# Educational Reward and Punishment and the Effect of Psychological Intervention on Adolescent Depression

Authors: Wensheng Wu; Hao Chen
Journal: Journal of Environmental and Public Health
 (2022)

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## Abstract

Teenage depression, also known as TD, is a common mental illness that is characterized by symptoms such as hopelessness, helplessness, pessimism, depression, and decreased energy. It has always been a hot topic to discuss how rewards and punishments work in education. In order to prevent and treat adolescent depression, this study examines the mechanisms of educational reinforcement and punishment as well as psychological interventions. In this study, the activated brain regions are analyzed using data mining (DM) technology to determine whether they are significantly more or less active than the rest of the brain of students who are not experiencing negative emotions. When the word vector has 90 dimensions, the results demonstrate that the averageF1 value of the weighted word vector method is 81.3 percent. It has been established that the approach taken in this work offers a reliable way to diagnose TD.


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## Body

## 1. Introduction

Reward and punishment have always played a significant role in teacher preparation and classroom instruction. How to use rewards and punishments reasonably has become a very important topic as a result of the passage of time, the evolution of history, and the ongoing reinterpretation of educational concepts. The behavioral allocation of behaviors can be known and understood scientifically thanks to the behaviorism theory in psychology’s research findings. Giving students excessive rewards will therefore lead to psychological dependence. Rewards that are not necessary will reduce students’ intrinsic motivation to learn. Students who lack or lack intrinsic motivation for learning can be encouraged with external rewards so they can develop that motivation. According to studies, 65 percent of adolescents experience one or more major depressive episodes at some point in their lives [1]. Depression is the main risk factor for teen suicide [2]. Adolescence is when most patients experience their first depressive episode. As a result, TD (Teen Depression) started to show up in medical professionals’ clinical diagnoses, and its use in treating children and adolescents has gained widespread acceptance.At present, the direct goal of depression intervention research is to reduce and eliminate adolescents’ depression experience, but the prevention of depression is seldom considered. This study focuses on the intervention of school sense of belonging, which can promote the generation of positive emotions, and explore whether its influence on depression is mediated by neurotic personality traits, so as to provide more information for preventing adolescent depression to a greater extent. The necessity and possibility of punishment in the field of education, the educational purpose pursued by punishment, and the effectiveness of punishment as an educational means. The incidence of TD is high and the prognosis is poor, but the cause of depression has not been fully clarified. It is generally believed that it is the result of the interaction of biological, psychological, and social factors. Kessler et al. found that positive coping is a common influencing factor of depression and anxiety, and positive coping has a protective effect on depression and anxiety [3]. Mcpeak et al.’ s research on adolescent patients with mental illness also found that people with high menstrual quality have a tendency to develop spermatic diseases [4]. Related studies have found that adolescents with childhood trauma experience are more likely to have emotional and behavioral problems such as anxiety, suicidal ideation, drug abuse, self-harm, and aggressive behavior [5].With the continuous development of the times, the growth and development of teenagers in our country are changing into a long-term trend, the age of puberty development is constantly advancing, the rapid physical and mental changes experienced by individuals, childhood stress events, external pressure and the relative lack of effective psychological adjustment methods will further lead to the development of mental disorders such as depression. Reward helps to reinforce correct behavior. Behaviorists believe that reinforcement is divided into positive and negative. Both positive reinforcement and negative reinforcement are rewards for increasing the probability of target behavior, thus consolidating behavior. Students are also rewarded to cultivate self-confidence, self-improvement, and positive enterprising belief. This study attempts to systematically investigate the evolution of the recognition of rewards and punishments in education, reveal the various influences of rewards and punishments on students’ psychology and behavior, and put forward the principles and countermeasures of rational application of rewards and punishments in practice.The research contribution of this study:(1)
At present, there are few research results on depression prevention, and there are few studies on neuroticism, an important personality factor, and there is a lack of individual differences in the intervention effect of depression. Combining these two factors that have important influence on depression, this study supplements the theoretical research in this field and provides some theoretical background and guidance.(2)
The psychological well-being of people has changed dramatically over time, particularly that of modern teenagers, as society and science and technology have developed and advanced continuously. Exploring the causes of adolescent negative emotions and deriving patterns of brain activity from psychological issues are now crucial research areas. The psychological data of teenagers is analyzed and extracted using DM technology, and a structural model is built using the appropriate DM algorithms. Potential clusters are then identified.


