# Practice and Exploration of Teaching Mental Health Education for College Students Based on Data Mining Algorithm

Authors: Lu Bai; Chunyan Tang
Journal: Security and Communication Networks
 (2022)

---

## Abstract

Based on the data mining algorithm, this paper combines the background of the requirements for cultivating talents in the new era, the psychological characteristics, and learning features of contemporary college students and the realistic needs of the reform of the teaching of mental health education courses in colleges and universities. By analysing the characteristics, psychology, and causes of crime of contemporary university students, the study will explore the regular experience of guiding and preventing university students from committing crimes at the level of mental health education in universities, which will not only help to reduce the crime rate of university students but also extend to the prevention of crimes of other people in society through the work of mental health education in general, so as to reduce the crime rate in society and create a harmonious society. In this paper,the decision tree C4.5 algorithm and the association rule apriori algorithm are used to analyze the mental health data of college students, which can improve the teaching quality of college mental health education courses and enhance the psychological quality of college students. It is of great significance to improve the teaching quality of mental health education courses in colleges and universities and to enhance and improve the psychological quality and health literacy of college students.


---

## Body

## 1. Introduction

University students are the valuable human resources of the country, the hope of the nation, and the future of the motherland. How they think and behave has a great deal to do with the future and destiny of the country. It is of great and far-reaching strategic significance to ensure that the cause of socialism with Chinese characteristics flourishes and is succeeded by people [1].Compared with other people of the same age, college students should have a higher moral quality. Although the overall quality of contemporary college students has generally improved, there are still individual college students with low moral quality, especially the growth of the crime rate of college students is even more worrying [2]. According to the relevant survey, “in 1965, the crime of college students accounted for 1% of the criminal crimes of the whole society,” during the “Cultural Revolution,” the crime of college students accounted for 2.5% of the whole criminal crimes, while in recent years, the crime of college students accounted for 17% of the whole social criminal crimes [3]. “Since the beginning of the new century, reports on crimes committed by university students have come into the limelight even more frequently and have become a matter of concern for society at large.” For example, the major vicious crime cases such as the case of Yao Jiaxin in 2010, the stabbing case of a mother at the airport of a foreign student in 2011, the assassination case of the vice president of Jiangsu University of Science and Technology in 2012, and the murder case of Li Starr at the Communication University of China in 2015 have all caused people to pay attention to and think about the crimes committed by university students [4]. As a group with a special status in society, university students have a bright future and a bright future. The state nurtures them and the schools teach them knowledge and skills in the hope that they can realize their value in life, support the future of the motherland, and contribute to society. This is a real warning to today’s university education [5].The role of colleges and universities is to teach and educate people, but some colleges and universities put more effort on academic education for college students, but to a certain extent neglect the legal education of college students [6]. A highly educated criminal may be more sophisticated, technical, and intelligent than an ordinary criminal, which undoubtedly creates more danger for the victim and makes the detection of the case more difficult. In the face of the rising crime rate among university students, universities need to rethink their current education [6–8].There are still some weak links in the implementation process of mental health education in colleges and universities, and the degree of attention paid to it still needs to be improved [9]. Exploring how to effectively prevent crimes among college students and effectively improve the effectiveness of mental health education in colleges and universities will help colleges and universities improve the level of mental health education and provide guarantee for improving students’ moral quality, shaping a good campus climate, cultivating a good learning atmosphere, and building a harmonious campus [10].Therefore, as an important research content of mental health education, it is of great theoretical and practical significance to strengthen the research on countermeasures for the crime problem of college students. Therefore, as an important research content of mental health education, it is of great theoretical and practical significance to strengthen the study of countermeasures for college students’ crime problems.


## 2. Review of the Literature

Since the end of the last century, there have been many scholars who have begun to study the crime problem of college students, most of them are in the field of criminology and sociology, etc., to explore and study the characteristics, types, causes, and preventive measures of college students’ crimes. [11] According to the different motives of delinquency and crime, the delinquency and crime of college students are divided into four types: property type, sexual sin type, revenge type, and political type. [12] According to the standard of criminology, the crimes of college students are divided into three categories: property crimes, violent crimes, and sexual crimes.Most scholars believe that the characteristics of current crimes committed by university students are increasing in number, diverse types, highly intelligent and brutal crime methods, and the expansion of the subjects involved in crimes [13] argues that “college students’ crimes present characteristics such as the young age of the offender, impulsiveness, extreme ego and individualism, concentration of crimes in property and violence type, clear purpose of committing crimes, and simple motives for committing crimes.” [14] summarises the characteristics of crime among university students as: diversity, passion, high intelligence, and cruelty. [15] argues that, “College students are socially inclined to commit crimes. Higher education institutions are part of the society, constantly subject to the impact and influence of the general social environment, and under the temptation of money, many college students collude with the unruly elements in the society and engage in illegal and criminal interaction. For example, some university students and some criminals in society have joined forces to engage in fraud. This characteristic of socialization of crime will become more and more obvious and will become the focus of crime prevention among college students.”Mental health education can play a very obvious role in the prevention of crime among university students. The role of mental health education is identified in [16] as, “The object of mental health education is people, and the goal is to promote the overall development of people. The functions of mental health education are mainly orientation function, guarantee function, nurturing function and development function. Preventing college students from committing crimes is a direct requirement of the guiding function of mental health education’s behavioral norms” [17]. “The formation and development of people’s mental health and moral and legal qualities are based on psychological activities, and in order to make mental health education achieve effective internalization and externalization from thoughts to behaviors, mental health education needs to pay special attention to the role played by the process of human psychological development in bringing into play the timeliness of mental health education.” [18] Strengthening mental health education for college students can help them establish ideals and beliefs, guide them to conform to the requirements of our socialism morally and legally, effectively prevent crime, and become useful talents of socialism.


