-image. The study of humanistic theory refers to the teaching philosophy or the concepts, attitudes, and ideas advocated by humanistic psychology in education, which are mainly expressed through humanistic psychology. Self-motivated learning activities can enable students to focus and identify problems and allow students to think about problems and find answers on their own. In this way, learning can inspire students’ thinking, enhance students’ knowledge and ability, and cultivate students’ interest in learning. These can make students love knowledge and study hard because of a sense of achievement. In the learning process, teachers must make students feel that teachers are trustworthy and sincere. Student-oriented foreign language teaching needs to respect students’ individual characteristics or to understand students’ existing knowledge, skill value, interesting life experiences, learning methods, and needs [10].The humanistic showing idea not just focuses on the mental advancement in the showing system, yet additionally focuses on the mental improvement in the showing system, and focuses on the understudies’ feelings, interests, and advancement regulations. It is necessary to understand the inner psychology of students in order to meet the interests, needs, experience, and individual differences of students, so that students can realize their potential development and stimulate the interaction between students’ potential cognition and emotion. The greatest contribution to humanism is to see the consistency of students’ nature and psychology and advocate people-oriented. Therefore, it is necessary for teachers to understand humanistic thought and apply it to daily teaching. The sandwich story teaching method itself embodies people-oriented and student-oriented, because students like stories. If students are taught what they like, the effect can be good.


#### 3.1.3. Constructivism

Constructivism can be examined from two perspectives: cognitive constructivism and social constructivism. Cognitive constructivism focuses on the internal knowledge structure of individuals. It emphasizes individuals’ pursuit of meaning as they interact with their environment, testing and modifying existing patterns. Social connections influence the process but are primarily a catalyst for personal cognitive conflict. Assuming that children are learning about the external environment around them, they will be more effective at learning things to generate ideas when they manipulate concrete objects (such as blocks and sticks), rather than having them introduced by a teacher or other expert. Therefore, it is important to provide engaging materials and a supportive learning environment. This is another point. Social constructivism, which is strongly influenced by Vygotsky’s writings, suggests that the learner is first in the social context and then appropriates and internalizes it. Learning is a constructive process. People build their own measure of understanding and information on their general surroundings by encountering things and thinking about those encounters. Only when the child self-constructs knowledge can knowledge become part of its structure. Social constructivism also emphasizes the role of teachers and advises them to consider all traditional teaching issues: how to organize and implement learning activities, motivate students, and assess learning [11].In a word, constructivism can be summarized as taking students as the center, considering students’ emotions, and emphasizing the interaction between teachers and students. The sandwich story pedagogy considers the psychological state of elementary school students who are developing their sense of self-efficacy. While planning showing undertakings, educators should consider whether the errand can be acknowledged by understudies and whether it can animate understudies’ advantage and cause understudies to genuinely want to partake in learning. Constructivism has formed its own interpretation of learning, and it is a meaning that allows students to actively construct students. A real learning environment is essential for learning, which is closer to real life and more meaningful in a real learning environment. The role of the teacher is to create an environment as realistic as possible, where students can generate new ideas from their previous experiences [12]. Teachers should adjust the teaching method and teach according to the actual situation in the teaching process. In addition, knowledge acquisition is not a process in which students mechanically master the main knowledge but a process in which a specific topic or problem is explored and solved in a specific context. Teaching should emphasize enabling students to use the activities they have to learn to discover and allow students to analyze and solve problems in real-world situations. Constructivists propose that instructors ought to assist understudies with building their own significant, reasonable, and practical portrayals of the outer world.


#### 3.1.4. Advantages of the Sandwich Teaching Method Based on the Internet Interactive Mode

Compared with the traditional sandwich teaching method, the sandwich teaching method based on the Internet interaction mode has more obvious advantages, which are reflected in (1) breaking through time and space constraints, (2) flexible syllabus production, (3) better reflecting individualized teaching, and (4) improving learning enthusiasm and increasing satisfaction.


### 3.2. Data Mining Technology

Big data is a very hot research field at present. Literally, big data refers to a large collection of data, which includes various types of data [13]. First of all, big data requires massive data. For logistic enterprises, they will collect a lot of data every day, including procurement data, sales data, transportation data, and data formed by cooperation with upstream and downstream enterprises, including customer data, storage data, consumer demand data, and consumer feedback data. After these data are collected, a large amount of data resources will be formed. However, collecting large amounts of data cannot be used directly in research. Data cleaning is also required to eliminate extreme values and leave usable values.Secondly, big data is data of diversity. Big data can come from multiple processes, such as transaction information between dealers and logistic companies, information on the circulation of goods in logistic companies’ warehouses, and consumer purchase data. There are various sources. There are several types of big data, which can be in the form of numbers, videos, etc. After aggregation, the diversity of big data is formed; the functions of big data are also diverse. Through big data, multiple functions can be realized for enterprises to use. Big data technology can speed up information processing, speed up the flow of upstream and downstream information, and transmit information in a timely manner to reduce information asymmetry, thereby improving service efficiency. It can conduct customer portrait analysis and customer demand forecasting based on existing data to improve and upgrade products and promote enterprises to improve service quality. In addition, big data also plays a role in improving service functions. By collecting customer satisfaction, customer evaluations, and user opinions, it can improve product defects or develop new services based on forecasting customer needs, thereby enriching service functions [14].As of now, the use of large information innovation depends on the qualities of enormous information; the incorporation and usage of huge information assets are the mining of large information. Big data is mineable, and enterprises can repeatedly mine, clean, and reuse big data according to their own needs [15, 16]. Therefore, big data is also called digital resources. At present, it is necessary to make good use of the huge resource pool of big data to develop the digital economy. Based on ensuring information security, multidirectional information is collected, and data is mined for data analysis, thereby enabling services and realizing value added.