## 2. Related Work

### 2.1. A Correlation Study on the Influencing Factors of TD

Depression is a mental illness with depression as its main symptom, which is caused by many reasons. It is a common disease that easily recurs, and can lead to mental disability and suicide. The pathogenesis of TD is still unclear, and many factors are at work, including biological, psychological, and social factors. At present, the treatment of TD tends to be the comprehensive treatment of drugs and psychotherapy.Freitas et al. summarized stress as events that affect the mental or physical health of individuals of a certain age in a specific social environment, and these events play a very important role in the pathogenesis of TD [6]. Merry et al. found that stressful life events, as the most important stressor, are positively related to suicidal ideation [7]. Dalen et al. think that, compared with the normal control group, negative life events such as low socioeconomic status and unsound families are all related to depression [8]. Podeszwa et al. found that positive thinking was related to the improvement of depressive symptoms, and its effect was significantly higher than that of the control group. Therefore, both negative thinking and positive thinking are related to depression. Positive thinking can alleviate depression, while negative thinking leads to depression [9].With the increase and deepening of research, people have discovered the protective factors that can promote disadvantaged children to achieve good development results, and put forward the concept of “resilience.” Jebeile et al. believe that environmental risk factors and protective factors independently influence individual development results, with protective factors playing a positive role, risk factors playing a negative role, and protective factors and risk factors having no effect on each other [10]. Sansom-Daly et al.’ s research shows that nearly 91% of people with depressive disorder have experienced stress events before their illness, and adolescents have experienced stress events before the onset, recurrence, and symptom deterioration of depression [11]. The results of Brown et al. show that adolescent depression often happens together with other psychological and behavioral problems, among which the comorbidity rate with anxiety is the highest and the two are closely related, which further aggravates its harm to adolescents [12]. Cox et al. pointed out that the occurrence and development of depression are closely related to stress events, and there is a dose-response relationship between stress intensity and depression degree. However, not all individuals who encounter stressful events will suffer from depression [13].


### 2.2. Research Status of DM Technology

In order to extract more useful knowledge and potential structural models from a large amount of psychological data, it is necessary to combine the characteristics of psychological data and related algorithms in DM(data mining) technology for research. There are two main aspects, one is from the algorithm with structural characteristics, and the other is from the algorithm that can find potential aggregation.Kahalnik et al. applied the Bayesian network to the analysis of primary liver cirrhosis and tested their hypothesis with a 95% confidence level [14]. Robles et al. assume that all points in a given cluster family obey the same probability distribution, and the objects in the data set are determined according to the highest probability value belonging to the distribution, which is also a highly organized clustering technique [15]. Frownfelter et al. proposed an improved algorithm, which is an extreme measure of the tree structure. Algorithms combined with clustering have been widely studied in recent years, but algorithms combined with hierarchical clustering are rare [16]. Modarres et al. combined fuzzy similarity matrix with hierarchical clustering and proposed an improved algorithm to make the aggregation faster [17]. Subspace clustering is an extension of the traditional clustering algorithm, which tries to find groups of different subspaces in the data set.In natural language processing, emotion analysis is a popular area of study. The main concept behind it is to analyze a text, draw out the emotional information contained therein, and then use that information to categorize texts or sentences and divide emotional polarity. Hobden et al. classified documents using the NB (Naive Bayes) method after building a feature vector space model based on the feature words from the training set text [18]. In order to perform a regression analysis using the data in accordance with the statistical information, Saxena et al. collected data from Twitter on patients who had and did not have depression [19]. They then used the least square method to do so. In order to predict the behavioral characteristics of 45 users’ mental health status, Cole et al. used multi-task regression learning. In order to examine the internal relationships between depression and anxiety, they used multi-task regression and correlation coefficient methods [20]. Pain uses a backpropagation neural network (BPNN) to classify 147 severe TD using features selected by a genetic algorithm (GA) (Genetic Algorithm) with an accuracy rate of 89.12 percent [21].


## 2.1. A Correlation Study on the Influencing Factors of TD

Depression is a mental illness with depression as its main symptom, which is caused by many reasons. It is a common disease that easily recurs, and can lead to mental disability and suicide. The pathogenesis of TD is still unclear, and many factors are at work, including biological, psychological, and social factors. At present, the treatment of TD tends to be the comprehensive treatment of drugs and psychotherapy.Freitas et al. summarized stress as events that affect the mental or physical health of individuals of a certain age in a specific social environment, and these events play a very important role in the pathogenesis of TD [6]. Merry et al. found that stressful life events, as the most important stressor, are positively related to suicidal ideation [7]. Dalen et al. think that, compared with the normal control group, negative life events such as low socioeconomic status and unsound families are all related to depression [8]. Podeszwa et al. found that positive thinking was related to the improvement of depressive symptoms, and its effect was significantly higher than that of the control group. Therefore, both negative thinking and positive thinking are related to depression. Positive thinking can alleviate depression, while negative thinking leads to depression [9].With the increase and deepening of research, people have discovered the protective factors that can promote disadvantaged children to achieve good development results, and put forward the concept of “resilience.” Jebeile et al. believe that environmental risk factors and protective factors independently influence individual development results, with protective factors playing a positive role, risk factors playing a negative role, and protective factors and risk factors having no effect on each other [10]. Sansom-Daly et al.’ s research shows that nearly 91% of people with depressive disorder have experienced stress events before their illness, and adolescents have experienced stress events before the onset, recurrence, and symptom deterioration of depression [11]. The results of Brown et al. show that adolescent depression often happens together with other psychological and behavioral problems, among which the comorbidity rate with anxiety is the highest and the two are closely related, which further aggravates its harm to adolescents [12]. Cox et al. pointed out that the occurrence and development of depression are closely related to stress events, and there is a dose-response relationship between stress intensity and depression degree. However, not all individuals who encounter stressful events will suffer from depression [13].