## 3. The Feasibility of Mental Health Education to Address Crime Prevention among University Students

Mental health education is a compulsory course to systematically educate college students on ideology and morality and general knowledge of social sciences, and it has a guiding function to enhance the quality and cultivation of college students [19]. Through the role of mental health education, the prevention of crime as the purpose of mental health education for college students can achieve good results.


### 3.1. Playing a Guiding Function to Establish the Correct Values of University Students

College students are highly malleable and their values are still in the process of formation. In a society with diverse ideas and cultures, information of good and bad quality can easily make their values deviate. Mental health education can lead university students’ deviant values to the mainstream by guiding, restraining and inspiring them. It enables university students to set up requirements that are in line with those of the party and the state. It also sets an example through behavioural guidance, so that everyone can follow suit in terms of spirit, psychology, personality, and behaviour to achieve self-restraint and recognition of social norms among university students.


### 3.2. Performing a Normative Function to Regulate the Behaviour of University Students

Mental health education in higher education can enhance the legal awareness and moral quality of university students. It enables university students to deal with social conflicts, emotional entanglements, and psychological misunderstandings in a coordinated manner according to socially accepted rules, without being emotional or blindly impulsive. Awareness of rules is the minimum quality and ability of social people. The cultivation of awareness of rules and responsibility in mental health education in colleges and universities becomes an important guarantee for the standard of behaviour of college students.


### 3.3. To Play a Coordinating Function to Adjust Interpersonal Relationships among University Students

Social interaction is a basic way of life for university students, so play the coordinating role of mental health education, smooth the channels of interpersonal interaction among university students, improve their social interaction ability and quality, resolve conflicts, coordinate interpersonal relationships, and avoid many unnecessary disputes.


## 3.1. Playing a Guiding Function to Establish the Correct Values of University Students

College students are highly malleable and their values are still in the process of formation. In a society with diverse ideas and cultures, information of good and bad quality can easily make their values deviate. Mental health education can lead university students’ deviant values to the mainstream by guiding, restraining and inspiring them. It enables university students to set up requirements that are in line with those of the party and the state. It also sets an example through behavioural guidance, so that everyone can follow suit in terms of spirit, psychology, personality, and behaviour to achieve self-restraint and recognition of social norms among university students.


## 3.2. Performing a Normative Function to Regulate the Behaviour of University Students

Mental health education in higher education can enhance the legal awareness and moral quality of university students. It enables university students to deal with social conflicts, emotional entanglements, and psychological misunderstandings in a coordinated manner according to socially accepted rules, without being emotional or blindly impulsive. Awareness of rules is the minimum quality and ability of social people. The cultivation of awareness of rules and responsibility in mental health education in colleges and universities becomes an important guarantee for the standard of behaviour of college students.


## 3.3. To Play a Coordinating Function to Adjust Interpersonal Relationships among University Students

Social interaction is a basic way of life for university students, so play the coordinating role of mental health education, smooth the channels of interpersonal interaction among university students, improve their social interaction ability and quality, resolve conflicts, coordinate interpersonal relationships, and avoid many unnecessary disputes.


## 4. Practical Pathways for Teaching Mental Health Education

### 4.1. Creating Deep Learning Classrooms with Pedagogical Intelligence

By selecting real-life cases that contain psychological principles, have educational significance and are enlightening and research-oriented for students’ minds, teachers promote students’ active exploration of mental health cases in relation to their own learning and life, and obtain more information that can be applied, analysed, and evaluated to effectively solve the mental health problems they encounter. Again, teachers should engage in moderate self-expression in the classroom, combining mental health knowledge with personal life experiences, discussing with students how their own professional, spiritual, or moral and emotional lives are intertwined (and sometimes conflicted) and how they have faced and resolved such problems, providing valuable and learnable modes of thinking for students’ growth, which will better facilitate students’ deeper learning, enabling students to connect mental health knowledge to their own learning and lives, to build personalised meanings of what they have learned, to apply what they have learned in the classroom to their real-life learning and lives, and to promote real-life problem solving and new creativity.