#### 3.2.1. The Meaning of Data Mining Technology

Data mining refers to the process of searching out the hidden information from a large amount of data through algorithms, which is a step in the discovery of data knowledge. The methods used by data mining technology to achieve the goals usually adopt these methods: statistical and analytical processing, situation retrieval, machine learning, expert systems, and pattern recognition. The application of these methods is inseparable from the development of computer science [17].The next part presents several methods of operation commonly used in data mining:(1)
Neural Network. For data mining problems, neural networks are generally a more suitable solution. The reason is that it has some excellent characteristics, such as good robustness, parallel processing, distributed storage, self-organization adaptability, and high fault tolerance, and different neural network models should be used when the data mining application is different. For example, for data mining problems such as prediction, classification, and pattern recognition, it is more appropriate to use a feedforward neural network model. The neural network model also has the inconvenience; that is, it is usually called “black box,” and the learning and decision-making methods of the network are difficult to be understood and used by people during the whole processing process.(2)
Decision Tree Method. The idea of the decision tree method is to first classify a large amount of disordered data purposefully and then find out those hidden and valuable information. It is often used in the building of predictive models. The decision tree method can classify quickly and describe it simply and clearly, but there are still some problems: a functional relationship (a deterministic relationship that can be represented by a functional formula) and a correlation relationship (a certain related deterministic relationship but cannot be represented by a functional formula).(3)
Statistical Analysis Method. In a database, there are two types of relationships that exist between field items: functional relationships and related relationships.(4)
Fuzzy Set Method. When using the fuzzy set method in practical problems, the intensity of fuzziness depends on the complexity of the system: when the complexity is low, the fuzziness is poor.


#### 3.2.2. Artificial Neural Network Modeling

The Artificial Neural Network (ANN) neural network is a technology that simulates human intelligent behavior [18, 19]. The input layer is composed of multiple neurons, which can accept a large amount of nonlinear input information, which is called input vector; the information in the output layer is connected between neurons through a series of transmission, analysis, and trade-offs. The structure of the operation model of neural network is mainly divided into two parts, many nodes and the connection part between nodes. Each node in the network represents a specific output function (also called an excitation function), and the connection between each two nodes represents a weighted value (also called a weight) for the signal passing through this connection. Since the composition, connection method, weighting value, and output function of each network are different, the final output of the network is also different. Its model is shown in Figure 2.Figure 2
Neural network model.ANN can be classified from different perspectives (network performance, structure, learning style, etc.). Taking the common multilayer structure forward network as an example, the network consists of three parts: input layer, hidden layer, and output layer.


#### 3.2.3. BP Neural Network Theory

The neural units on the same layer of the BP neural network are not connected to each other, but each layer is connected to each other, generally using a fully connected method. The whole network is one-way propagation, and the structure of the 3-layer BP neural network is shown in Figure3 [20].Figure 3
Structure diagram of 3-layer BP neural network.The learning process and steps of BP network are as follows:(1)
Initialize. On the interval (-1, 1), random values are assigned to each weighted valueQij and Djs; threshold δj and χs are drawn(2)
The formula is as follows:(1)Tj=∑i=1vAiQij+δj,(2)Pj=gTj(3)
According to the outputPj, connection weight Djs, and threshold χs of the hidden layer, the input Hs of each neural unit on the output layer is calculated, and then, the output xs is calculated according to the transfer function. The calculation formula is as follows:(3)Hs=∑j=1pKjDjs+χs,(4)xj=gHs(4)
According to the target vectorSr and the actual output Xs of the network, the generalized error Jsr for each unit on the output layer is calculated. The formula is as follows:(5)Jsr=Bsr−Xs⋅Xs⋅1−Xs(5)
The calculation formula is as follows:(6)Ejr=∑s=1mJsr⋅Djs⋅Pj⋅1−Pj(6)
According to the resultPj of every unit on the secret layer and the summed up blunder Jsr of every unit on the result layer, the edge χs and the association weight Djs are remedied:(7)DjsG+1=DjsV+α⋅Jsr⋅Pj,χsG+1=χsG+α⋅Jsr0<α<1(7)
According to the inputAi on each unit of the input layer and the generalized error Ejr on each unit of the hidden layer, the correction of the connection weight Qij and the threshold δj is completed:(8)QijG+1=QijG+β⋅Ejr⋅αir,δjG+1=δjG+β⋅Ejr0<β<1(8)
After completing these several training steps, the input samples are randomly selected and given to the network for repeated training operations until the training of all samples is completed. After the network training is completed, its convergence should be judged, and the number of learning times of the network is also set in advance. Once the number of learning times after training is larger than that set in advance, the trained network cannot converge [21]The learning process of the entire network mainly includes these eight steps. After the training, the performance of the network needs to be analyzed and tested. The data of the test set needs to complete the test of the network, randomly select the samples in the test set to input the network, and test the performance of the network by checking the classification results of the network. It should be noted that the learning samples of the network and the test sample set cannot have the same pattern, so that the performance of the network can be tested more accurately [22, 23].


### 3.3. Deep Learning

Various network frameworks such as Deep Neural Network (DNN), convolutional neural network, Deep Belief Network (DBN), and Recurrent Neural Network (RNN) are commonly used deep learning frameworks. Deep learning is a very popular method in machine learning and covers a wide range of theoretical perspectives. The convolutional neural network is a neural network that is mainly composed of convolutional layers and includes various types of network layers. The basic structure of the convolutional neural network is shown in Figure4.Figure 4
Deep convolutional neural network architecture.


#### 3.3.1. Convolutional Layer

Deep convolution operations are divided into continuous convolution and discrete convolution.The formula for continuous convolution is(9)Ys=∫−∞∞XpJs−pdp=Xs⋅Js.The discrete convolution formula is(10)Ym=∑−∞∞XiJm−i=Xm⋅Jm.The convolution operation of the convolutional neural network is a discrete convolution operation, but it is somewhat different from the definition in analytical mathematics, and it is a linear operation.The formula is expressed as(11)gX,Y⋅ωX,Y=∑k=−vv∑s=−ccωk,sgX−k,Y−s.In the formula,gX,Y is the gray value of X row and Y column in the image and ωX,Y is the convolution kernel or filter.


#### 3.3.2. Downsampling Layer

The algorithm expression for average downsampling is(12)Kij=∑i=1d∑j=1dGijd2+c2.The algorithm expression for maximum downsampling is(13)Kij=maxi=1,j=1Gij+c2.In the formula,G is the feature map matrix, K is the obtained downsampling feature map, d is the movement step, and maxi=1,j=1Gij is the largest element taken from a region of size d×d in the input feature map G.


#### 3.3.3. Logistic Regression and Softmax Layer

The logistic regression algorithm is a classification algorithm with output values ranging from(14)0≤JθX≤1.It is suitable for the case where the value of labelB is discrete, such as 0, 1, 1, 0. It can be assumed:
(15)JθX=fθsX.In the formula,X is the feature vector and f is the logical function.The sigmoid function formula is(16)fz=11+e−z.