## 2.2. Research Status of DM Technology

In order to extract more useful knowledge and potential structural models from a large amount of psychological data, it is necessary to combine the characteristics of psychological data and related algorithms in DM(data mining) technology for research. There are two main aspects, one is from the algorithm with structural characteristics, and the other is from the algorithm that can find potential aggregation.Kahalnik et al. applied the Bayesian network to the analysis of primary liver cirrhosis and tested their hypothesis with a 95% confidence level [14]. Robles et al. assume that all points in a given cluster family obey the same probability distribution, and the objects in the data set are determined according to the highest probability value belonging to the distribution, which is also a highly organized clustering technique [15]. Frownfelter et al. proposed an improved algorithm, which is an extreme measure of the tree structure. Algorithms combined with clustering have been widely studied in recent years, but algorithms combined with hierarchical clustering are rare [16]. Modarres et al. combined fuzzy similarity matrix with hierarchical clustering and proposed an improved algorithm to make the aggregation faster [17]. Subspace clustering is an extension of the traditional clustering algorithm, which tries to find groups of different subspaces in the data set.In natural language processing, emotion analysis is a popular area of study. The main concept behind it is to analyze a text, draw out the emotional information contained therein, and then use that information to categorize texts or sentences and divide emotional polarity. Hobden et al. classified documents using the NB (Naive Bayes) method after building a feature vector space model based on the feature words from the training set text [18]. In order to perform a regression analysis using the data in accordance with the statistical information, Saxena et al. collected data from Twitter on patients who had and did not have depression [19]. They then used the least square method to do so. In order to predict the behavioral characteristics of 45 users’ mental health status, Cole et al. used multi-task regression learning. In order to examine the internal relationships between depression and anxiety, they used multi-task regression and correlation coefficient methods [20]. Pain uses a backpropagation neural network (BPNN) to classify 147 severe TD using features selected by a genetic algorithm (GA) (Genetic Algorithm) with an accuracy rate of 89.12 percent [21].


## 3. Methodology

### 3.1. An Analysis of Educational Reward and Disciplinary Intervention of Adolescent Depression

Rewards can have both favorable and unfavorable effects. The benefit of rewards is that they can satisfy needs, give people a sense of success, encourage students to reach their full potential, and pursue new objectives, all of which contribute to the development of positive relationships between teachers and students. As a result, there are some conditions we must meet when using rewards in education. In addition to material rewards, we must prioritize spiritual rewards. Awards, not materials or other extras, should be used to recognize students’ abilities directly. This type of discipline can help students recognize their errors, rather than harboring teacher resentment, and correct them quickly, which is very advantageous to teachers’ practical service in management and educational practice. Only when rewards and penalties are applied promptly can they have the desired impact. Students who engage in inappropriate behavior are subject to discipline, not those who engage in appropriate behavior. It cannot directly increase the frequency of appropriate student behavior; it can only decrease the frequency of inappropriate student behavior. Negative feelings can also result from punishment. For instance, fear of punishment will ruin students’ learning moods and lower their learning effectiveness. Discipline should only be used when absolutely necessary. Even when discipline is used, it serves to first temporarily stop undesirable behavior and then reward good behavior.A large number of experiments in behavioral psychology show that whether in animals or people, the rewarded reaction or behavior will increase the possibility of its occurrence in the future. This situation can be observed in school situations. Students who are often praised are more likely to lose motivation than other students when they are not praised. Therefore, punishment can only play a role in overcoming bad behaviors when it signals appropriate or correct behaviors and punishes incorrect choices or behaviors.It is one of the predictors of depression. Neuroticism is the source of negative emotions, which is characterized by emotional instability. Individuals are emotionally unstable due to excessive attention and sensitivity to negative and negative events. This personality trait often leads to psychopathological problems such as depression. In a large number of studies on depression, researchers have noticed that for some people, even in the face of great difficulties and great pressure, they are not necessarily depressed; In this model, risk factors are usually stronger than protective factors.Natural language processing is the mathematical modeling of human language, which converts words into mathematical forms that can be recognized and processed by computers. Then−gram model assumes that the probability of the current word is related to the first n−1 words, but not to the following words [21]. Therefore, the simplified language model of this model can be expressed as:(1)Pw1,w2,…,wn=∏i=1npwi−n+1,…,wi−1.When different numbers ofnn are taken, different language models can be obtained, such as the univariate model for n=1, binary model for n=2, and ternary model for n=3.There are many methods of distributed word vector representation, such as word vector representation in matrix form, word vector representation in cluster form, and word vector representation in neural network training [18]. At present, it has become a trend to use neural network training to obtain word vectors, which can make full use of the context information of words. The vectors of n−1 words are spliced, and the spliced vectors are used for hidden layer input. The hidden layer calculation formula is as follows:(2)h=tanhb1+Hx,b is the bias term, and H is the weight matrix of the hidden layer. In this study, the author uses the hyperbolic function tanh.The correlation between feature sets will increase with the increase of the correlation coefficient between features and categories, and decrease with the increase of correlation coefficient between features. The formula is:(3)Merits=krcf¯k+kk−1rff¯.Merits is a heuristic index of feature subset S with k feature items, namely correlation coefficient, rcf¯ is the average of correlation coefficients between features and classes, and rff¯ is the average of correlation coefficients between features.SEM (Structural equation model) is the most important tool to measure the relationship between potential variables by analyzing and using several related observable indicators. In traditional SEM, these two components are average regression models, so they are easily affected by outliers or non-normality in response distribution. SEM’s hypothetical model of depression established three variables: environment, stress, and self-quality.According to our hypothesis, depression is an endogenous variable, while environment, stress, and personal quality are exogenous variables. Analyze the influence of stress on depression, which is affected by environment and self-quality, as shown in Figure1 below.Figure 1
Hypothetical model of depression.If you encounter psychological and social factors from family, school, and society, if you do not get help from others in time, you cannot solve them yourself and it is easy to cause depression and anxiety due to psychological imbalance. The occurrence of TD is not only related to gender and age, but also to bad cognitive behavior. Comprehensive treatment can improve patients’ medication compliance, and depression-related symptoms are relieved more obviously at the end of treatment. Cognitive therapy combined with drug therapy can quickly relieve depression-related symptoms in the early stage of treatment intervention.