### 4.2. Student-Centred, Identifying the Learner as the Subject

The concept of “student-centred” and focusing on students’ deeper learning is the foundation of a high-quality undergraduate education. First, teachers should analyse the students’ situation and get to know them comprehensively, not only by analysing their existing mental health knowledge but also by understanding the learning, life, and psychological characteristics of students in the new era, and by tapping into their personal learning and life experiences to bring in material from their own lives for classroom learning, so that they can participate more deeply in classroom learning. Second, as students are the subjects of cognition and active constructors of knowledge meaning, teachers should take the initiative to explore and study how to better utilise the features of the online teaching mode to carry out mental health education teaching activities in the context of digital learning, with students’ learning at the centre, to enhance students’ interest in learning, promote the role of endogenous factors in students’ learning, cultivate students’ initiative and creativity, and guide them to deep learning.Teachers can therefore assign exploratory writing to students as a classroom assignment in mental health education, which can guide students in deeper learning while also providing indicators for process- and development-based assessment of achievement.


### 4.3. Create Effective Learning Contexts to Promote Student Engagement

Contextual cognitive theory suggests that knowledge is generated as learners interact with social and physical contexts. Creating effective learning contexts guided by contextual cognitive theory can better support and facilitate the occurrence of deep learning. First, it is important to create a safe classroom learning context. As the content of mental health education courses will involve students’ knowledge, inner experiences, and feelings about themselves and others, there is a need to create a trustworthy and safe learning context where students have the opportunity to self-express in an appropriate way, encourage them to openly express their true experiences and feelings in class, and be willing to discuss them with peers or teachers around them to promote personal reflection and discover the connections between old and new knowledge, or recognise the relevance of what they have learned to their learning and lives, and deepen their understanding, transfer, and application of mental health knowledge. Second, it is important to create a participatory, experiential, and interactive learning context in the classroom so that students learn knowledge and skills about mental health in a lively and active teaching and learning environment. An experiential and interactive classroom enables students to integrate their own active understanding and feelings into a state of “moving,” generating emotion and meaning, learning through conflict and joy, forming collisions between their own and others’ minds in performance and discussion, experiencing the rehearsal or realisation of various psychological needs, allowing emotional learning to take place in the classroom, and hitting students directly in the heart and soul. This allows emotional learning to take place in the classroom, to rethink the self, to rebuild the true self, to enhance problem-solving skills, and to promote the effective application of classroom teaching in real life. Once again, a culture of integration and symbiosis is a guarantee for the design of in-depth learning. In addition to psychology, the theoretical foundation of university students’ mental health education courses must have a high degree of cultural confidence, i.e., using the essence of Chinese culture to construct the Chinese heart, Chinese feelings, and the character and charm of Chinese people among university students. We should explore the unique thinking characteristics, behavioural habits, and value patterns of Chinese students imbued with Chinese culture and build a mental health education curriculum for university students with Chinese cultural characteristics.


## 4.1. Creating Deep Learning Classrooms with Pedagogical Intelligence

By selecting real-life cases that contain psychological principles, have educational significance and are enlightening and research-oriented for students’ minds, teachers promote students’ active exploration of mental health cases in relation to their own learning and life, and obtain more information that can be applied, analysed, and evaluated to effectively solve the mental health problems they encounter. Again, teachers should engage in moderate self-expression in the classroom, combining mental health knowledge with personal life experiences, discussing with students how their own professional, spiritual, or moral and emotional lives are intertwined (and sometimes conflicted) and how they have faced and resolved such problems, providing valuable and learnable modes of thinking for students’ growth, which will better facilitate students’ deeper learning, enabling students to connect mental health knowledge to their own learning and lives, to build personalised meanings of what they have learned, to apply what they have learned in the classroom to their real-life learning and lives, and to promote real-life problem solving and new creativity.


## 4.2. Student-Centred, Identifying the Learner as the Subject

The concept of “student-centred” and focusing on students’ deeper learning is the foundation of a high-quality undergraduate education. First, teachers should analyse the students’ situation and get to know them comprehensively, not only by analysing their existing mental health knowledge but also by understanding the learning, life, and psychological characteristics of students in the new era, and by tapping into their personal learning and life experiences to bring in material from their own lives for classroom learning, so that they can participate more deeply in classroom learning. Second, as students are the subjects of cognition and active constructors of knowledge meaning, teachers should take the initiative to explore and study how to better utilise the features of the online teaching mode to carry out mental health education teaching activities in the context of digital learning, with students’ learning at the centre, to enhance students’ interest in learning, promote the role of endogenous factors in students’ learning, cultivate students’ initiative and creativity, and guide them to deep learning.Teachers can therefore assign exploratory writing to students as a classroom assignment in mental health education, which can guide students in deeper learning while also providing indicators for process- and development-based assessment of achievement.