## 3.1. Sandwich Teaching Method

### 3.1.1. The Concept of Sandwich Teaching Method

The “sandwich” teaching method originated from the Sunderland Institute of Technology in the United Kingdom. Later, the School of Medicine of Heidelberg University in Germany applied the sandwich teaching concept to small medical class teaching and established the sandwich medical teaching method. The sandwich medical teaching method is a new type of teaching method, which enables teachers and students to communicate continuously, stimulates students’ enthusiasm for active learning, and improves students’ ability of autonomous learning, reflection, and inquiry [8, 9]. In small class teaching, students’ new knowledge is cultivated through group discussions, student cross-study, and student study reports. After practical application by students, this method has been adopted by majors in most colleges and universities and has been gradually promoted in medical colleges in other countries (Figure 1).Figure 1
The basic flow of the sandwich pedagogy.


### 3.1.2. Humanistic Psychology Perspective

Humanistic philosophy basically believes that everyone has the value and the right to achieve self-realization and rational thinking. Humanistic psychologists postulate that an individual’s actions are linked to their inner feelings and self-image. The study of humanistic theory refers to the teaching philosophy or the concepts, attitudes, and ideas advocated by humanistic psychology in education, which are mainly expressed through humanistic psychology. Self-motivated learning activities can enable students to focus and identify problems and allow students to think about problems and find answers on their own. In this way, learning can inspire students’ thinking, enhance students’ knowledge and ability, and cultivate students’ interest in learning. These can make students love knowledge and study hard because of a sense of achievement. In the learning process, teachers must make students feel that teachers are trustworthy and sincere. Student-oriented foreign language teaching needs to respect students’ individual characteristics or to understand students’ existing knowledge, skill value, interesting life experiences, learning methods, and needs [10].The humanistic showing idea not just focuses on the mental advancement in the showing system, yet additionally focuses on the mental improvement in the showing system, and focuses on the understudies’ feelings, interests, and advancement regulations. It is necessary to understand the inner psychology of students in order to meet the interests, needs, experience, and individual differences of students, so that students can realize their potential development and stimulate the interaction between students’ potential cognition and emotion. The greatest contribution to humanism is to see the consistency of students’ nature and psychology and advocate people-oriented. Therefore, it is necessary for teachers to understand humanistic thought and apply it to daily teaching. The sandwich story teaching method itself embodies people-oriented and student-oriented, because students like stories. If students are taught what they like, the effect can be good.


### 3.1.3. Constructivism

Constructivism can be examined from two perspectives: cognitive constructivism and social constructivism. Cognitive constructivism focuses on the internal knowledge structure of individuals. It emphasizes individuals’ pursuit of meaning as they interact with their environment, testing and modifying existing patterns. Social connections influence the process but are primarily a catalyst for personal cognitive conflict. Assuming that children are learning about the external environment around them, they will be more effective at learning things to generate ideas when they manipulate concrete objects (such as blocks and sticks), rather than having them introduced by a teacher or other expert. Therefore, it is important to provide engaging materials and a supportive learning environment. This is another point. Social constructivism, which is strongly influenced by Vygotsky’s writings, suggests that the learner is first in the social context and then appropriates and internalizes it. Learning is a constructive process. People build their own measure of understanding and information on their general surroundings by encountering things and thinking about those encounters. Only when the child self-constructs knowledge can knowledge become part of its structure. Social constructivism also emphasizes the role of teachers and advises them to consider all traditional teaching issues: how to organize and implement learning activities, motivate students, and assess learning [11].In a word, constructivism can be summarized as taking students as the center, considering students’ emotions, and emphasizing the interaction between teachers and students. The sandwich story pedagogy considers the psychological state of elementary school students who are developing their sense of self-efficacy. While planning showing undertakings, educators should consider whether the errand can be acknowledged by understudies and whether it can animate understudies’ advantage and cause understudies to genuinely want to partake in learning. Constructivism has formed its own interpretation of learning, and it is a meaning that allows students to actively construct students. A real learning environment is essential for learning, which is closer to real life and more meaningful in a real learning environment. The role of the teacher is to create an environment as realistic as possible, where students can generate new ideas from their previous experiences [12]. Teachers should adjust the teaching method and teach according to the actual situation in the teaching process. In addition, knowledge acquisition is not a process in which students mechanically master the main knowledge but a process in which a specific topic or problem is explored and solved in a specific context. Teaching should emphasize enabling students to use the activities they have to learn to discover and allow students to analyze and solve problems in real-world situations. Constructivists propose that instructors ought to assist understudies with building their own significant, reasonable, and practical portrayals of the outer world.


### 3.1.4. Advantages of the Sandwich Teaching Method Based on the Internet Interactive Mode

Compared with the traditional sandwich teaching method, the sandwich teaching method based on the Internet interaction mode has more obvious advantages, which are reflected in (1) breaking through time and space constraints, (2) flexible syllabus production, (3) better reflecting individualized teaching, and (4) improving learning enthusiasm and increasing satisfaction.


## 3.1.1. The Concept of Sandwich Teaching Method

The “sandwich” teaching method originated from the Sunderland Institute of Technology in the United Kingdom. Later, the School of Medicine of Heidelberg University in Germany applied the sandwich teaching concept to small medical class teaching and established the sandwich medical teaching method. The sandwich medical teaching method is a new type of teaching method, which enables teachers and students to communicate continuously, stimulates students’ enthusiasm for active learning, and improves students’ ability of autonomous learning, reflection, and inquiry [8, 9]. In small class teaching, students’ new knowledge is cultivated through group discussions, student cross-study, and student study reports. After practical application by students, this method has been adopted by majors in most colleges and universities and has been gradually promoted in medical colleges in other countries (Figure 1).Figure 1
The basic flow of the sandwich pedagogy.


## 3.1.2. Humanistic Psychology Perspective

Humanistic philosophy basically believes that everyone has the value and the right to achieve self-realization and rational thinking. Humanistic psychologists postulate that an individual’s actions are linked to their inner feelings and self-image. The study of humanistic theory refers to the teaching philosophy or the concepts, attitudes, and ideas advocated by humanistic psychology in education, which are mainly expressed through humanistic psychology. Self-motivated learning activities can enable students to focus and identify problems and allow students to think about problems and find answers on their own. In this way, learning can inspire students’ thinking, enhance students’ knowledge and ability, and cultivate students’ interest in learning. These can make students love knowledge and study hard because of a sense of achievement. In the learning process, teachers must make students feel that teachers are trustworthy and sincere. Student-oriented foreign language teaching needs to respect students’ individual characteristics or to understand students’ existing knowledge, skill value, interesting life experiences, learning methods, and needs [10].The humanistic showing idea not just focuses on the mental advancement in the showing system, yet additionally focuses on the mental improvement in the showing system, and focuses on the understudies’ feelings, interests, and advancement regulations. It is necessary to understand the inner psychology of students in order to meet the interests, needs, experience, and individual differences of students, so that students can realize their potential development and stimulate the interaction between students’ potential cognition and emotion. The greatest contribution to humanism is to see the consistency of students’ nature and psychology and advocate people-oriented. Therefore, it is necessary for teachers to understand humanistic thought and apply it to daily teaching. The sandwich story teaching method itself embodies people-oriented and student-oriented, because students like stories. If students are taught what they like, the effect can be good.