### 3.2. DM Clustering of Adolescent Depression

The meaning of punishment and criticism is that punishment is not simply suppressing the punished behavior. The reaction in the case of reward and light punishment is often more difficult to calm than that in the case of reward alone. Cognitive psychologists use the results of animal experiments to explain the design of human behavior to behaviorists. They strongly criticize that human behavior is not a simple connection between stimulus and response, and external stimuli must be perceived and explained by individuals to have an impact on individual responses. It plays a dual role in strengthening and consolidating behavior and stimulating sports.In education, we can use punishment, but we should use it carefully. When being punished, teachers should express their unwillingness but helplessness. After being punished, they should help students in time to avoid causing physical and mental harm to students. The purpose of teacher’s punishment is to eliminate some improper behaviors of students, but students may associate such behaviors with situations and people. Simply rewarding the students’ union leads to the students only accepting rewards and lacking the courage to face setbacks. On the contrary, if only punishment is given without reward, students’ self-confidence will be damaged. Therefore, reward and punishment must be properly combined.Clustering technology is generally used in unsupervised learning. DM clustering technology automatically divides similar examples through a series of calculations, thus dividing different examples into different categories. Clustering technology in DM is also very useful for teenagers with negative psychological problems. We can use clustering technology to describe some potential characteristics of groups with psychological problems, and then make corresponding countermeasures according to the characteristics of money to improve the psychological level of college students in China.Since no single taxonomy is suitable for all applications, it is often useful to try multiple methods. The bayesian network has an important property, that is, each node is independent of all its indirect predecessor nodes after the value of its direct predecessor node is set. The joint conditional probability distribution of any combination of random variables can be simplified as:(4)Px1,x2,…,xn=∏i=1nPxi|ParentsYi,where Px1,x2,…,xn is the probability of a specific combination of values of X, and the value of Pxi|ParentsYi corresponds to the set of conditional probability tables of Yi.KNN is based on analogy learning, and its purpose is to assign unknown tuples to most of itsk nearest neighbors in the training set. Euclidean distance is used in this paper. The Euclidean distance of two points or tuples X1x11,x12,…,x1n,X2x21,x22,…,x2n is:(5)distX1X2=∑i=1nx1i−x2i.In order to reduce the influence of noise in the text, this study uses the dictionary fused with the previous articles to construct the vector space of users with these emotional words. In this study, the number of words in the dictionary appearing in users’ discourse is counted, and word frequency is used as the weight of words. The word frequency of a wordi of the user a can be expressed as:(6)TFi=ni∑j=1Nnj.In this study, the Min-Max normalization strategy is adopted to eliminate the influence of the inconsistency between different variable dimensions and speed up the classification speed of machine learning algorithms. This method maps all features to an interval, and the formula is given as follows:(7)x∗=x−xminxmax−xmin.In the formula,x is the input feature, and xmin,xmax is the minimum value and maximum value in the feature data. The output is a normalized feature. This formula will map all features to the interval between 0 and 1.This study presents a depression classification algorithm based on bimodal data fusion. The algorithm proposed in this paper mainly classifies the fusion features of the bimodal data of the subjects to identify whether the subjects are healthy or TD. After preprocessing the data, the algorithm is divided into three steps: feature extraction, feature fusion, and feature classification. The flow chart of the algorithm is shown in Figure2.Figure 2
Flow chart of depression classification algorithm.First, a multi-scale functional brain network is constructed to extract fMRI (Functional Magnetic Resonance Imaging) and sMRI (Structural Magnetic Resonance Imaging) features. Merge two features, and finally classify the merged features using KNN. Through merging strategies such as concatenation or addition, not only feature merging is realized, but also redundant features are eliminated to some extent, and feature dimension reduction is realized.Inverse EEG problem is a process of estimating the position of endogenous sources in the brain according to the known potential distribution observed in the scalp. At present, there are two main methods to solve the inverse problem of EEG: the parameter location method based on equivalent dipole; the image reconstruction method based on the current distribution model. There have been some algorithms to solve the inverse EEG problem, such as low-resolution EEG and normalized low-resolution EEG. The calculation process is given as:(8)JMN=KtKKt+V,JMN is the current density value obtained under the minimum norm algorithm, where V is the signal vector measured at N electrode positions; “+”means Moore-Penrose pseudo-inverse, and T means transpose matrix.A method of measuring the root mean square of the ratio of slope change to the ideal curve is often used to estimate the bandwidth of a signal. If the signal is very similar to the ideal signal, the value will converge to 1:(9)Complexity=Mobilitydyt/dtMobilityyt,where yt represents the amplitude of the signal at time t.Finally, a new feature vector is merged by series fusion, and the fused feature is marked asFCC, which can be expressed as:(10)FCC=A⟶bestB⟶best=a⟶00b⟶TU⟶V⟶,where A⟶best,B⟶best is the set of two linear relationships, and U⟶,V⟶ is two eigenvectors. The fused feature FCC contains the features of two modal data, therefore, compared with the single modal feature, the fused feature can achieve a more accurate classification effect.The traditional user model is basically built by feature customization. The disadvantage of this method is that the text features must be defined manually, and the features must be simplified and dimensionalized. In order to solve this problem, this paper uses the word vector trained by deep learning as the minimum granularity feature, that is, vocabulary feature. The workflow of this chapter is shown in Figure3.Figure 3
User vector classification process.Text preprocessing includes symbol, expression and punctuation filtering, and only the text content is kept. Secondly, this paper segments the text, using hyphenation in the python environment to segment the text. Use word2vec to convert these words into vector representations of words. The training set and test set of users are still divided by ten times, and the classifier uses logistic regression, SVM, and perceptron neural network. Finally, the experimental results of different methods are compared.


