Optimization of Teaching Model and Design of Intelligent Algorithm for College Students’ Psychological Education Course Based on Cognitive Behavioral Theory
Publié en ligne: 05 juin 2025
Reçu: 06 janv. 2025
Accepté: 22 avr. 2025
DOI: https://doi.org/10.2478/amns-2025-0978
Mots clés
© 2025 Wei Zhang et al., published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 International License.
In recent years, the competitive situation in the society is getting tighter and tighter, and the pressure on college students is also increasing day by day. Multiple challenges, such as heavy academic tasks, heavy employment pressure, complex interpersonal interactions, self-conceptualization problems, and life experiences, have gradually highlighted the mental health problems of college students [1-4]. This not only affects their studies, but also indirectly leads to a decline in their quality of life.
With the increasing concern of the society about the mental health problems of college students, under the intervention form of single psychological counseling, colleges and universities have begun to introduce psychological education courses and educational interventions as a way to help students identify and cope with psychological problems [5-7]. However, most of the current psychoeducational methods are still based on one-way output, with insufficient interactivity, which makes it difficult for students to recognize their own psychological problems, and the courses are single, making it difficult to fit the real needs of college students [8-9]. And the cognitive-behavioral theory proposed by Ellis, Beck, and Meckenbaum advocates that human thinking, emotions, and behaviors are interactive [10]. In cognitive-behavioral theory, cognition represents an individual’s perception, understanding, and interpretation of himself and the external world, and behavior refers to an individual’s performance and action, which regards an individual’s cognition and behavior as a whole and emphasizes the influence of human thinking on emotions and behavior [11-12]. Cognitive-behavioral theory believes that individuals learn and change their behaviors by observing and imitating the behaviors of others, and it also emphasizes the relationship between external stimuli and individuals’ interpretations of and responses to the stimuli, i.e., people’s behaviors are mainly affected by their perceptions and appraisals of the stimuli [13-14]. And cognitive-behavioral theory occupies an indispensable position in psychotherapy and brings new hope to the teaching of psychological courses [15-16].
This paper explores the path of cognitive-behavioral theory from two aspects of social cognition and behavior. A semantic network structure is proposed to represent knowledge using a graph structure. Knowledge is organized and connected using a mesh structure, and the connections between knowledge points are described through visualization techniques to establish a knowledge system and to mine and display the invisible relationships of knowledge. The knowledge graph is applied to the psychoeducational course, based on MinsCourse, and the students’ cognitive behavior data are acquired based on their use in the knowledge graph. Cluster analysis of the collected data of students’ cognitive rows is carried out, and some clusters of learning behaviors are obtained through clustering division, and second-order clustering method is chosen for revealing the naturally occurring groupings in the dataset. The experimental process was sorted out to complete the testing of the teaching model of college students’ psychoeducation course through the model of pre-test, teaching and post-test.
Generally speaking, social cognition is cognitive activity or cognitive processes, including beliefs and belief systems, thinking and imagination. Specifically cognition refers to a person’s knowledge and perception of a thing or an object, his or her own views, thoughts about others, awareness of the environment and insights into events and so on. Cognition involves two different levels in terms of content: first, cognition about the person himself, including cognition about various mental activities (attention, perception, thinking, emotion, motivation, memory, etc.) and thinking points of view of oneself and others. Second, the cognition about various bilateral relationships between people, such as the cognition of friendship, conflict, cooperation and other relationships. The most representative of these is Bandura’s social cognitive theory.