## 4.3. Create Effective Learning Contexts to Promote Student Engagement

Contextual cognitive theory suggests that knowledge is generated as learners interact with social and physical contexts. Creating effective learning contexts guided by contextual cognitive theory can better support and facilitate the occurrence of deep learning. First, it is important to create a safe classroom learning context. As the content of mental health education courses will involve students’ knowledge, inner experiences, and feelings about themselves and others, there is a need to create a trustworthy and safe learning context where students have the opportunity to self-express in an appropriate way, encourage them to openly express their true experiences and feelings in class, and be willing to discuss them with peers or teachers around them to promote personal reflection and discover the connections between old and new knowledge, or recognise the relevance of what they have learned to their learning and lives, and deepen their understanding, transfer, and application of mental health knowledge. Second, it is important to create a participatory, experiential, and interactive learning context in the classroom so that students learn knowledge and skills about mental health in a lively and active teaching and learning environment. An experiential and interactive classroom enables students to integrate their own active understanding and feelings into a state of “moving,” generating emotion and meaning, learning through conflict and joy, forming collisions between their own and others’ minds in performance and discussion, experiencing the rehearsal or realisation of various psychological needs, allowing emotional learning to take place in the classroom, and hitting students directly in the heart and soul. This allows emotional learning to take place in the classroom, to rethink the self, to rebuild the true self, to enhance problem-solving skills, and to promote the effective application of classroom teaching in real life. Once again, a culture of integration and symbiosis is a guarantee for the design of in-depth learning. In addition to psychology, the theoretical foundation of university students’ mental health education courses must have a high degree of cultural confidence, i.e., using the essence of Chinese culture to construct the Chinese heart, Chinese feelings, and the character and charm of Chinese people among university students. We should explore the unique thinking characteristics, behavioural habits, and value patterns of Chinese students imbued with Chinese culture and build a mental health education curriculum for university students with Chinese cultural characteristics.


## 5. Data Mining Applied to Student Mental Health Research

After years of development, data mining techniques have been used in a wide range of fields with good results that are unmatched by other technical methods. Data mining techniques are used to uncover the hidden laws and values in data to solve specific problems, and people can use computer applications to perform the same functions when they do not have the technology to do so. Therefore, this paper proposes to introduce data mining technology into the study of mental health data, mining and analysing the causes of students’ mental health problems, providing a scientific basis for early prevention and early intervention to control the emergence of psychological crises on campus.


### 5.1. ID3 Algorithm

Proposed by Quinlan in 1986, ID3 is a well-known algorithm in machine learning and is the first and most influential decision tree method proposed in the world [9]. In the ID3 algorithm, the most important feature is the selection of attributes. The attribute with the greatest gain will be used as the root node of the decision tree, and it is necessary to define the gain using entropy, one of the concepts of information theory. The information gain of the prepartition entropy and postpartition entropy is calculated as a criterion for judging the information content of an attribute [10].SupposeP is the set of 5 data samples. Then the category attributes that have M different values: Cii=1,2,…,m. Let Si be the number of samples in class Ci. For this given sample, its total information entropy is(1)Is1,s2,…,sm=−a∞i=1mPilog2Pi,where Pi is the probability of any sample belonging to Ci, which we can usually express as si/s.Assume that attributeA has a different value a1,a2,…,ak, that the set S is divided into no subsets s1,s2,…,sk by attribute A, and that the samples in the set S for which attribute A takes the value aj are included in subset sj. If the test attribute is determined to be attribute A, then the new leaf node generated from this set node is the subset described above. Let sij be the number of samples in subset sj with category Ci, and the information entropy value of the sample obtained by partitioning is(2)EA=j=1ks1j+s2j+⋯+smjsIs1j,s2j,…,smj,where Pij=sij/s1j+s2j+⋯+smj is the probability of a sample with category Ci in subset sj.Finally, the information gain (Gain) of the sample setS is obtained by dividing:(3)GainA=Is1,s2,…,sm−EA.Show that if the value of information entropyE(4) becomes smaller, the value of information gain Gain(A) becomes larger. Then the uncertainty of the test attribute A on the classification becomes small.


### 5.2. C4.5 Algorithms

Original ID3 algorithm mainly made the following adjustment changes.Split Information for AttributeA:(4)SplitIA=−∑j=1mSjS×log2SjS.In the above equation, attributeA splits the training data set S into m sub data sets, and the number of samples in the jth sub data set is denoted as Sj, while S is the total number of samples in the data set before the split.After splitting by attributeA, the value of the information gain rate of the sample set is(5)Gain RatioA=GainASplitA.When the decision tree is constructed and generated by the C4.5 algorithm, the splitting attribute of the current node is determined by the attribute with the maximum information gain rate. The information gain rate of the attribute is calculated to become progressively smaller, and the attribute with the relatively large information gain rate is determined as the splitting attribute when it is generated later.The decision tree-based C4.5 algorithm operates as follows.(1)
First, the information gain rate of each split attribute in the training sample set is obtained by calculation.(2)
The root node of the decision tree is determined by the splitting attribute with the largest information gain rate, and the data set is split into corresponding sub data sets according to the number of values taken.(3)
Step 1 and 2 are performed recursively in the sub data set in turn.


