The construction of the integrated system of ideological and political education and curriculum system based on big data

The United States is the first country in the world to cultivate MPH, which is used to train public health talents for the United States. The Master of Public Health (MPH) is an internationally recognized mainstream degree in the field of public health. It aims to train high-quality, complex and application-oriented public health professionals for public health departments, including health administrative departments, disease control institutions, medical institutions at all levels and communities [1]. The cultivation of master of public health requires broad knowledge and solid foundation, good interpersonal communication, ability to find problems, analyze problems, and solve practical problems. The curriculum is an important part of the cultivation model of master of public health [2]. Therefore, it is of great significance to compare the curriculum of Master of Public Health in China and the United States to strengthen the training of applied public health talents and solve the problem of lack of applied public health talents in China.

Since the 1950s, China's public health education in colleges and universities has mainly followed the model of the Soviet Union, and has not changed substantially until now. The theory and practice of public health education are seriously disjointed, and the communication and contact channels between teachers in colleges and universities and disease prevention and control systems at home and abroad are not smooth, thus there are obstacles between theory and practice [3]. It is understood that at that time, less than ten colleges and universities carried out preventive medicine education. Up to now, more than 60 colleges throughout the country have undergraduate education in public health and preventive medicine, with an annual enrollment scale of more than 6000 people. In terms of number, the absolute number is relatively large, but if the average population is 1.3 billion, it is very scarce. After the 1980s, the scale of public health postgraduate education continued to expand, mainly in two aspects, one is a master's degree in science, the other is a doctor's degree in science, focusing on scientific research. Since the pilot program of Master of Public Health was started in 2002 in China [4], there have been many colleges and universities in the country carrying out postgraduate education for master of public health [5].

The unsound public health system does great harm and costs a lot. In addition to harming the national health, it can also bring people panic, disorder and social instability. For example, the outbreak and prevalence of SARS in 2003 caused people to question the government's governance ability and credibility. The national characteristics of a large population, social transformation, new industrialization, urbanization and globalization make China face more public health challenges. Therefore, building a first-class public health system is the only way to achieve the health of all people, and also the cornerstone to ensure economic development and social stability. In 2013, China's orderly response to avian influenza was praised as "exemplary" by the World Health Organization (WHO), which is a good example. To build a first-class public health system, there are a series of problems that need to be solved in theory and practice, such as: no consensus on what is public health/public health system; It is not clear how many public health problems a country (region) government faces; What kind of first-class system should be built is not conclusive; How to evaluate whether the system is first-class lacks scientific methods; The gap between China's iconic cities and first-class cities is still unknown[6].

With the support of big data technology, education evaluation is no longer used to support the decision-making needs of education management departments and educational institutions, but can serve all groups or individuals concerned about education and participating in education. The pertinence and effectiveness of ideological and political education can be improved by using education big data to analyze students' learning needs[7-10].

Research on big data. Compared with domestic research, foreign research on big data started earlier, forming many representative and valuable research results. In the 1960s, the concept of "data science" was first proposed. In the 1980s, Alvin Toffler praised in his book that "big data is the colorful movement of the third wave[11]".In 2008, Nature published a research article [12] entitled "Big Data: Science in the Petabyte Era", and launched a special issue of "Big Data", which specializes in the application of big data in the Internet, finance, biomedicine and other fields. The word "Big Data", that is, "big data", has become widely popular. In 2012, American scholar Steve Lohr published the article "The Age of Big Data", declaring that "the era of big data has come [13]". Since 2012, big data has attracted extensive attention from all walks of life, and has begun to attract the attention of leaders of all countries, which has subsequently become a hot spot in academic research.

In the big data environment, the ideological and political education is integrated into the teaching model of the public health curriculum system. Through in-depth exploration of the ideological and political elements of each curriculum, the integration of implicit education and explicit education is promoted to achieve an education model for all personnel, all directions and all courses [14-17]. Medical colleges and universities should not only cultivate health professionals with professional medical skills, but also strengthen the cultivation of medical students' humanistic quality, professional ethics, and ideological and political quality [18-19], all teachers are mobilized to participate in ideological and political education, and moral education elements are integrated into each medical course, so that classroom teaching can become the implementation of ideological and political education The main channel for cultivating people by virtue [20].