## 3.1. An Analysis of Educational Reward and Disciplinary Intervention of Adolescent Depression

Rewards can have both favorable and unfavorable effects. The benefit of rewards is that they can satisfy needs, give people a sense of success, encourage students to reach their full potential, and pursue new objectives, all of which contribute to the development of positive relationships between teachers and students. As a result, there are some conditions we must meet when using rewards in education. In addition to material rewards, we must prioritize spiritual rewards. Awards, not materials or other extras, should be used to recognize students’ abilities directly. This type of discipline can help students recognize their errors, rather than harboring teacher resentment, and correct them quickly, which is very advantageous to teachers’ practical service in management and educational practice. Only when rewards and penalties are applied promptly can they have the desired impact. Students who engage in inappropriate behavior are subject to discipline, not those who engage in appropriate behavior. It cannot directly increase the frequency of appropriate student behavior; it can only decrease the frequency of inappropriate student behavior. Negative feelings can also result from punishment. For instance, fear of punishment will ruin students’ learning moods and lower their learning effectiveness. Discipline should only be used when absolutely necessary. Even when discipline is used, it serves to first temporarily stop undesirable behavior and then reward good behavior.A large number of experiments in behavioral psychology show that whether in animals or people, the rewarded reaction or behavior will increase the possibility of its occurrence in the future. This situation can be observed in school situations. Students who are often praised are more likely to lose motivation than other students when they are not praised. Therefore, punishment can only play a role in overcoming bad behaviors when it signals appropriate or correct behaviors and punishes incorrect choices or behaviors.It is one of the predictors of depression. Neuroticism is the source of negative emotions, which is characterized by emotional instability. Individuals are emotionally unstable due to excessive attention and sensitivity to negative and negative events. This personality trait often leads to psychopathological problems such as depression. In a large number of studies on depression, researchers have noticed that for some people, even in the face of great difficulties and great pressure, they are not necessarily depressed; In this model, risk factors are usually stronger than protective factors.Natural language processing is the mathematical modeling of human language, which converts words into mathematical forms that can be recognized and processed by computers. Then−gram model assumes that the probability of the current word is related to the first n−1 words, but not to the following words [21]. Therefore, the simplified language model of this model can be expressed as:(1)Pw1,w2,…,wn=∏i=1npwi−n+1,…,wi−1.When different numbers ofnn are taken, different language models can be obtained, such as the univariate model for n=1, binary model for n=2, and ternary model for n=3.There are many methods of distributed word vector representation, such as word vector representation in matrix form, word vector representation in cluster form, and word vector representation in neural network training [18]. At present, it has become a trend to use neural network training to obtain word vectors, which can make full use of the context information of words. The vectors of n−1 words are spliced, and the spliced vectors are used for hidden layer input. The hidden layer calculation formula is as follows:(2)h=tanhb1+Hx,b is the bias term, and H is the weight matrix of the hidden layer. In this study, the author uses the hyperbolic function tanh.The correlation between feature sets will increase with the increase of the correlation coefficient between features and categories, and decrease with the increase of correlation coefficient between features. The formula is:(3)Merits=krcf¯k+kk−1rff¯.Merits is a heuristic index of feature subset S with k feature items, namely correlation coefficient, rcf¯ is the average of correlation coefficients between features and classes, and rff¯ is the average of correlation coefficients between features.SEM (Structural equation model) is the most important tool to measure the relationship between potential variables by analyzing and using several related observable indicators. In traditional SEM, these two components are average regression models, so they are easily affected by outliers or non-normality in response distribution. SEM’s hypothetical model of depression established three variables: environment, stress, and self-quality.According to our hypothesis, depression is an endogenous variable, while environment, stress, and personal quality are exogenous variables. Analyze the influence of stress on depression, which is affected by environment and self-quality, as shown in Figure1 below.Figure 1
Hypothetical model of depression.If you encounter psychological and social factors from family, school, and society, if you do not get help from others in time, you cannot solve them yourself and it is easy to cause depression and anxiety due to psychological imbalance. The occurrence of TD is not only related to gender and age, but also to bad cognitive behavior. Comprehensive treatment can improve patients’ medication compliance, and depression-related symptoms are relieved more obviously at the end of treatment. Cognitive therapy combined with drug therapy can quickly relieve depression-related symptoms in the early stage of treatment intervention.