## 3.1.3. Constructivism

Constructivism can be examined from two perspectives: cognitive constructivism and social constructivism. Cognitive constructivism focuses on the internal knowledge structure of individuals. It emphasizes individuals’ pursuit of meaning as they interact with their environment, testing and modifying existing patterns. Social connections influence the process but are primarily a catalyst for personal cognitive conflict. Assuming that children are learning about the external environment around them, they will be more effective at learning things to generate ideas when they manipulate concrete objects (such as blocks and sticks), rather than having them introduced by a teacher or other expert. Therefore, it is important to provide engaging materials and a supportive learning environment. This is another point. Social constructivism, which is strongly influenced by Vygotsky’s writings, suggests that the learner is first in the social context and then appropriates and internalizes it. Learning is a constructive process. People build their own measure of understanding and information on their general surroundings by encountering things and thinking about those encounters. Only when the child self-constructs knowledge can knowledge become part of its structure. Social constructivism also emphasizes the role of teachers and advises them to consider all traditional teaching issues: how to organize and implement learning activities, motivate students, and assess learning [11].In a word, constructivism can be summarized as taking students as the center, considering students’ emotions, and emphasizing the interaction between teachers and students. The sandwich story pedagogy considers the psychological state of elementary school students who are developing their sense of self-efficacy. While planning showing undertakings, educators should consider whether the errand can be acknowledged by understudies and whether it can animate understudies’ advantage and cause understudies to genuinely want to partake in learning. Constructivism has formed its own interpretation of learning, and it is a meaning that allows students to actively construct students. A real learning environment is essential for learning, which is closer to real life and more meaningful in a real learning environment. The role of the teacher is to create an environment as realistic as possible, where students can generate new ideas from their previous experiences [12]. Teachers should adjust the teaching method and teach according to the actual situation in the teaching process. In addition, knowledge acquisition is not a process in which students mechanically master the main knowledge but a process in which a specific topic or problem is explored and solved in a specific context. Teaching should emphasize enabling students to use the activities they have to learn to discover and allow students to analyze and solve problems in real-world situations. Constructivists propose that instructors ought to assist understudies with building their own significant, reasonable, and practical portrayals of the outer world.


## 3.1.4. Advantages of the Sandwich Teaching Method Based on the Internet Interactive Mode

Compared with the traditional sandwich teaching method, the sandwich teaching method based on the Internet interaction mode has more obvious advantages, which are reflected in (1) breaking through time and space constraints, (2) flexible syllabus production, (3) better reflecting individualized teaching, and (4) improving learning enthusiasm and increasing satisfaction.


## 3.2. Data Mining Technology

Big data is a very hot research field at present. Literally, big data refers to a large collection of data, which includes various types of data [13]. First of all, big data requires massive data. For logistic enterprises, they will collect a lot of data every day, including procurement data, sales data, transportation data, and data formed by cooperation with upstream and downstream enterprises, including customer data, storage data, consumer demand data, and consumer feedback data. After these data are collected, a large amount of data resources will be formed. However, collecting large amounts of data cannot be used directly in research. Data cleaning is also required to eliminate extreme values and leave usable values.Secondly, big data is data of diversity. Big data can come from multiple processes, such as transaction information between dealers and logistic companies, information on the circulation of goods in logistic companies’ warehouses, and consumer purchase data. There are various sources. There are several types of big data, which can be in the form of numbers, videos, etc. After aggregation, the diversity of big data is formed; the functions of big data are also diverse. Through big data, multiple functions can be realized for enterprises to use. Big data technology can speed up information processing, speed up the flow of upstream and downstream information, and transmit information in a timely manner to reduce information asymmetry, thereby improving service efficiency. It can conduct customer portrait analysis and customer demand forecasting based on existing data to improve and upgrade products and promote enterprises to improve service quality. In addition, big data also plays a role in improving service functions. By collecting customer satisfaction, customer evaluations, and user opinions, it can improve product defects or develop new services based on forecasting customer needs, thereby enriching service functions [14].As of now, the use of large information innovation depends on the qualities of enormous information; the incorporation and usage of huge information assets are the mining of large information. Big data is mineable, and enterprises can repeatedly mine, clean, and reuse big data according to their own needs [15, 16]. Therefore, big data is also called digital resources. At present, it is necessary to make good use of the huge resource pool of big data to develop the digital economy. Based on ensuring information security, multidirectional information is collected, and data is mined for data analysis, thereby enabling services and realizing value added.


### 3.2.1. The Meaning of Data Mining Technology

Data mining refers to the process of searching out the hidden information from a large amount of data through algorithms, which is a step in the discovery of data knowledge. The methods used by data mining technology to achieve the goals usually adopt these methods: statistical and analytical processing, situation retrieval, machine learning, expert systems, and pattern recognition. The application of these methods is inseparable from the development of computer science [17].The next part presents several methods of operation commonly used in data mining:(1)
Neural Network. For data mining problems, neural networks are generally a more suitable solution. The reason is that it has some excellent characteristics, such as good robustness, parallel processing, distributed storage, self-organization adaptability, and high fault tolerance, and different neural network models should be used when the data mining application is different. For example, for data mining problems such as prediction, classification, and pattern recognition, it is more appropriate to use a feedforward neural network model. The neural network model also has the inconvenience; that is, it is usually called “black box,” and the learning and decision-making methods of the network are difficult to be understood and used by people during the whole processing process.(2)
Decision Tree Method. The idea of the decision tree method is to first classify a large amount of disordered data purposefully and then find out those hidden and valuable information. It is often used in the building of predictive models. The decision tree method can classify quickly and describe it simply and clearly, but there are still some problems: a functional relationship (a deterministic relationship that can be represented by a functional formula) and a correlation relationship (a certain related deterministic relationship but cannot be represented by a functional formula).(3)
Statistical Analysis Method. In a database, there are two types of relationships that exist between field items: functional relationships and related relationships.(4)
Fuzzy Set Method. When using the fuzzy set method in practical problems, the intensity of fuzziness depends on the complexity of the system: when the complexity is low, the fuzziness is poor.