Mutual Determinism According to Bandura, “Behavior, cognition, and environment are interconnected and mutually determinative, a process that involves the interaction of the three factors rather than a combination of the two or a unidirectional interaction between the two. Behavioral and environmental conditions function as interacting determinants. Human cognitive factors (i.e., perceptions, beliefs, and self-perceptions) and behavior are also interactive determinants of each other. Observational Learning Observational learning is a basic concept of Bandura’s social learning theory. In the past, both cognitive and behaviorist schools focused on the acquisition of direct experience and reinforcement, while Bandura believed that any behavior acquired by direct experience can be acquired by observing the behavior of role models, and similarly, a certain behavior can be learned without personally experiencing direct reinforcement, through the alternative response and alternative reinforcement of role models. Self-efficacy Self-efficacy refers to an individual’s judgmental beliefs or subjective self-grasp and feelings about his or her ability to perform a certain activity at a certain level [17]. Theoretically, self-efficacy is the ability that people show to perform a certain activity. But in practice, this ability is not a definite value, it is only a potential self-factor, a variable value of feelings. It is not the competence itself that has an impact on the behaving subject, but the subject’s beliefs about his or her own competence.
Behaviorism suggests that human behavior is learned and can be changed, increased, or eliminated through learning, and that individuals are motivated to learn by reinforcement. For example, if a person is rewarded for certain behaviors, or if the behavior is met with unexpected praise, the behavior is easily learned and more likely to be sustained. Conversely, if a person is punished for a behavior, or if the behavior does not have the desired outcome, the behavior will be difficult to sustain.
Cognitive-behavioral theory is a set of methods to change maladaptive cognitions and maladaptive behavioral responses by altering thinking or beliefs and behaviors; it is an integration of cognitive and behavioral theories, and it is an addition to, and a development of, the deficiencies that exist in cognitive and behavioral theories. It is both the individual’s exposure to environmental events and the individual’s construction of meaning for those events. Individuals behave in response to cognitive imagery of environmental events, for example, they selectively attend to or interpret the meaning of events.
Knowledge graph is a model for knowledge representation, management and organization, which is essentially a semantic network that uses a graph structure to represent knowledge, where nodes in the graph represent entities or concepts and edges represent relationships between entities or concepts [18]. Knowledge graph organizes knowledge using a mesh structure, uses visualization techniques to describe knowledge and the relationships between them, builds a knowledge system and mines and displays the invisible relationships of knowledge, and gives humans an easier model for organizing, managing, and understanding knowledge.
Educational Knowledge Graph is the application of Knowledge Graph in the field of education. Educational knowledge mapping associates a large number of fragmented, multimodal educational resources of various types with each entity to build a semantic network, which focuses on realizing the association between knowledge and educational resources and demonstrates the relationship between knowledge points.
Educational knowledge mapping is a carrier of educational resources, a technical means to realize the modernization of education, which can better assist teachers in teaching and students in learning. The advantages of educational knowledge mapping are mainly reflected in the following points:
The net-like resource organization mode, which can associate scattered teaching resources with knowledge points, and integrates a variety of modal teaching resources, including videos, courseware, exercises and other multi-kinds of teaching resources. The form of its semantic network can accurately express the relationship between knowledge points and between knowledge points and teaching resources, and can construct a complete knowledge framework of the discipline or course. Educational knowledge mapping is an advanced technology for realizing educational intelligence, providing technical means such as user profiling, personalized recommendation, behavioral prediction, precise analysis, and decision support. The common forms of educational knowledge mapping are subject knowledge mapping and curriculum knowledge mapping. Discipline knowledge mapping is to reveal the relationship between the development history of the discipline and the knowledge structure of the discipline in the form of visualization, which can be used to assist in the construction of the discipline, and can also be used for teaching and learning of the discipline. Course knowledge mapping is used to show the knowledge network of a single course, mainly used to assist course teaching and learning.
The first step is to build the learning resources needed to be used for the knowledge mapping of college students’ mental health courses, including course micro-videos, courseware, exercise libraries, reference documents, etc., and deploy the learning resources on the online teaching platform, and the second step is to choose the tools for constructing the knowledge mapping of the courses.