## 3.2. DM Clustering of Adolescent Depression

The meaning of punishment and criticism is that punishment is not simply suppressing the punished behavior. The reaction in the case of reward and light punishment is often more difficult to calm than that in the case of reward alone. Cognitive psychologists use the results of animal experiments to explain the design of human behavior to behaviorists. They strongly criticize that human behavior is not a simple connection between stimulus and response, and external stimuli must be perceived and explained by individuals to have an impact on individual responses. It plays a dual role in strengthening and consolidating behavior and stimulating sports.In education, we can use punishment, but we should use it carefully. When being punished, teachers should express their unwillingness but helplessness. After being punished, they should help students in time to avoid causing physical and mental harm to students. The purpose of teacher’s punishment is to eliminate some improper behaviors of students, but students may associate such behaviors with situations and people. Simply rewarding the students’ union leads to the students only accepting rewards and lacking the courage to face setbacks. On the contrary, if only punishment is given without reward, students’ self-confidence will be damaged. Therefore, reward and punishment must be properly combined.Clustering technology is generally used in unsupervised learning. DM clustering technology automatically divides similar examples through a series of calculations, thus dividing different examples into different categories. Clustering technology in DM is also very useful for teenagers with negative psychological problems. We can use clustering technology to describe some potential characteristics of groups with psychological problems, and then make corresponding countermeasures according to the characteristics of money to improve the psychological level of college students in China.Since no single taxonomy is suitable for all applications, it is often useful to try multiple methods. The bayesian network has an important property, that is, each node is independent of all its indirect predecessor nodes after the value of its direct predecessor node is set. The joint conditional probability distribution of any combination of random variables can be simplified as:(4)Px1,x2,…,xn=∏i=1nPxi|ParentsYi,where Px1,x2,…,xn is the probability of a specific combination of values of X, and the value of Pxi|ParentsYi corresponds to the set of conditional probability tables of Yi.KNN is based on analogy learning, and its purpose is to assign unknown tuples to most of itsk nearest neighbors in the training set. Euclidean distance is used in this paper. The Euclidean distance of two points or tuples X1x11,x12,…,x1n,X2x21,x22,…,x2n is:(5)distX1X2=∑i=1nx1i−x2i.In order to reduce the influence of noise in the text, this study uses the dictionary fused with the previous articles to construct the vector space of users with these emotional words. In this study, the number of words in the dictionary appearing in users’ discourse is counted, and word frequency is used as the weight of words. The word frequency of a wordi of the user a can be expressed as:(6)TFi=ni∑j=1Nnj.In this study, the Min-Max normalization strategy is adopted to eliminate the influence of the inconsistency between different variable dimensions and speed up the classification speed of machine learning algorithms. This method maps all features to an interval, and the formula is given as follows:(7)x∗=x−xminxmax−xmin.In the formula,x is the input feature, and xmin,xmax is the minimum value and maximum value in the feature data. The output is a normalized feature. This formula will map all features to the interval between 0 and 1.This study presents a depression classification algorithm based on bimodal data fusion. The algorithm proposed in this paper mainly classifies the fusion features of the bimodal data of the subjects to identify whether the subjects are healthy or TD. After preprocessing the data, the algorithm is divided into three steps: feature extraction, feature fusion, and feature classification. The flow chart of the algorithm is shown in Figure2.Figure 2
Flow chart of depression classification algorithm.First, a multi-scale functional brain network is constructed to extract fMRI (Functional Magnetic Resonance Imaging) and sMRI (Structural Magnetic Resonance Imaging) features. Merge two features, and finally classify the merged features using KNN. Through merging strategies such as concatenation or addition, not only feature merging is realized, but also redundant features are eliminated to some extent, and feature dimension reduction is realized.Inverse EEG problem is a process of estimating the position of endogenous sources in the brain according to the known potential distribution observed in the scalp. At present, there are two main methods to solve the inverse problem of EEG: the parameter location method based on equivalent dipole; the image reconstruction method based on the current distribution model. There have been some algorithms to solve the inverse EEG problem, such as low-resolution EEG and normalized low-resolution EEG. The calculation process is given as:(8)JMN=KtKKt+V,JMN is the current density value obtained under the minimum norm algorithm, where V is the signal vector measured at N electrode positions; “+”means Moore-Penrose pseudo-inverse, and T means transpose matrix.A method of measuring the root mean square of the ratio of slope change to the ideal curve is often used to estimate the bandwidth of a signal. If the signal is very similar to the ideal signal, the value will converge to 1:(9)Complexity=Mobilitydyt/dtMobilityyt,where yt represents the amplitude of the signal at time t.Finally, a new feature vector is merged by series fusion, and the fused feature is marked asFCC, which can be expressed as:(10)FCC=A⟶bestB⟶best=a⟶00b⟶TU⟶V⟶,where A⟶best,B⟶best is the set of two linear relationships, and U⟶,V⟶ is two eigenvectors. The fused feature FCC contains the features of two modal data, therefore, compared with the single modal feature, the fused feature can achieve a more accurate classification effect.The traditional user model is basically built by feature customization. The disadvantage of this method is that the text features must be defined manually, and the features must be simplified and dimensionalized. In order to solve this problem, this paper uses the word vector trained by deep learning as the minimum granularity feature, that is, vocabulary feature. The workflow of this chapter is shown in Figure3.Figure 3
User vector classification process.Text preprocessing includes symbol, expression and punctuation filtering, and only the text content is kept. Secondly, this paper segments the text, using hyphenation in the python environment to segment the text. Use word2vec to convert these words into vector representations of words. The training set and test set of users are still divided by ten times, and the classifier uses logistic regression, SVM, and perceptron neural network. Finally, the experimental results of different methods are compared.