### 3.2.2. Artificial Neural Network Modeling

The Artificial Neural Network (ANN) neural network is a technology that simulates human intelligent behavior [18, 19]. The input layer is composed of multiple neurons, which can accept a large amount of nonlinear input information, which is called input vector; the information in the output layer is connected between neurons through a series of transmission, analysis, and trade-offs. The structure of the operation model of neural network is mainly divided into two parts, many nodes and the connection part between nodes. Each node in the network represents a specific output function (also called an excitation function), and the connection between each two nodes represents a weighted value (also called a weight) for the signal passing through this connection. Since the composition, connection method, weighting value, and output function of each network are different, the final output of the network is also different. Its model is shown in Figure 2.Figure 2
Neural network model.ANN can be classified from different perspectives (network performance, structure, learning style, etc.). Taking the common multilayer structure forward network as an example, the network consists of three parts: input layer, hidden layer, and output layer.


### 3.2.3. BP Neural Network Theory

The neural units on the same layer of the BP neural network are not connected to each other, but each layer is connected to each other, generally using a fully connected method. The whole network is one-way propagation, and the structure of the 3-layer BP neural network is shown in Figure3 [20].Figure 3
Structure diagram of 3-layer BP neural network.The learning process and steps of BP network are as follows:(1)
Initialize. On the interval (-1, 1), random values are assigned to each weighted valueQij and Djs; threshold δj and χs are drawn(2)
The formula is as follows:(1)Tj=∑i=1vAiQij+δj,(2)Pj=gTj(3)
According to the outputPj, connection weight Djs, and threshold χs of the hidden layer, the input Hs of each neural unit on the output layer is calculated, and then, the output xs is calculated according to the transfer function. The calculation formula is as follows:(3)Hs=∑j=1pKjDjs+χs,(4)xj=gHs(4)
According to the target vectorSr and the actual output Xs of the network, the generalized error Jsr for each unit on the output layer is calculated. The formula is as follows:(5)Jsr=Bsr−Xs⋅Xs⋅1−Xs(5)
The calculation formula is as follows:(6)Ejr=∑s=1mJsr⋅Djs⋅Pj⋅1−Pj(6)
According to the resultPj of every unit on the secret layer and the summed up blunder Jsr of every unit on the result layer, the edge χs and the association weight Djs are remedied:(7)DjsG+1=DjsV+α⋅Jsr⋅Pj,χsG+1=χsG+α⋅Jsr0<α<1(7)
According to the inputAi on each unit of the input layer and the generalized error Ejr on each unit of the hidden layer, the correction of the connection weight Qij and the threshold δj is completed:(8)QijG+1=QijG+β⋅Ejr⋅αir,δjG+1=δjG+β⋅Ejr0<β<1(8)
After completing these several training steps, the input samples are randomly selected and given to the network for repeated training operations until the training of all samples is completed. After the network training is completed, its convergence should be judged, and the number of learning times of the network is also set in advance. Once the number of learning times after training is larger than that set in advance, the trained network cannot converge [21]The learning process of the entire network mainly includes these eight steps. After the training, the performance of the network needs to be analyzed and tested. The data of the test set needs to complete the test of the network, randomly select the samples in the test set to input the network, and test the performance of the network by checking the classification results of the network. It should be noted that the learning samples of the network and the test sample set cannot have the same pattern, so that the performance of the network can be tested more accurately [22, 23].


## 3.2.1. The Meaning of Data Mining Technology

Data mining refers to the process of searching out the hidden information from a large amount of data through algorithms, which is a step in the discovery of data knowledge. The methods used by data mining technology to achieve the goals usually adopt these methods: statistical and analytical processing, situation retrieval, machine learning, expert systems, and pattern recognition. The application of these methods is inseparable from the development of computer science [17].The next part presents several methods of operation commonly used in data mining:(1)
Neural Network. For data mining problems, neural networks are generally a more suitable solution. The reason is that it has some excellent characteristics, such as good robustness, parallel processing, distributed storage, self-organization adaptability, and high fault tolerance, and different neural network models should be used when the data mining application is different. For example, for data mining problems such as prediction, classification, and pattern recognition, it is more appropriate to use a feedforward neural network model. The neural network model also has the inconvenience; that is, it is usually called “black box,” and the learning and decision-making methods of the network are difficult to be understood and used by people during the whole processing process.(2)
Decision Tree Method. The idea of the decision tree method is to first classify a large amount of disordered data purposefully and then find out those hidden and valuable information. It is often used in the building of predictive models. The decision tree method can classify quickly and describe it simply and clearly, but there are still some problems: a functional relationship (a deterministic relationship that can be represented by a functional formula) and a correlation relationship (a certain related deterministic relationship but cannot be represented by a functional formula).(3)
Statistical Analysis Method. In a database, there are two types of relationships that exist between field items: functional relationships and related relationships.(4)
Fuzzy Set Method. When using the fuzzy set method in practical problems, the intensity of fuzziness depends on the complexity of the system: when the complexity is low, the fuzziness is poor.


## 3.2.2. Artificial Neural Network Modeling

The Artificial Neural Network (ANN) neural network is a technology that simulates human intelligent behavior [18, 19]. The input layer is composed of multiple neurons, which can accept a large amount of nonlinear input information, which is called input vector; the information in the output layer is connected between neurons through a series of transmission, analysis, and trade-offs. The structure of the operation model of neural network is mainly divided into two parts, many nodes and the connection part between nodes. Each node in the network represents a specific output function (also called an excitation function), and the connection between each two nodes represents a weighted value (also called a weight) for the signal passing through this connection. Since the composition, connection method, weighting value, and output function of each network are different, the final output of the network is also different. Its model is shown in Figure 2.Figure 2
Neural network model.ANN can be classified from different perspectives (network performance, structure, learning style, etc.). Taking the common multilayer structure forward network as an example, the network consists of three parts: input layer, hidden layer, and output layer.