Referring to the catalogue of multiple textbooks of “Mental Health of College Students”, this paper sorts out the knowledge structure of Java programming courses, clarifies the hierarchical relationship of concepts, and divides modules according to the aggregation degree of content, and divides 7 modules, including “Java Language Overview”, “Java Language Basics”, “Java Object-Oriented Basics”, “Java Advanced Features”, “Exception Handling”, “Input and Output Flow”, and “Common Tools”, as the top-level concepts. Then sort out the knowledge points in each module, and first display the knowledge points and the relationship between the knowledge points in a tree structure in the form of a mind map. When sorting out the course knowledge points, the granularity of the decomposition of the knowledge points should be fine, and the content involved in the mental health syllabus for college students should be fully covered.
Create all knowledge points based on the structure of the knowledge points that you have previously sorted out. When creating knowledge points, the naming of knowledge points should be concise and should not be named too long. The college mental health course created a total of 172 knowledge points in the course knowledge map.
After constructing the knowledge points, the relationship between the knowledge points was established in the knowledge graph of the college students’ mental health course, and the relationship between the knowledge points was “pre-position”, “post-position” and “association”. An antecedent relationship refers to a point of knowledge that is a prerequisite for another point of knowledge. Posterior relationship means that a knowledge point is a subsequent knowledge point of another knowledge point. Correlation refers to the fact that there is no relationship between two knowledge points, but rather a correlation between the knowledge points.
After constructing the knowledge points and the relationship between the knowledge points in the knowledge mapping of college students’ mental health courses, the next step is to mount all kinds of teaching resources to the relevant knowledge points. Common teaching resources include course videos, courseware, exercises, various types of documents, etc., just choose the corresponding teaching resources and knowledge points associated together.
Cognitive behavior analysis relies on students’ cognitive behavior data, which is mainly acquired through questionnaire collection and automatic collection by the online learning system.
In the knowledge graph and online courseware interface of MinsCourse teaching system, each node in the knowledge graph of the course chapter corresponds to a knowledge point, students first select the first knowledge node to learn, click “Start Learning” to learn the course knowledge content, and when they click on other nodes or “End Learning” to end the learning of the previous knowledge point, and record the learning time of the knowledge point and store it in the database table. Time nodes are recorded when students download assignments and upload assignments in the MinsCourse system, and the length of time and the number of revisions of assignments completed by students are calculated. The daily cognitive behavior data of students obtained through the MinsCourse system is more objective compared to the questionnaire collection, and the student behavior data is stored in the database, which can be exported by data managers and analysts to directly access the cognitive behavior data of all student users.
In order to better have an in-depth and comprehensive understanding of students’ cognitive behavior and knowledge learning, the data collected through the knowledge map
In this paper, we propose an evaluation index of course knowledge learning based on the mastery level of knowledge points. In Mins Coursc, during the learning process of knowledge mapping and online courseware, the system will record the students’
For each knowledge point in the college mental health course, the calculation of students’ knowledge point score is carried out. Assuming that there are
In order to rate students’ cognitive behavior and classify the difficulty of knowledge points, this paper carries out a cluster analysis of the collected students’
Clustering is a method of dividing a large number of data objects into different groups or classes according to certain rules, and the divided groups and classes are called clusters, with high similarity of data within each cluster and high difference of data between clusters. As opposed to categorization, clustering is the process of bringing similar things together and there is no definition of specific categories before clustering. The analysis based on clustering is called cluster analysis, which is usually used to divide data into different clusters through clustering from a large amount of data with no a priori knowledge and no regularity, analyze the characteristics of data in different clusters, and summarize the change rule of data. Algorithms in cluster analysis include many kinds of algorithms, such as prototype clustering, density clustering, hierarchical clustering, for the above clustering objectives, this paper chooses a common prototype clustering algorithm that is K-Means (
K-means algorithm flow
The algorithm uses the least squared error
Student cognitive behavior ratings
For each student, the learning of knowledge points in the assignment has data corresponding to
The final weighted vectors of all the students of the course are clustered, and after repeated iterations, the resulting clusters are assumed to be
Analyzing the cluster center vector data of each cluster, classifying the student data contained therein, and rating the cognitive behaviors of students in each category, the distribution of cognitive behaviors of students in the course can be seen as a whole.