2

Public health system in the new era

2.1

Public health system

What is the public health system? There are different opinions and lack of universally accepted definition. The project team believes that the definition of "public health system" should not only follow the method of defining "public health", but also be subject to the upper concept "system" as a sub item of "system". First of all, the meta analysis was used to analyze 103 representative definitions of "systems" in five categories, and it was clear that their commonness included three necessary components: common goals, constituent elements, and interrelation. Secondly, based on bibliometric analysis, 11 classical connotations in the definition of public health system (such as "providing necessary public health services" and "coordinating with organized departments") are obtained to ensure that the characteristics of public health are reflected. Third, following the accepted methods in logic and 11 rounds of multiple argumentation, the project team's definition of public health system was formed, and the acceptance of all parties was 99.1%.

The public health system is a three-dimensional structure. It is an organic whole of a country (region), which is dominated by the government for public health, with relevant departments, professional institutions, social organizations, etc. performing their respective responsibilities, cooperating and linking, and comprehensively using legal regulation, organizational guarantee, management mechanism, resource allocation, technical support and other measures to provide appropriate public health services to the whole society. It is the responsibility of the government to maintain the effective operation of the public health system.

2.2

First class public health system

What should a first-class public health system look like? At present, there is no clear definition and elaboration at home and abroad. The project team first used boundary analysis, bibliometric analysis, etc., followed the operation rules of the macro model of the health system, and made clear the eight elements of the public health system through multiple demonstrations, namely, the theoretical framework, including the degree of attention to public needs, the degree of grasping public needs, the degree of perfection of the organizational system, the degree of soundness of functional services, the degree of suitability of resource allocation, the degree of perfection of management and operation Control the influence of natural factors and the support of social environment. Secondly, around the theoretical framework, using "submodel concept/dimension" logic deduction, meta analysis, comparative research and eight rounds of multiple argumentation, we have formed eight elements and 63 orientations that should be covered by a first-class public health system, and the acceptance degree of all parties is 88.8%~93.9%. Taking the improvement of the organizational system as an example, a first-class system should have complete subsystems, widely cover public health needs, and be able to focus on, respond to, and meet key needs to the greatest extent. The internal organizational structure of the subsystem is complete, including different levels of government and relevant departments, professional institutions, other organizations, etc. A first-class system should also have an authoritative unified coordination organization, which can play an effective role in coordinating different subsystems, relevant departments and professional institutions by means of planning, administration, supervision, guidance, etc.

3

Application of UMU interactive platform in public health curriculum system under big data environment

In recent years, big data education has become the focus of teaching reform [21]. Internet+education, with questionnaire stars, Backboard, WeChat and other carriers, has increasingly become the forefront of the reform of education and teaching methods. Its core connotation is to facilitate students' learning and fully mobilize their enthusiasm for learning, so as to improve teaching and learning efficiency [22-23]. The public health curriculum system is very important for training medical students. It is characterized by multi-disciplinary intersection, large capacity, but easy to understand. The traditional teaching content of this course is complicated and boring, with poor participation. The UMU platform is a convenient and applicable online education platform. Its core teaching resources are micro courses, which can be interspersed with various forms of interactive links to increase students' interest in learning, and can well assist theoretical teaching. Therefore, UMU platform is used to assist the teaching of public health curriculum system and explore its impact on the teaching effect, so as to promote the reform of teaching methods and improve the teaching quality.

3.1

Research objects and methods

98 students from experimental class 1 and experimental class 2 of a medical college were selected as research subjects. The UMU platform is applied to the production, development and evaluation of online courses. The changes of students' motivational beliefs and autonomous learning strategies were evaluated with the Self access Learning Scale. The questionnaire consists of five modules, in which motivational beliefs are composed of self-efficacy, intrinsic value and test anxiety; Autonomous learning strategies consist of the use of cognitive strategies and self-regulation [24]. 98 questionnaires of self-regulated learning scale were distributed before and after class, and 92 were valid for both times.