### 5.3. Association Rule Algorithm

Association rules reflect the interdependence and correlation between one thing and other things and are used to explore the correlation between valuable data items from a large variety of data.(1)
Representation of association rulesThe probability of item setA and item set B occurring together is called the support of an association rule, also known as relative support:(6)SupportA⇒B=PA∩B.When item setA occurs, the probability that item set B also occurs is the confidence level of the association rule:(7)ConfidenceA⇒B=PB,A.(2)
Minimum support and minimum confidenceThe threshold used to define a measure of support is often referred to as the minimum support, which represents the minimum statistical significance criterion for an item set. The threshold used to define a measure of confidence is often referred to as the minimum confidence level, which represents the minimum standard of reliability of an association rule. Rules that meet both the minimum support threshold and the minimum confidence threshold are often referred to as strong rules.(3)
Item setsA general frequent k-item set can be written asLk(4)
Support countsIf the support counts of the item set are known, the support and confidence of ruleA⇒B can be deduced from the support counts of all transactions, item set A and the support counts of the item set:(8)SupportA⇒B=A,BNumber of simultaneous transactionsNumber of all transactions=Support_countA∩BSupport_CountA,ConfidenceA⇒B=PB∣A=SupportA∩BSupportA=Support_countA∩BSupport_countA.As soon as the number of all transactions, iteration 5 and the support counts of the three item sets are obtained, the corresponding association rulesJ5 and 5d are generated, which ultimately determine whether they are strong association rules or not.The basic idea of the Apriori algorithm is to retrieve all frequent itemsets from the transaction database and find the maximum frequent itemset and a predefined minimum trust construct to generate a strong association rule.(1)
Properties of AprioriAll nonempty subsets of a frequent itemset are also frequent itemsets. According to this property, we can conclude that if we add transactionA to the set of items that are not frequent, the newly generated itemset I∩A cannot be a frequent itemset.(2)
The process of implementing the Apriori algorithm:(a)
By finding all frequent itemsets, where the support cannot be less than the minimum support threshold set, and by connecting and pruning the two steps in the process of finding, the largest frequent itemsetL can be obtained after several cycles of operation.