Difficulty Classification of Knowledge Points
For each knowledge point, there are multiple sets of
The final weighted vectors of the course knowledge points are clustered, and after repeated iterations, the resulting clusters are assumed to be
Analyzing the cluster center vector data of each cluster, classifying the knowledge point data contained in it, and dividing the difficulty level of each category of knowledge points, the difficulty distribution of the knowledge points of the course can be seen as a whole.
Before selecting the samples, based on the results of the quantitative study above, important attributes such as education, cognitive dissonance, and learning burnout were first selected for clustering and division, with the purpose of exploring whether there are classes within the group of college students that are distinctly different in these attributes. If such classes do exist, then consideration needs to be given to covering all the categories when selecting the interview samples, so that the samples of the qualitative research are as representative as possible of the whole of the research population.
Clustering refers to the division of a set into multiple classes, where elements in the same class are similar to each other and elements in different classes differ significantly. Because the clustering variables involved in this study are both categorical and continuous, second-order clustering was chosen.
Second-order clustering, also known as TwoStep clustering, is an exploratory process of analysis used to reveal naturally occurring groupings in a data set [20]. The advantages of the algorithm used for second-order clustering are: the first is that it can be accomplished for both categorical and continuous variables, and the second is that the optimal number of clusters can be determined automatically by comparing the model selection criterion values across cluster solutions. The third is that large data can be analyzed by constructing a feature tree of summary records. Also second order clustering has strict conditions of application, it requires that the variables are independent of each other, while the categorical variables are polynomially distributed and the continuous variables are normally distributed.
The whole clustering process is completed in two steps: in the first step, each record is examined to construct a CF categorical feature number, records within the same tree node will be more similar, and records with less similarity will be regenerated into new nodes. In the second step, on the basis of the already generated classification tree, all cohesive points are then classified using the coalescence method, and the results of each clustering will be judged using the Bayesian Information Criterion (BIC) or the Akaike Information Criterion (AIC) to arrive at the final result. Table 1 shows the clustering results and the main characteristics of the categories, and finally the samples are clustered into 5 categories, in which the category with the largest sample size accounts for 27.36% and the smallest category accounts for 10.82%, and the difference between each category is more significant. The indicator characteristics of each category are listed in the table below:
Clustering results and main characteristics of categories
| Categories | Percentage of the mix | Main feature |
|---|---|---|
| A | 19.24% | There is a certain degree of cognitive dissonance, low degree of emotional exhaustion, low degree of deindividuation, and center of accomplishment |
| B | 10.82% | There is a certain degree of cognitive dissonance, degree of emotional exhaustion, low degree of deindividuation, and sense of accomplishment |
| C | 19.87% | There is a certain degree of cognitive dissonance, emotional exhaustion, low degree of deindividuation, low sense of accomplishment |
| D | 27.36% | There is a higher degree of cognitive dissonance, a high degree of emotional exhaustion, a high degree of deindividuation Sensory center |
| E | 22.71% | There is basically no cognitive dissonance, in the degree of emotional exhaustion, low degree of deindividuation, achievement Middle sensation |
Figure 2 shows the analysis of the clustering results, and it can be seen that the four categories of students with the highest percentage of the variable characteristics of cognitive dissonance, emotional exhaustion, and de-personalization are all the highest, with the exception of low-achievement, which are 34.195, 3.745, and 3.766, respectively.