3.2

Research design and result statistics

Complete the upload of UMU courses 2 weeks before the class starts, including five compulsory courses, two elective courses and theoretical course courseware. Teachers carry out online teaching activities on the platform, such as opening learning groups, sharing basic curriculum knowledge in micro classes, expanding videos, discussions and case studies. All course sections have the same basic sub items, teachers' approval bonus items, standard learning bonus items, excellent learning bonus items and active learning bonus items. The cumulative and average points of the courses can reflect the students' activeness in the learning of this online course. Before and after the class, the students' motivational beliefs and autonomous learning strategies were investigated by using the autonomous learning ability questionnaire. At the end of the course, apply the teaching effect feedback questionnaire to investigate their attitudes, opinions and experiences towards this online course, and conduct a knowledge test with a full score of 100 two weeks after the course. SPSS17.0 was used for data statistical analysis, and the difference was considered statistically significant when P<0.05.

3.3

Research results

The completion rate of each summary of compulsory courses is more than 80%, while the completion rate of elective courses is only 10.2% and 3.1%. The impact of using UMU platform on students' autonomous learning ability can be seen from Table 1 and Fig. 1. After a semester of learning on the UMU network teaching platform, students' self-efficacy, intrinsic value and self-regulation scores have increased but no significant change, while test anxiety has significantly decreased, and the average score of cognitive strategies has significantly increased. The impact of using UMU platform on students' learning effect is shown in Table 2. Students who pass the final evaluation have higher activity on the network platform than those who fail.

Table 1.

Corresponding Relation and Coefficient of Number and Name

Number	Name	t	P
1	Self energy efficiency	-1.32	0.06
2	intrinsic value	-1.3	0.21
3	Examination anxiety	4.67	0.02
4	cognitive strategy	-4.32	0.04
5	Self-regulation	2.08	0.06

Table 2.

Comparison of average scores of UMU courses for students with different test scores

Group	List	Course average points
Pass	65	79.2
Fail	32	65.1
t		3.22
P		0.02

The effect feedback of using UMU platform for teaching is shown in the figure: the main reason why students can't learn all the content is that they don't have time (39.1%), followed by insufficient attention (23.7%), laziness (15.5%), and other reasons (21.7%). 94.6% of the students like to use UMU to learn this course, 95.6% of the students think that UMU is helpful for learning this course, and 88.9% of the students are able to adapt to independent learning on the network.

3.4

Result analysis

With the development of big data technology, network platform assisted teaching is increasingly welcomed by teachers and students [25]. The above research shows that the completion rate of compulsory courses is high, while the completion rate of elective courses is low, indicating that students' online learning is affected by the final assessment form. Teachers should take advantage of the internal driving force characteristics of students' self-study at the current stage, which mainly focuses on assessment, to appropriately improve the proportion of online courses in the final assessment results, and gradually guide students to develop the habit of using the network platform to obtain learning resources. This UMU online course consists of three parts: the basic knowledge review is a 10 minute audio based on nine pictures, which is the sorting, integration and fine processing of the basic knowledge of the theoretical course; Expand the video so that students can understand the context and new progress of this course; Case discussion is convenient for students to apply theoretical knowledge in practice. This research shows that the average score of the passing students on the platform is higher than that of the failing students, indicating that the more active the students are on the platform, the better their basic knowledge will be. It is suggested that teachers should make every effort to let all students participate in the learning of online courses in order to help them master the basic contents of the courses. Feedback on platform teaching effect Most students prefer to use UMU learning platform to assist learning, but the application of network platform should fully consider students' learning time, because nearly half of students think that lack of time is the main reason for failing to complete the learning task. Students' attitudes towards the curriculum and their own inertia should not be ignored, and individual interviews and encouragement should be conducted in due time. The most popular teaching resources are courseware, which suggests that teaching resources should be comprehensive and rich, and pay special attention to the importance of basic teaching resources. To sum up, the application of UMU platform assisted teaching can promote students to master basic theoretical knowledge, reduce their test anxiety, improve their cognitive strategies, and then improve their autonomous learning ability.

4

Comprehensive evaluation of public health system driven by big data

In the big data environment, to better integrate ideological and political education with the public health system, this section studies the comprehensive evaluation system of the public health system driven by big data this section studies the comprehensive evaluation system of the public health system driven by big data, which provides a reference for institutions with similar ideas in the future, and lays a foundation for the further integration of ideological and political education with public health services.