### 5.4. Mind Health Data Mining Business Processes

The SCL-90 was used to collect data on mental health problems among university students. The SCL-90 is a world-renowned measure of mental health that is widely used in the diagnosis of mental illness and detection of mental disorders, analysing the extent of a patient’s mental health status in 10 dimensions. The scale has 90 items SCL-90 rated on a 5-point scale (or 4-point scale) and contains a wide range of psychiatric symptomatology, including thinking and feeling, eating and sleeping, interpersonal relationships, behavioural habits, etc., from 10 factors (obsessive-compulsive symptoms, depression, somatisation, hostility, anxiety, interpersonal sensitivity, phobia, psychoticism, paranoia, and other symptoms). Psychoticism, paranoia, and others) reflecting 10 aspects of psychological symptomatology. The results are a reflection of the level of psychological symptoms over time and have the ability to distinguish between people with psychological health and those with psychological disorders. It is suitable for use in testing the presence or absence of symptoms of mental health problems in a population. It is generally used in hospitals for the detection of clinical psychological symptoms and can also be used for the screening of new university entrants for mental health.Due to the many iterations in data mining, the mining tools used in various fields are different in the widely used data mining techniques. The selection of appropriate data mining algorithms and data mining tools to guide computer analysis to obtain valuable knowledge information is always an important part of data mining efforts. By using the classification rule C4.5 algorithm and the association rule Apriori algorithm, the data mining operation on college students’ mental health data is carried out through the wood paper.The data mining process of college students’ mental health assessment data is shown in Figure1.Figure 1
Flow chart for data mining of mental health assessment for university students.The database system of a health vocational college used SQL Server 2008 to store and manage students’ personal information, including student number, ID card number, name, gender, ethnicity, date of birth, place of origin, household registration, department, major, only child or not, and contact telephone number [20, 21]. The student’s personal psychological assessment form is generated by the psychological assessment system and reflects the student’s tendency to have personal psychological problems through the relevant psychological dimensions. It contains the student’s student number, name, topic 1 … And 10 psychological dimensions: obsessive-compulsive symptoms, depression, somatisation, hostility, anxiety, relationship sensitivity, phobia, psychoticism, paranoia, and other fields, as shown in Table 1.Table 1
Selected data content after data integration.
GenderRegistered residenceFamily statusFacultySomatizationObsessive compulsive symptomsDepressedAnxiousPsychoticOtherFemaleTownYesNon needy households1.331.51.311.41.11.29FemaleTownYesNon needy households1.172.11.921.61.51.71FemaleCountrysideNoDifficult family1.922.32.152.221.86FemaleCountrysideNoNon needy households1.922.122.11.61.57FemaleCountrysideNoNon needy households1.332.11.151.31.21.43FemaleTownYesNon needy households1.52.32.772.42.41.86FemaleCountrysideNoNon needy households1.081.61.231.21.11FemaleCountrysideNoNon needy households2.332.42.231.71.31.71FemaleCountrysideNoNon needy households1.51.72.311.31.41.29FemaleCountrysideNoNon needy households1.171.21.311.111.29MaleTownYesNon needy households2.082.71.922.62.22.14FemaleCountrysideNoDifficult family2.582.92.462.42.22.43FemaleTownYesNon needy households11.31111.14FemaleCountrysideNoDifficult family1.171.91.541.61.51.14MaleCountrysideYesNon needy households1111.11.11Data integration is the process of integrating records from multiple related data collections into a new data warehouse based on the content of the mining objectives. The data used in the thesis are mainly from the Basic Student Information Form and the SCL-90 Mental Health Assessment Form. The two tables are linked through the association fields, and the data set determined by the “data selection” process is used to generate a new mental health assessment form for students, as shown in Table2.Table 2
Mental health assessment scale for university students (partial data).
XBHKDSZNJTZKBXQTHQPZZRJGXYYJLDDKBPZJSPXQTXB2HK1DS1JT2BX1QTH2QP2RJ2Y1JL2D2KB2PZ2JS2QT2XB2HK1DS1JT2BX1QTH2QP1RJ2Y1JL2D2KB2PZ2JS2QT2XB2HK2DS2JT1BX1QTH2QP1RJ1Y2JL1D1KB1PZ1JS2QT2XB2HK2DS2JT1BX1QTH2QP1RJ1Y2JL1D1KB1PZ2JS2QT2XB2HK1DS2JT2BX1QTH2QP1RJ2Y1JT2D2KB2PZ2JS2QT2XB2HK2DS1JT2BX1QTH2QP1RJ1Y2JT1D1KB1PZ1JS1QT2XB2HK1DS2JT2BX1QTH2QP2RJ2Y1JT2D2KB2PZ2JS2QT2


## 5.1. ID3 Algorithm

Proposed by Quinlan in 1986, ID3 is a well-known algorithm in machine learning and is the first and most influential decision tree method proposed in the world [9]. In the ID3 algorithm, the most important feature is the selection of attributes. The attribute with the greatest gain will be used as the root node of the decision tree, and it is necessary to define the gain using entropy, one of the concepts of information theory. The information gain of the prepartition entropy and postpartition entropy is calculated as a criterion for judging the information content of an attribute [10].SupposeP is the set of 5 data samples. Then the category attributes that have M different values: Cii=1,2,…,m. Let Si be the number of samples in class Ci. For this given sample, its total information entropy is(1)Is1,s2,…,sm=−a∞i=1mPilog2Pi,where Pi is the probability of any sample belonging to Ci, which we can usually express as si/s.Assume that attributeA has a different value a1,a2,…,ak, that the set S is divided into no subsets s1,s2,…,sk by attribute A, and that the samples in the set S for which attribute A takes the value aj are included in subset sj. If the test attribute is determined to be attribute A, then the new leaf node generated from this set node is the subset described above. Let sij be the number of samples in subset sj with category Ci, and the information entropy value of the sample obtained by partitioning is(2)EA=j=1ks1j+s2j+⋯+smjsIs1j,s2j,…,smj,where Pij=sij/s1j+s2j+⋯+smj is the probability of a sample with category Ci in subset sj.Finally, the information gain (Gain) of the sample setS is obtained by dividing:(3)GainA=Is1,s2,…,sm−EA.Show that if the value of information entropyE(4) becomes smaller, the value of information gain Gain(A) becomes larger. Then the uncertainty of the test attribute A on the classification becomes small.


## 5.2. C4.5 Algorithms

Original ID3 algorithm mainly made the following adjustment changes.Split Information for AttributeA:(4)SplitIA=−∑j=1mSjS×log2SjS.In the above equation, attributeA splits the training data set S into m sub data sets, and the number of samples in the jth sub data set is denoted as Sj, while S is the total number of samples in the data set before the split.After splitting by attributeA, the value of the information gain rate of the sample set is(5)Gain RatioA=GainASplitA.When the decision tree is constructed and generated by the C4.5 algorithm, the splitting attribute of the current node is determined by the attribute with the maximum information gain rate. The information gain rate of the attribute is calculated to become progressively smaller, and the attribute with the relatively large information gain rate is determined as the splitting attribute when it is generated later.The decision tree-based C4.5 algorithm operates as follows.(1)
First, the information gain rate of each split attribute in the training sample set is obtained by calculation.(2)
The root node of the decision tree is determined by the splitting attribute with the largest information gain rate, and the data set is split into corresponding sub data sets according to the number of values taken.(3)
Step 1 and 2 are performed recursively in the sub data set in turn.