Clustering result analysis,
The whole research process is divided into three parts: pre-test, teaching, and post-test. Before the implementation of teaching and one week after the completion of teaching, the subjects in the experimental group and the control group, need to complete the Le Quotient Questionnaire, Resilience Questionnaire, Loneliness Scale, Life Satisfaction Scale. In the teaching process, the experimental group used the knowledge mapping college students’ psychological education course teaching model, and the control group teaching method (using traditional knowledge-based teaching) remained unchanged. The teaching arrangement and content of the control group were synchronized with that of the experimental group, and the difference was only in the choice of teaching method. The traditional knowledge-based teaching emphasized the input of ideas, and there was no arrangement of behavioral learning and training tasks, and at the same time, the organization of the teaching only involved fewer group interaction sessions.
SPSS statistical software was used to conduct paired samples t-tests on the data results of the pre-test and post-test.
In order to test the application effect of the teaching model of college students’ psychoeducation courses at the level of knowledge mastery, SPSS software was used to conduct paired-sample t-tests on the data of students’ diagnostic and summary knowledge test scores, and Figure 3 shows the comparison of students’ pre- and post-test mental health knowledge test scores. From the results of the analysis, it can be seen that after participating in the mental health education designed by the “Knowledge Graph Mental Health Teaching Curriculum Model for College Students”, the average value of the students’ scores is higher than that of the students who did not study, and the average scores of the pre- and post-tests are 60.92 and 83.93, respectively. It shows that the designed knowledge mapping teaching model for college students’ psychoeducation courses can effectively achieve the purpose of assessment for learning and improve students’ mastery of knowledge.
Comparison of test results of mental health knowledge before and after students
SPSS software was used to conduct paired-sample t-test on the pre- and post-test data of students’ mental health literacy, and the results are shown in Table 2. From the analysis results, it can be seen that after participating in the process evaluation activities designed by the “Knowledge Mapping Teaching Model of College Students’ Mental Health Education Curriculum”, the students’ psychological cognition, situational learning, practical inquiry, social interaction, and rational decision-making dimensions have a significant difference compared with the pre-test (p<0.001). In terms of mean values, the mean values of the data of the dimensions of mental health literacy in the post-test were higher than those in the pre-test, and the range of the mean values was between [-1.3595,-0.9466].
Test the test of the students’ mental health literacy data
| Front and rear measurement dimensions | Mean value | Standard deviation | T | Freedom | Significant p values |
|---|---|---|---|---|---|
| Mental cognition | -1.1658 | 0.5866 | -9.5264 | 21 | 0.0000*** |
| Situational learning | -1.3165 | 0.8415 | -7.3499 | 21 | 0.0000*** |
| Practical inquiry | -1.3595 | 0.8135 | -7.6948 | 21 | 0.0000*** |
| Social interaction | -0.9466 | 0.5566 | -7.9563 | 21 | 0.0000*** |
| Rational decision | -1.0364 | 0.7315 | -6.4263 | 21 | 0.0000*** |
| Creative display | -1.3498 | 0.6348 | -9.9165 | 21 | 0.0000*** |
| Value reflection | -1.0645 | 0.7648 | -6.4658 | 21 | 0.0000*** |
Figure 4 shows the comparison of students’ quality data in the pre and post-tests, and all the dimensions of college students’ mental health literacy have been improved compared with the pre-tests, and the mean scores of the pre and post-tests are 4.924 points and 6.101 points, respectively. The most significant improvement is in the dimension of “Creative Presentation”. This indicates that the evaluation activities designed by the teaching model of college students’ mental health education program through knowledge mapping can effectively improve students’ mental health literacy, especially in the aspect of creative presentation, which has a more obvious effect.
The quality data comparison of the students before and after the student
Table 3 shows the difference test of the pretest results between the experimental group and the control group, and Table 4 shows the difference test of the posttest results between the experimental group and the control group, at the beginning of the experiment, there was no significant difference in the scores of each scale between the control group and the experimental group, and the P value was > 0.05. In Table 4, at the end of the experiment, there is a significant difference between the control group and the experimental group in the score of the resilience scale (p<0.05), and there is no significant difference in the other three items, which shows that the teaching model of the college students’ psychoeducation course based on the knowledge map has begun to be effective.