## 4. Experiment and Results

The relationship between teenagers and their families is very close. As one of the direct and important growth environments for teenagers, families play a more important role in cultivating, supporting and influencing teenagers’ physical and mental growth. This study found that the scores of family financial difficulties and internal conflicts of depressed adolescents in the adolescent life events scale were higher than those of the normal control group, and the difference was highly significant. TD and father-son relationships are a more normal poor control group. Table1 shows that positive coping is negatively correlated with stress, while negative coping is positively correlated with total stress.Table 1
Correlation between coping style, stress, and social support in depression group.
Total stress scoreFamily supportFriends supportOther supportTotal supportRespond positivelyr−0.3210.1170.2440.2010.274P0.0010.2630.0160.0460.012Negative responser0.368−0.172−0.078−0.236−0.208P0.0000.0690.4510.0320.058The scores of the neuroticism dimension of the adolescent personality questionnaire for depression were significantly higher than those of the normal control group, while the scores of introversion and extroversion dimensions were significantly lower, and the scores of adolescents aged 16 and above were also higher than those of the normal control group. Extroversion is also a very important personality dimension. Introverts focus on their inner activities, tend to be lost in thought and focus on the meaning and significance of their depressed emotions, thus developing severe and persistent depressive symptoms.Depression can inhibit the related functions of the prefrontal cortex and basal ganglia, which are related to the reward circuit of the brain. The persistent inactivity of the reward circuit of the brain may be the main cause of depression. Between the selection of characteristic variables, the correlation analysis of all data is carried out. A total of 200 pieces of data were selected. Table2 shows the analysis results of depression.Table 2
Correlation analysis of depression emotion.
Depressive moodPerson correlationRemarkable (bilateral)Age−0.2340.001Family relations−0.1220.086Teacher-student relationship−0.4120.432Competitive relation−0.1330.087Interpersonal relationship−0.7520.000Learning pressure−0.6680.000Economic pressures−0.7820.000Employment pressure−0.8060.000Disposition−0.7580.000Coping style−0.8210.000Social support−0.8110.000When the value of Person is 0, variables are positively correlated with depression and anxiety. The higher the value of the Person, the greater the correlation. Significant ((bilateral) > 0.05, and there is no significant difference between variables. As shown in Table 2, all variables are positively or negatively related to depression to some extent. Learning stress, economic stress, work stress, coping style, and social support are closely related to depression and anxiety, so they are selected as predictive attributes when constructing the depression and anxiety model.It is often observed in educational practice that when some altruistic behaviors of students are praised or over-praised, the tendency of altruistic behaviors does not increase. Therefore, in education, even those good behaviors that we expect students to show often need to be properly praised. It is not to make him feel that praise puts pressure on him, but to make him understand that his behavior is correct so that this kind of behavior can happen.In terms of age, the detection rates of emotional abuse, physical abuse, and emotional neglect among different age groups are statistically significant (P<0.01). Among them, the detection rate of emotional abuse and emotional neglect in the 14-year-old group is higher than that in other age groups, and the detection rate of physical abuse in the 10-year-old group is the highest. In all age groups, the detection rate of physical neglect and emotional neglect is higher than in other types of childhood trauma, as shown in Figure 4.Figure 4
Comparison of detection rates of various types of childhood trauma among adolescents of different ages.The results show that with the increase in age and grade, the detection rate of TD symptoms increases. The reason may be that teenagers are in a critical period of physical and mental development, and they are prone to confusion, doubt, or conflict with themselves and the world. At the same time, with the increase in age and grade, teenagers face more and more learning pressure, which can easily lead to depression [11]. Families with poor economic status have more TD symptoms, which is consistent with previous research results [3]. The reason may be that people with poor family economic conditions are prone to inner inferiority complexes and difficulties, leading to depression.The classification effects of word vectors obtained using various word vector training methods and different word vector dimensions are examined in this section based on experiments carried out using the user vectors that were obtained. In this section, classification experiments are conducted using SVM (support vector machine), KNN, and BPNN, and theF1 value is obtained as the classification index using the ten-fold cross method. The word vector’s dimension ranges from 10 to 300, and the word vector training method used is cbow. The weighted word vector method's experimental results are displayed in Figure 5.Figure 5