## 5.3. Association Rule Algorithm

Association rules reflect the interdependence and correlation between one thing and other things and are used to explore the correlation between valuable data items from a large variety of data.(1)
Representation of association rulesThe probability of item setA and item set B occurring together is called the support of an association rule, also known as relative support:(6)SupportA⇒B=PA∩B.When item setA occurs, the probability that item set B also occurs is the confidence level of the association rule:(7)ConfidenceA⇒B=PB,A.(2)
Minimum support and minimum confidenceThe threshold used to define a measure of support is often referred to as the minimum support, which represents the minimum statistical significance criterion for an item set. The threshold used to define a measure of confidence is often referred to as the minimum confidence level, which represents the minimum standard of reliability of an association rule. Rules that meet both the minimum support threshold and the minimum confidence threshold are often referred to as strong rules.(3)
Item setsA general frequent k-item set can be written asLk(4)
Support countsIf the support counts of the item set are known, the support and confidence of ruleA⇒B can be deduced from the support counts of all transactions, item set A and the support counts of the item set:(8)SupportA⇒B=A,BNumber of simultaneous transactionsNumber of all transactions=Support_countA∩BSupport_CountA,ConfidenceA⇒B=PB∣A=SupportA∩BSupportA=Support_countA∩BSupport_countA.As soon as the number of all transactions, iteration 5 and the support counts of the three item sets are obtained, the corresponding association rulesJ5 and 5d are generated, which ultimately determine whether they are strong association rules or not.The basic idea of the Apriori algorithm is to retrieve all frequent itemsets from the transaction database and find the maximum frequent itemset and a predefined minimum trust construct to generate a strong association rule.(1)
Properties of AprioriAll nonempty subsets of a frequent itemset are also frequent itemsets. According to this property, we can conclude that if we add transactionA to the set of items that are not frequent, the newly generated itemset I∩A cannot be a frequent itemset.(2)
The process of implementing the Apriori algorithm:(a)
By finding all frequent itemsets, where the support cannot be less than the minimum support threshold set, and by connecting and pruning the two steps in the process of finding, the largest frequent itemsetL can be obtained after several cycles of operation.


## 5.4. Mind Health Data Mining Business Processes

The SCL-90 was used to collect data on mental health problems among university students. The SCL-90 is a world-renowned measure of mental health that is widely used in the diagnosis of mental illness and detection of mental disorders, analysing the extent of a patient’s mental health status in 10 dimensions. The scale has 90 items SCL-90 rated on a 5-point scale (or 4-point scale) and contains a wide range of psychiatric symptomatology, including thinking and feeling, eating and sleeping, interpersonal relationships, behavioural habits, etc., from 10 factors (obsessive-compulsive symptoms, depression, somatisation, hostility, anxiety, interpersonal sensitivity, phobia, psychoticism, paranoia, and other symptoms). Psychoticism, paranoia, and others) reflecting 10 aspects of psychological symptomatology. The results are a reflection of the level of psychological symptoms over time and have the ability to distinguish between people with psychological health and those with psychological disorders. It is suitable for use in testing the presence or absence of symptoms of mental health problems in a population. It is generally used in hospitals for the detection of clinical psychological symptoms and can also be used for the screening of new university entrants for mental health.Due to the many iterations in data mining, the mining tools used in various fields are different in the widely used data mining techniques. The selection of appropriate data mining algorithms and data mining tools to guide computer analysis to obtain valuable knowledge information is always an important part of data mining efforts. By using the classification rule C4.5 algorithm and the association rule Apriori algorithm, the data mining operation on college students’ mental health data is carried out through the wood paper.The data mining process of college students’ mental health assessment data is shown in Figure1.Figure 1
Flow chart for data mining of mental health assessment for university students.The database system of a health vocational college used SQL Server 2008 to store and manage students’ personal information, including student number, ID card number, name, gender, ethnicity, date of birth, place of origin, household registration, department, major, only child or not, and contact telephone number [20, 21]. The student’s personal psychological assessment form is generated by the psychological assessment system and reflects the student’s tendency to have personal psychological problems through the relevant psychological dimensions. It contains the student’s student number, name, topic 1 … And 10 psychological dimensions: obsessive-compulsive symptoms, depression, somatisation, hostility, anxiety, relationship sensitivity, phobia, psychoticism, paranoia, and other fields, as shown in Table 1.Table 1
Selected data content after data integration.
GenderRegistered residenceFamily statusFacultySomatizationObsessive compulsive symptomsDepressedAnxiousPsychoticOtherFemaleTownYesNon needy households1.331.51.311.41.11.29FemaleTownYesNon needy households1.172.11.921.61.51.71FemaleCountrysideNoDifficult family1.922.32.152.221.86FemaleCountrysideNoNon needy households1.922.122.11.61.57FemaleCountrysideNoNon needy households1.332.11.151.31.21.43FemaleTownYesNon needy households1.52.32.772.42.41.86FemaleCountrysideNoNon needy households1.081.61.231.21.11FemaleCountrysideNoNon needy households2.332.42.231.71.31.71FemaleCountrysideNoNon needy households1.51.72.311.31.41.29FemaleCountrysideNoNon needy households1.171.21.311.111.29MaleTownYesNon needy households2.082.71.922.62.22.14FemaleCountrysideNoDifficult family2.582.92.462.42.22.43FemaleTownYesNon needy households11.31111.14FemaleCountrysideNoDifficult family1.171.91.541.61.51.14MaleCountrysideYesNon needy households1111.11.11Data integration is the process of integrating records from multiple related data collections into a new data warehouse based on the content of the mining objectives. The data used in the thesis are mainly from the Basic Student Information Form and the SCL-90 Mental Health Assessment Form. The two tables are linked through the association fields, and the data set determined by the “data selection” process is used to generate a new mental health assessment form for students, as shown in Table2.Table 2
Mental health assessment scale for university students (partial data).
XBHKDSZNJTZKBXQTHQPZZRJGXYYJLDDKBPZJSPXQTXB2HK1DS1JT2BX1QTH2QP2RJ2Y1JL2D2KB2PZ2JS2QT2XB2HK1DS1JT2BX1QTH2QP1RJ2Y1JL2D2KB2PZ2JS2QT2XB2HK2DS2JT1BX1QTH2QP1RJ1Y2JL1D1KB1PZ1JS2QT2XB2HK2DS2JT1BX1QTH2QP1RJ1Y2JL1D1KB1PZ2JS2QT2XB2HK1DS2JT2BX1QTH2QP1RJ2Y1JT2D2KB2PZ2JS2QT2XB2HK2DS1JT2BX1QTH2QP1RJ1Y2JT1D1KB1PZ1JS1QT2XB2HK1DS2JT2BX1QTH2QP2RJ2Y1JT2D2KB2PZ2JS2QT2