Test of the difference between the experimental group and the control group
| / | N | M | SD | T | P | |
|---|---|---|---|---|---|---|
| Philman | Control group | 13 | 50.615 | 6.428 | 0.6918 | 0.4936 |
| Experimental group | 13 | 49.741 | 7.236 | |||
| Energy | Control group | 13 | 29.416 | 5.716 | 0.9769 | 0.3184 |
| Experimental group | 13 | 28.436 | 6.269 | |||
| Loneliness | Control group | 13 | 42.716 | 8.246 | -1.0652 | 0.2936 |
| Experimental group | 13 | 43.315 | 7.856 | |||
| Life satisfaction | Control group | 13 | 19.066 | 4.936 | 1.5648 | 0.1254 |
| Experimental group | 13 | 18.036 | 5.136 | |||
Test results of the experimental group and the control group
| / | N | M | SD | T | P | |
|---|---|---|---|---|---|---|
| Philman | Control group | 13 | 51.394 | 6.345 | -0.5266 | 0.5716 |
| Experimental group | 13 | 51.766 | 6.215 | |||
| Energy | Control group | 13 | 28.566 | 4.626 | -2.0965 | 0.0385* |
| Experimental group | 13 | 29.826 | 5.593 | |||
| Loneliness | Control group | 13 | 41.945 | 7.826 | 0.3648 | 0.7168 |
| Experimental group | 13 | 41.536 | 8.132 | |||
| Life satisfaction | Control group | 13 | 19.139 | 4.826 | -1.1439 | 0.2548 |
| Experimental group | 13 | 19.848 | 5.398 | |||
Table 5 shows the results of the comparative analysis of the differences between the pre- and post-tests of the experimental group and the control group, and the results of the post-test of the control group are better than those of the pre-test, which indicates that the traditional teaching method, although insufficient, also contributes to the enhancement of the quality of the mental health of college students (e.g., resilience). The teaching of mental health courses based on knowledge mapping is effective and a beneficial model of mental health education for college students. However, in the comparison of pre- and post-test differences in Le Quotient (t=3.6485, P=0<0.05) and Life Satisfaction (t=3.9482, P=0<0.05), it was found that there was a significant difference between the experimental group and the control group, indicating that as far as the teaching effect is concerned, the teaching effect of the experimental group is better than that of the control group.
The results of the comparison were compared with the control group
| / | N | T | P | |
|---|---|---|---|---|
| Le Shang (post-test - pre-test) | Control group | 13 | 3.6485 | 0.0000*** |
| Experimental group | 13 | |||
| Stress resistance (post-test - pre-test) | Control group | 13 | 0.5264 | 0.6152 |
| Experimental group | 13 | |||
| Loneliness (post-test - pre-test) | Control group | 13 | -1.8165 | 0.0648 |
| Experimental group | 13 | |||
| Life satisfaction (Post-test - Pre-test) | Control group | 13 | 3.9482 | 0.0000*** |
| Experimental group | 13 | |||
Figure 5 shows the effect of diagnostic mean data statistics, the figure 1-13 are know yourself, like yourself, learning motivation, regulating emotions, understanding family, do not know how to love, shy to talk about sex, love of life, psychological crisis, communicate with others, keep secrets, willing to fit in, overall satisfaction, according to the degree of the score range of 0~10 points. Using knowledge mapping before and after the mental health teaching, the scores of the assessment items have obvious changes, before and after comparisons generally increase by 2-3 points, especially the satisfaction scores for themselves, indicating that the mental health teaching has improved the mental health literacy of the students. they become more aware of acceptance of themselves, have motivation to study, can manage their emotions, be grateful to their families, know how to love, look at sex reasonably, cherish life, learn to intervene in crises, and are able to communicate with others, be happy to share with others, be willing to share with others, be willing to cooperate with others, be willing to communicate with people, be happy to share with people, be willing to cooperate with people, and be willing to cooperate with others. They can communicate well with others, enjoy sharing and making friends, and are generally satisfied with themselves.