## 3.2.3. BP Neural Network Theory

The neural units on the same layer of the BP neural network are not connected to each other, but each layer is connected to each other, generally using a fully connected method. The whole network is one-way propagation, and the structure of the 3-layer BP neural network is shown in Figure3 [20].Figure 3
Structure diagram of 3-layer BP neural network.The learning process and steps of BP network are as follows:(1)
Initialize. On the interval (-1, 1), random values are assigned to each weighted valueQij and Djs; threshold δj and χs are drawn(2)
The formula is as follows:(1)Tj=∑i=1vAiQij+δj,(2)Pj=gTj(3)
According to the outputPj, connection weight Djs, and threshold χs of the hidden layer, the input Hs of each neural unit on the output layer is calculated, and then, the output xs is calculated according to the transfer function. The calculation formula is as follows:(3)Hs=∑j=1pKjDjs+χs,(4)xj=gHs(4)
According to the target vectorSr and the actual output Xs of the network, the generalized error Jsr for each unit on the output layer is calculated. The formula is as follows:(5)Jsr=Bsr−Xs⋅Xs⋅1−Xs(5)
The calculation formula is as follows:(6)Ejr=∑s=1mJsr⋅Djs⋅Pj⋅1−Pj(6)
According to the resultPj of every unit on the secret layer and the summed up blunder Jsr of every unit on the result layer, the edge χs and the association weight Djs are remedied:(7)DjsG+1=DjsV+α⋅Jsr⋅Pj,χsG+1=χsG+α⋅Jsr0<α<1(7)
According to the inputAi on each unit of the input layer and the generalized error Ejr on each unit of the hidden layer, the correction of the connection weight Qij and the threshold δj is completed:(8)QijG+1=QijG+β⋅Ejr⋅αir,δjG+1=δjG+β⋅Ejr0<β<1(8)
After completing these several training steps, the input samples are randomly selected and given to the network for repeated training operations until the training of all samples is completed. After the network training is completed, its convergence should be judged, and the number of learning times of the network is also set in advance. Once the number of learning times after training is larger than that set in advance, the trained network cannot converge [21]The learning process of the entire network mainly includes these eight steps. After the training, the performance of the network needs to be analyzed and tested. The data of the test set needs to complete the test of the network, randomly select the samples in the test set to input the network, and test the performance of the network by checking the classification results of the network. It should be noted that the learning samples of the network and the test sample set cannot have the same pattern, so that the performance of the network can be tested more accurately [22, 23].


## 3.3. Deep Learning

Various network frameworks such as Deep Neural Network (DNN), convolutional neural network, Deep Belief Network (DBN), and Recurrent Neural Network (RNN) are commonly used deep learning frameworks. Deep learning is a very popular method in machine learning and covers a wide range of theoretical perspectives. The convolutional neural network is a neural network that is mainly composed of convolutional layers and includes various types of network layers. The basic structure of the convolutional neural network is shown in Figure4.Figure 4
Deep convolutional neural network architecture.


### 3.3.1. Convolutional Layer

Deep convolution operations are divided into continuous convolution and discrete convolution.The formula for continuous convolution is(9)Ys=∫−∞∞XpJs−pdp=Xs⋅Js.The discrete convolution formula is(10)Ym=∑−∞∞XiJm−i=Xm⋅Jm.The convolution operation of the convolutional neural network is a discrete convolution operation, but it is somewhat different from the definition in analytical mathematics, and it is a linear operation.The formula is expressed as(11)gX,Y⋅ωX,Y=∑k=−vv∑s=−ccωk,sgX−k,Y−s.In the formula,gX,Y is the gray value of X row and Y column in the image and ωX,Y is the convolution kernel or filter.


### 3.3.2. Downsampling Layer

The algorithm expression for average downsampling is(12)Kij=∑i=1d∑j=1dGijd2+c2.The algorithm expression for maximum downsampling is(13)Kij=maxi=1,j=1Gij+c2.In the formula,G is the feature map matrix, K is the obtained downsampling feature map, d is the movement step, and maxi=1,j=1Gij is the largest element taken from a region of size d×d in the input feature map G.


### 3.3.3. Logistic Regression and Softmax Layer

The logistic regression algorithm is a classification algorithm with output values ranging from(14)0≤JθX≤1.It is suitable for the case where the value of labelB is discrete, such as 0, 1, 1, 0. It can be assumed:
(15)JθX=fθsX.In the formula,X is the feature vector and f is the logical function.The sigmoid function formula is(16)fz=11+e−z.


## 3.3.1. Convolutional Layer

Deep convolution operations are divided into continuous convolution and discrete convolution.The formula for continuous convolution is(9)Ys=∫−∞∞XpJs−pdp=Xs⋅Js.The discrete convolution formula is(10)Ym=∑−∞∞XiJm−i=Xm⋅Jm.The convolution operation of the convolutional neural network is a discrete convolution operation, but it is somewhat different from the definition in analytical mathematics, and it is a linear operation.The formula is expressed as(11)gX,Y⋅ωX,Y=∑k=−vv∑s=−ccωk,sgX−k,Y−s.In the formula,gX,Y is the gray value of X row and Y column in the image and ωX,Y is the convolution kernel or filter.


## 3.3.2. Downsampling Layer

The algorithm expression for average downsampling is(12)Kij=∑i=1d∑j=1dGijd2+c2.The algorithm expression for maximum downsampling is(13)Kij=maxi=1,j=1Gij+c2.In the formula,G is the feature map matrix, K is the obtained downsampling feature map, d is the movement step, and maxi=1,j=1Gij is the largest element taken from a region of size d×d in the input feature map G.


## 3.3.3. Logistic Regression and Softmax Layer

The logistic regression algorithm is a classification algorithm with output values ranging from(14)0≤JθX≤1.It is suitable for the case where the value of labelB is discrete, such as 0, 1, 1, 0. It can be assumed:
(15)JθX=fθsX.In the formula,X is the feature vector and f is the logical function.The sigmoid function formula is(16)fz=11+e−z.