## 6. Effectiveness of Mental Health Education

From the analysis of the causes of crimes committed by university students, it can be seen that many university students commit crimes because they do not have a firm political stance and a high moral quality. And the cultivation of socialist ideological character and the improvement of the political and ideological awareness and moral level of all people is one of the fundamental goals of mental health education. As shown in Figure2, through the energy diagram of the psychological changes of university students after the data analysis in this paper, we can know that mental health education can guide university students to master the scientific worldview and methodology, so that they have the initial ability to draw the line between materialism and materialism, science and superstition, civilization and ignorance, and resist materialism, feudal superstition, and all kinds of pseudoscience [21, 22].Figure 2
Heart health energy map.As shown in Figure3, psychological problems are one of the major causes of crime among university students, and many university students commit criminal acts not because of moral quality but because of psychological problems, so it is very important to ensure psychological health, both for society and for individual university students. Mental health education can educate and guide university students, through persuasion and motivation, strengthen psychological counselling for different individual requirements, focus on guiding university students to form positive emotions, moderate emotions, harmonious interpersonal relationships, good personality qualities and a strong will, and help them develop healthy psychological qualities, thus eliminating the situation of university students committing crimes due to psychological problems.Figure 3
Propensity to offend after psychoeducation.College students have strong self-esteem and competitive spirit during adolescence, and because they come from different regions and families, they have obvious differences in their lifestyles, ideologies, and moral standards, and conflicts and contradictions can easily arise between them. The association of different factors is shown in Figure4, where the study shows that 30% of vocational school students have some psychological barriers to communication, lack self-confidence, and are overly cautious and timid, leading to severe psychological depression. In their interactions with teachers, peers, and family members, not wanting to open up about their ideas, their ability to adapt, academic problems, interpersonal relationships, etc., students usually show loneliness, suspicion, jealousy, reluctance, or fear of communicating with others, leading to poor communication and interpersonal barriers.Figure 4
Different factors associated with university students.


## 7. Conclusions

After years of exploration, the mental health education curriculum in colleges and universities has received increasing attention from schools, and great progress has been made in the understanding of the curriculum and its construction. However, there are some shortcomings in the course of curriculum implementation, such as the lack of institutional, financial, and human resources support for curriculum organisation, excessive refinement of curriculum objectives, neglect of students’ needs in curriculum content, and a single way of curriculum evaluation. This paper analyses the mental health of college students based on data mining algorithms, and the experiments show that under current educational conditions, it is important to adhere to a student-oriented in-depth learning orientation and emphasize the developmental and educational nature of classroom teaching.



---

*Source: 8936152-2022-06-17.xml*