4.1

bibliometric analysis of public health system research

WOS is a widely used international academic information database. Therefore, WOS is widely used in bibliometric analysis of biomedical literature. The three most commonly used measurement methods are coauthor analysis, co citation analysis. VOSviewer can provide support for various types of bibliometric research, including coauthor analysis, co citation analysis, co occurrence analysis, etc. Use VOSviewer to make the first 100 author keywords for co occurrence network visualization. Fig. 2 shows the co occurrence network visualization of the top 100 high-frequency author keywords in medical big data research. The top 100 high-frequency author keywords in medical big data research form four clusters, which are identified by four colors: red, blue, yellow and green.

4.2

Risk information analysis model of public health emergencies in big data environment

Traditional information analysis methods are mainly classified according to the analysis object or means, and combined with big data analysis methods, they are introduced into the risk information analysis of public health emergencies. In combination with the occurrence process of public health emergencies, facing the risk prevention and control of public health emergencies, the risk information analysis model of public health emergencies in the big data environment is established, as shown in Fig.3 below.

The risk information analysis model of public health emergencies in the big data environment combines the characteristics of traditional information analysis methods that use literature and data for measurement, and the analysis method of big data. Through the integration of government departments to monitor the circulation of people in public places, it collects big data from self navigation software, taxi platforms, takeout platforms, comment software, social networking platforms, etc, Build a data driven intelligent platform for data sharing and co governance. Through the organic combination of the two methods, we can realize knowledge discovery, excavate the inherent development law of public health emergency risk, alleviate the problem that a single way is limited in the face of multi-source heterogeneous big data information format, and improve the problem of single document form and lack of representation from the perspective of dynamic information data.

4.3

Empirical study on the matching between supply and demand of public health services driven by big data

4.3.1

Data source of public health service supply and demand matching driven by big data

The data used in this section are obtained through expert consultation. Among the 30 experts, 10 are invited from all regions of Changchun City. The selected experts are representative and have a good understanding of the supply and demand of public health services in other regions of Jilin Province and the policies and documents related to public health services in all regions of Jilin Province driven by big data, For each indicator in the evaluation system of public health service supply and demand matching driven by big data, score one by one. The comment set is set as five grades: good, good, average, poor, and poor. The number is 1, 2, 3, 4, and 5 in turn. The evaluation result is r_ij(i = 1,2,3,⋯,13; j = 1,2,⋯,10), assuming that the evaluator's evaluation result on the evaluation indicator is $u_{i 1}^{k}, u_{i 2}^{k}, u_{i 3}^{k}, u_{i 4}^{k}, u_{i 5}^{k} (k = 1, 2, \dots, 10)$ , it is specified that there is only one 1 in $u_{i 1}^{k}, u_{i 2}^{k}, u_{i 3}^{k}, u_{i 4}^{k}, u_{i 5}^{k}$ , and the rest is 0. The membership matrix is determined according to R = (r_i1, r_i2, r_i3, r_i4, r_i5), i = 1,2⋯,13: (1) $r_{i j} = \sum_{k = 1}^{10} u_{i j}^{k}, i = 1, 2, \dots, 13; j = 1, 2, 3, 4, 5$

4.3.2

Evaluation and analysis of public health service supply and demand matching driven by big data

According to the domestic and foreign literature on big data, public health services, public health services, as well as the discussion of the expert team, the evaluation criteria for public health services were obtained, and the indicator evaluation criteria and indicator scoring table were determined, as shown in Fig. 4. Finally, the comprehensive evaluation score of the supply and demand matching of public health services driven by big data was obtained.