## 4. Experiment of Sandwich Teaching Method in Rehabilitation Nursing Teaching

### 4.1. Two Groups of Basic Data

#### 4.1.1. Comparison of the Gender Composition of the Two Groups of Subjects

The subjects of this study were the third grade students in the department of nursing. The subjects were 63 cases, including 57 girls and 6 boys. There were 66 subjects in the control group, including 61 girls and 5 boys. After the chi-squared test between the two groups,χ2=0.156, p>0.05, the difference is not statistically significant, indicating that the groups are comparable (Table 1).Table 1
Comparison of the gender composition of the two groups of students.
GroupTest groupControl groupNumber of cases6366Girl student57 (90.48%)61 (92.42%)Schoolboy6 (9.52%)5 (7.58%)χ20.156p0.698


#### 4.1.2. Age Comparison of the Two Groups of Subjects

The minimum age of the students in the experimental group is 20 years, the maximum age is 25 years, and the average age is21.25±1.20 years. The youngest age of the students in the control group is 19 years, the oldest is 24 years old, and the average age is 21.18±0.96 years. The test group and the control group are tested by the t-test and two-sided test. The test results show that the test level is a=0.05, p>0.05. The difference is not statistically significant, and the groups are comparable (Table 2).Table 2
Age comparison of the two groups of students.
GroupTest groupControl groupNumber of cases6366Average age21.25±1.2021.18±0.96t0.387p0.710


#### 4.1.3. Comparison of the Average Grades of the Two Groups of Students in the Last Semester

The average score of the students in the experimental group in the last semester of the seven courses, including health assessment, is80.35±4.01. In the control group, the average score of seven courses including health assessment last semester is 80.15±3.05 points (Table 3).Table 3
Comparison of the average grades of the two groups in the last semester.
GroupTest groupControl groupNumber of cases6366Course average80.35±4.0180.15±3.05t0.426p0.672


#### 4.1.4. Comparison of the Source of Students between the Two Groups

There were 63 subjects in the experimental group, including 37 urban students and 26 rural students. There were 66 subjects in the control group, including 39 urban students and 27 rural students (Figure5).Figure 5
Comparison of origins between the two groups.


### 4.2. Comparison of Two Groups of Students before Intervention

#### 4.2.1. Comparison of Critical Thinking Ability

It can be seen from Figure6 that after the t-test, t=−0.032,p=0.974, and there is no statistically significant difference in the scores of the seven dimensions in the figure, which is comparable [24, 25].Figure 6
Comparison of critical thinking skills before intervention.


#### 4.2.2. Comparison of Learning Initiative

It can be seen from Figure7 that after the t-test, t=−0.632,p=0.529, the difference between the scores of the five dimensions shown in Figure 7 is not statistically significant, so they are comparable.Figure 7
Comparison of learning initiative before intervention.


### 4.3. Intragroup Comparison before and after Intervention

#### 4.3.1. Comparison of Critical Thinking Ability before and after Intervention

(1) Experimental Group. It can be seen from Figure 8(a) that after the t-test, t=−14.692,p≤0.001, and the difference in the scores of the six dimensions in the figure is statistically significant.Figure 8
Comparison of critical thinking skills before and after the intervention.
(a)
Experimental group(b)
Control group(2) Control Group. According to Figure 8(b), t=−0.406,p=0.686 after the t-test. The six dimension differences, opening mind (t=−1.140,p=0.258), analytical ability (t=−1.840,p=0.070), systematic ability (t=−1.143,p=0.257), confidence in critical thinking (t=1.211,p=0.230), desire for knowledge (t=1.349,p=0.182), and cognitive maturity (t=−0.708,p=0.481), have no statistical significance.


#### 4.3.2. Comparison of Students’ Learning Initiative before and after Intervention

(1) Experimental Group. It can be seen from Figure 9(a) that after the t-test, t=−29.713,p≤0.001, and the difference in the five dimensions in the figure is statistically significant.Figure 9
Comparison of learning initiative of two groups of students before and after intervention.
(a)
Experimental group(b)
Control group(2) Control Group. According to Figure 9(b), t=−3.368,p=0.001 after the t-test. Among them, there is learning drive (t=−5.779,p≤0.001). There are no statistical significance in the four dimensions of learning goal (t=−1.742,p=0.461), deep learning (t=−1.867,p=0.066), controlled learning (t=−1.010,p=0.316), and solid learning (t=−1.424,p=0.159).


## 4.1. Two Groups of Basic Data

### 4.1.1. Comparison of the Gender Composition of the Two Groups of Subjects

The subjects of this study were the third grade students in the department of nursing. The subjects were 63 cases, including 57 girls and 6 boys. There were 66 subjects in the control group, including 61 girls and 5 boys. After the chi-squared test between the two groups,χ2=0.156, p>0.05, the difference is not statistically significant, indicating that the groups are comparable (Table 1).Table 1
Comparison of the gender composition of the two groups of students.
GroupTest groupControl groupNumber of cases6366Girl student57 (90.48%)61 (92.42%)Schoolboy6 (9.52%)5 (7.58%)χ20.156p0.698


### 4.1.2. Age Comparison of the Two Groups of Subjects

The minimum age of the students in the experimental group is 20 years, the maximum age is 25 years, and the average age is21.25±1.20 years. The youngest age of the students in the control group is 19 years, the oldest is 24 years old, and the average age is 21.18±0.96 years. The test group and the control group are tested by the t-test and two-sided test. The test results show that the test level is a=0.05, p>0.05. The difference is not statistically significant, and the groups are comparable (Table 2).Table 2
Age comparison of the two groups of students.
GroupTest groupControl groupNumber of cases6366Average age21.25±1.2021.18±0.96t0.387p0.710


### 4.1.3. Comparison of the Average Grades of the Two Groups of Students in the Last Semester

The average score of the students in the experimental group in the last semester of the seven courses, including health assessment, is80.35±4.01. In the control group, the average score of seven courses including health assessment last semester is 80.15±3.05 points (Table 3).Table 3
Comparison of the average grades of the two groups in the last semester.
GroupTest groupControl groupNumber of cases6366Course average80.35±4.0180.15±3.05t0.426p0.672


### 4.1.4. Comparison of the Source of Students between the Two Groups

There were 63 subjects in the experimental group, including 37 urban students and 26 rural students. There were 66 subjects in the control group, including 39 urban students and 27 rural students (Figure5).Figure 5
Comparison of origins between the two groups.