Experimental results of weighted word vector.It is clear that, among the two user vector construction techniques, the multilayer perceptron produces the best results. When the word vector has 90 dimensions, the weighted word vector method’s averageF1 value is 81.3 percent. KNN has a general effect in comparison to the experimental results of the classification algorithm, whereas SVM divides precisely using the hyperplane because the obtained end-user vector is a vector representation in high-dimensional space. Multilayer perceptrons use numerous multilayer nonlinear activation functions in order to fit nonlinear data, and neural networks have a certain level of fault tolerance. In this section, the multilayer perceptron has the best classification performance.All dimensions of the data come from different questionnaires, and the scores of each psychological condition are calculated by different calculation methods. These calculation processes are automatically completed by the background psychological evaluation system. The data involved in the analysis come from two tables, one of which records the personal information of the subjects, and the other of which records the psychological status of the students, including anxiety, depression, and school fatigue rate. Set the value ofk to 7. The coordinates of the center point of the k-means execution result are shown in Figure 6.Figure 6
K mean center point.In the cluster group, the incidence of self-reported anxiety and depression is significantly higher than the average, and the incidence of other negative emotions is higher than the average, especially the incidence of school aversion and stress. Among the people belonging to the cluster, the scores of positive energy, social support, and problem-solving ability are higher than the normal values, while the negative emotions are anxiety, anger, hostility, fatigue, stress, poor health, and so on. All negative emotions are lower than the average level, so this group of people can be classified as people with normal psychology.In Figures7 and 8, AUC (area under the receiver operating characteristic curve) and ROC (receiver operating characteristic curve) under KNN are basically similar, both of which are greater than 0.9. However, the number of features required by the BPNN classifier to obtain the highest accuracy is greater than that required by the KNN classifier, and the BPNN classifier takes more time than the KNN classifier. Therefore, KNN + BPNN is the best combination method in this paper.Figure 7
ROC curves of mild depression groups with different classifiers (data set 1).Figure 8
ROC curves of mild depression groups with different classifiers (data set 2).Moderate stress and adversity can help people learn to make use of internal and external resources, exercise their social skills, and improve their coping ability [10]. With the increase of stress and adversity, their negative influence on personal development results also increases. Students should be educated to treat rewards and punishments correctly, and not be complacent when they get rewards, and not discouraged when they get rewards. Use self-esteem and self-confidence to overcome your own shortcomings, so in school, you cannot praise students unconditionally or be too generous. Especially when students' behaviors that meet the educational requirements are motivated by intrinsic motivation, they do not need to be praised so as not to weaken their intrinsic motivation.


## 5. Conclusions

Patients with TD must take into account their psychological and social needs. Family environment conflicts, nervousness and psychosis in personality, and poor coping mechanisms are the factors that have an impact on TD. The principle of operant conditioning must be correctly understood in order to choose and apply different reinforcement and punishment strategies sensibly and comprehensively in accordance with the unique circumstances of each student. In this study, the negative emotions experienced by teenagers are examined using DM analysis technology. Of the two methods for creating user vectors, multilayer perceptron has produced the best results. The averageF1 value for the weighted word vector method when the word vector has 90 dimensions is 81.3 percent. The opposite is also true: the wrong reward can be turned into punishment and the right punishment back into reward. As a result, encouragement and reprimand are the education’s wings. Students can only soar higher and farther by always maintaining their balance.



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*Source: 3919519-2022-09-06.xml*