Effect self-evaluation data statistics
Schools should strengthen the organization and management of mental health education and establish a three-level management system at the school-institute-class level. Among them, the school level is responsible for formulating policies, planning and management: schools should formulate policies related to mental health education, and clarify the status, goals and implementation methods of mental health education. These policies should be formulated based on the relevant regulations and guiding documents of the education department and in conjunction with the actual situation of the school. They should also plan mental health education from a global perspective to ensure its coordination with the overall educational work of the school, and they need to formulate the relevant management regulations of mental health education to ensure the reasonable distribution and effective utilization of educational resources. The faculty level is responsible for the implementation of university-level policies, integrating the policies with the specific conditions of their faculties and departments, formulating implementation plans and timetables, and organizing various forms of mental health education activities, such as lectures, workshops, and mental health weeks. These activities should have as their main theme to enhance students’ awareness and skills in mental health. The class level is responsible for the concrete implementation, which includes activities such as conducting thematic class meetings, group discussions and individual counseling, as well as keeping an eye on the mental health status of students and identifying and solving potential problems in a timely manner. Class teachers or counselors should pay attention to students’ mental health status through observation and communication, and provide timely help and support to students in need. At the same time, the school should establish an early warning mechanism for mental health problems, and discover possible problems in time through regular assessment of students’ mental health. In response to emergencies, schools should develop contingency plans to ensure that they can respond quickly and effectively.
It is very necessary to organize the preparation of model teaching materials for mental health education applicable to university students. First of all, in preparing the teaching materials, we need to ensure that the teaching content is scientific and standardized. This requires not only drawing on advanced theories and practical experiences of mental health education at home and abroad, but also taking into account the actual situation and characteristics of our students, we need to develop teaching contents suitable for their age and background. Secondly, we can enhance students’ participation and learning effect through innovative teaching methods. A combination of online and offline, case teaching, situational experience, behavioral training and many other forms can be introduced into mental health education.
Colleges and universities should focus on training and upgrading the quality of teachers, especially professional teachers engaged in mental health education. This is because teachers, especially professional teachers, play a very important role in guiding students in their growth process. The words and teachings of professional teachers, as well as what they teach, can often influence students’ worldview, outlook on life and values.
Incorporate mental health education courses into the personnel training program to ensure that students receive comprehensive and systematic mental health education during their college years Such courses exist in the form of compulsory and elective courses, and the compulsory courses can include the development of basic theoretical knowledge and basic skills, which can help students to understand the basic concepts and theories of mental health, as well as the ability to cope with common psychological problems.
This paper summarizes the theory of cognitive behavior, constructs a course knowledge mapping model, and applies the model to college students’ mental health education. Based on MinsCourse, combined with the knowledge mapping model, we obtain students’ cognitive behavior data and make cluster analysis of cognitive behavior data, the experimental results of this paper are as follows:
After clustering analysis, the samples are divided into 5 categories, in which the category with the largest sample size accounts for 27.36% and the smallest category accounts for 10.82%. The 4 categories of students with the highest percentage of students have the highest variable characteristics of cognitive dissonance, emotional exhaustion, and de-personalization, which are 34.195, 3.745, and 3.766, respectively, in addition to low achievement. After using the pre- and post-test experiments to analyze the knowledge map of college students’ mental health teaching curriculum model designed for mental health education, the mean value of students’ achievement after learning was higher than that before they had not learned, and the average scores of the pre- and post-tests were 60.92 and 83.93, respectively. it shows that the model effectively achieves the purpose of promoting learning by assessment and improves the students’ mastery level of knowledge. Diagnosis of mental health cognitive behavior of college students, using knowledge mapping of mental health before and after the teaching, the score of the assessment items have obvious changes, before and after the comparison of the general increase of 2-3 points, the students are generally more satisfied with their own after the education of mental health courses.