## 4.1.1. Comparison of the Gender Composition of the Two Groups of Subjects

The subjects of this study were the third grade students in the department of nursing. The subjects were 63 cases, including 57 girls and 6 boys. There were 66 subjects in the control group, including 61 girls and 5 boys. After the chi-squared test between the two groups,χ2=0.156, p>0.05, the difference is not statistically significant, indicating that the groups are comparable (Table 1).Table 1
Comparison of the gender composition of the two groups of students.
GroupTest groupControl groupNumber of cases6366Girl student57 (90.48%)61 (92.42%)Schoolboy6 (9.52%)5 (7.58%)χ20.156p0.698


## 4.1.2. Age Comparison of the Two Groups of Subjects

The minimum age of the students in the experimental group is 20 years, the maximum age is 25 years, and the average age is21.25±1.20 years. The youngest age of the students in the control group is 19 years, the oldest is 24 years old, and the average age is 21.18±0.96 years. The test group and the control group are tested by the t-test and two-sided test. The test results show that the test level is a=0.05, p>0.05. The difference is not statistically significant, and the groups are comparable (Table 2).Table 2
Age comparison of the two groups of students.
GroupTest groupControl groupNumber of cases6366Average age21.25±1.2021.18±0.96t0.387p0.710


## 4.1.3. Comparison of the Average Grades of the Two Groups of Students in the Last Semester

The average score of the students in the experimental group in the last semester of the seven courses, including health assessment, is80.35±4.01. In the control group, the average score of seven courses including health assessment last semester is 80.15±3.05 points (Table 3).Table 3
Comparison of the average grades of the two groups in the last semester.
GroupTest groupControl groupNumber of cases6366Course average80.35±4.0180.15±3.05t0.426p0.672


## 4.1.4. Comparison of the Source of Students between the Two Groups

There were 63 subjects in the experimental group, including 37 urban students and 26 rural students. There were 66 subjects in the control group, including 39 urban students and 27 rural students (Figure5).Figure 5
Comparison of origins between the two groups.


## 4.2. Comparison of Two Groups of Students before Intervention

### 4.2.1. Comparison of Critical Thinking Ability

It can be seen from Figure6 that after the t-test, t=−0.032,p=0.974, and there is no statistically significant difference in the scores of the seven dimensions in the figure, which is comparable [24, 25].Figure 6
Comparison of critical thinking skills before intervention.


### 4.2.2. Comparison of Learning Initiative

It can be seen from Figure7 that after the t-test, t=−0.632,p=0.529, the difference between the scores of the five dimensions shown in Figure 7 is not statistically significant, so they are comparable.Figure 7
Comparison of learning initiative before intervention.


## 4.2.1. Comparison of Critical Thinking Ability

It can be seen from Figure6 that after the t-test, t=−0.032,p=0.974, and there is no statistically significant difference in the scores of the seven dimensions in the figure, which is comparable [24, 25].Figure 6
Comparison of critical thinking skills before intervention.


## 4.2.2. Comparison of Learning Initiative

It can be seen from Figure7 that after the t-test, t=−0.632,p=0.529, the difference between the scores of the five dimensions shown in Figure 7 is not statistically significant, so they are comparable.Figure 7
Comparison of learning initiative before intervention.


## 4.3. Intragroup Comparison before and after Intervention

### 4.3.1. Comparison of Critical Thinking Ability before and after Intervention

(1) Experimental Group. It can be seen from Figure 8(a) that after the t-test, t=−14.692,p≤0.001, and the difference in the scores of the six dimensions in the figure is statistically significant.Figure 8
Comparison of critical thinking skills before and after the intervention.
(a)
Experimental group(b)
Control group(2) Control Group. According to Figure 8(b), t=−0.406,p=0.686 after the t-test. The six dimension differences, opening mind (t=−1.140,p=0.258), analytical ability (t=−1.840,p=0.070), systematic ability (t=−1.143,p=0.257), confidence in critical thinking (t=1.211,p=0.230), desire for knowledge (t=1.349,p=0.182), and cognitive maturity (t=−0.708,p=0.481), have no statistical significance.


### 4.3.2. Comparison of Students’ Learning Initiative before and after Intervention

(1) Experimental Group. It can be seen from Figure 9(a) that after the t-test, t=−29.713,p≤0.001, and the difference in the five dimensions in the figure is statistically significant.Figure 9
Comparison of learning initiative of two groups of students before and after intervention.
(a)
Experimental group(b)
Control group(2) Control Group. According to Figure 9(b), t=−3.368,p=0.001 after the t-test. Among them, there is learning drive (t=−5.779,p≤0.001). There are no statistical significance in the four dimensions of learning goal (t=−1.742,p=0.461), deep learning (t=−1.867,p=0.066), controlled learning (t=−1.010,p=0.316), and solid learning (t=−1.424,p=0.159).


## 4.3.1. Comparison of Critical Thinking Ability before and after Intervention

(1) Experimental Group. It can be seen from Figure 8(a) that after the t-test, t=−14.692,p≤0.001, and the difference in the scores of the six dimensions in the figure is statistically significant.Figure 8
Comparison of critical thinking skills before and after the intervention.
(a)
Experimental group(b)
Control group(2) Control Group. According to Figure 8(b), t=−0.406,p=0.686 after the t-test. The six dimension differences, opening mind (t=−1.140,p=0.258), analytical ability (t=−1.840,p=0.070), systematic ability (t=−1.143,p=0.257), confidence in critical thinking (t=1.211,p=0.230), desire for knowledge (t=1.349,p=0.182), and cognitive maturity (t=−0.708,p=0.481), have no statistical significance.


## 4.3.2. Comparison of Students’ Learning Initiative before and after Intervention

(1) Experimental Group. It can be seen from Figure 9(a) that after the t-test, t=−29.713,p≤0.001, and the difference in the five dimensions in the figure is statistically significant.Figure 9
Comparison of learning initiative of two groups of students before and after intervention.
(a)
Experimental group(b)
Control group(2) Control Group. According to Figure 9(b), t=−3.368,p=0.001 after the t-test. Among them, there is learning drive (t=−5.779,p≤0.001). There are no statistical significance in the four dimensions of learning goal (t=−1.742,p=0.461), deep learning (t=−1.867,p=0.066), controlled learning (t=−1.010,p=0.316), and solid learning (t=−1.424,p=0.159).


## 5. Conclusions

The goal of modern medical education is to cultivate innovative and practical comprehensive talents. The cultivation of talents comes from the cause of medical education. To achieve this goal, it is necessary to promote the reform of education and teaching and improve the quality of teaching. Teachers not only “give a man fish” but also “teach him fishing.” Therefore, in order to change the concept in the teaching process, it must gradually change from passive knowledge instillation to active learning, pay attention to the cultivation of students’ ability and personality development, integrate quality education into the whole teaching process, and lay emphasis on improving students’ comprehensive ability. Using the “trinity” teaching method can stimulate students’ enthusiasm for learning and improve their ability to learn independently, think, and explore new knowledge.



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*Source: 7489023-2022-10-11.xml*