According to Fig. 4, for indicator C₁, three experts rated it as "1", six experts rated it as "2", and one expert rated it as "3". Therefore, in the five evaluation results of indicator C₁, the proportion of "2" is 3/10=0.3, the proportion of "good" is 6/10=0.6, the proportion of "3" is 1/10=0.1, and the other two items are all 0. If the membership matrix of indicators at the criterion level is set as R₁, R₂, R₃ and R₄, then there are: (2) $R_{1} = [\begin{matrix} 0.5 & 0.2 & 0.2 & 0 & 0 \\ 0 & 0 & 0.2 & 0 & 0.4 \\ 0.3 & 0.5 & 0 & 0.1 & 0 \\ 0 & 0 & 0.2 & 0.7 & 0.1 \end{matrix}]$ (3) $R_{2} = [\begin{matrix} 0 & 0 & 0.2 & 0.7 & 0 \\ 0.4 & 0.6 & 0.2 & 0 & 0 \\ 0.2 & 0.5 & 0.3 & 0 & 0 \\ 0.2 & 0.7 & 0 & 0.4 & 0 \end{matrix}]$ (4) $R_{3} = [\begin{matrix} 0 & 0.4 & 0.2 & 0 & 0 \\ 0 & 0 & 0.4 & 0.7 & 0 \end{matrix}]$ (5) $R_{4} = [\begin{matrix} 0.7 & 0.2 & 0.1 & 0 & 0 \\ 0.6 & 0.4 & 0 & 0 & 0 \\ 0.3 & 0.2 & 0 & 0 & 0 \end{matrix}]$

Weight vector W_B1: (6) $\begin{array}{l} W_{B 1} = [\begin{matrix} 0.2456 & 0.2142 & 0.2245 & 0.1866 \end{matrix}] \cdot [\begin{matrix} 0.5 & 0.2 & 0.2 & 0 & 0 \\ 0 & 0 & 0.2 & 0 & 0.4 \\ 0.3 & 0.5 & 0 & 0.1 & 0 \\ 0 & 0 & 0.2 & 0.7 & 0.1 \end{matrix}] \\ = [\begin{matrix} 0.19015 & 0.16137 & 0.12928 & 0.15307 & 0.10434 \end{matrix}] \end{array}$

Then the membership weights corresponding to W_B1 are 27.82%, 28.22%, 16.25%, 23.33% and 2.54% respectively. Similarly, we can get the corresponding membership degree of W_B1, W_B2, W_B3, W_B4.

Combine the above four membership degree vectors and combine with the weight of the criteria layer to obtain the comprehensive evaluation result of the matching evaluation of public health service supply and demand driven by big data, namely: (7) $\begin{array}{l} M = [\begin{matrix} 0.142 & 0.208 & 0.402 & 0.401 \end{matrix}] \cdot [\begin{matrix} 0.2782 & 0.2822 & 0.1625 & 0.2333 & 0.0254 \\ 0.1242 & 0.3012 & 0.2966 & 0.07668 & 0 \\ 0 & 0.54 & 0.3 & 0.1 & 0 \\ 0.504 & 0.208 & 0.075 & 0 & 0 \end{matrix}] \\ = [\begin{matrix} 0.2142 & 0.2066 & 0.3386 & 0.2054 & 0.0045 \end{matrix}] \end{array}$

According to the result M, the proportion of good, good, average, poor and poor matching between supply and demand of public health services is 26.55%, 36.44%, 22.77%, 11.02% and 0.41% respectively.

According to the above survey data, the final comprehensive score of Jilin Province public health service supply and demand matching evaluation is 89.12, and the evaluation grade is good. On the whole, the supply and demand matching of public health services in Jilin Province has progressed smoothly and achieved good results, which is worth learning from enterprises and institutions with similar ideas. Exploring the lack of matching between supply and demand of public health services driven by big data in Jilin Province, and deeply integrating big data with public health services are important issues facing us today.

5

Countermeasures for the integration of college ideological and political work and public health curriculum system in the age of big data

Li F [26] analyzed the mathematical and statistical models of political factors in the integration of ideological and political education and public mental health education. The model analysis results are shown in Fig. 5. The ideological and political factors can be included in the analysis model in various forms. The teaching of mental health in colleges and universities is shown in Figure 6. However, there are few studies on the integration of ideological and political education and public health curriculum system. In order to integrate ideological and political education into the public health curriculum system in the big data environment, this section introduces the corresponding measures.

5.1

Analysis of the current situation of smart ideological and political education in colleges and universities in the age of big data

With the help of big data technology, smart ideological and political education is taking an increasingly prominent position in the innovation and transformation of ideological and political education in colleges and universities, and is developing towards a wider application direction, relying on such advantages as accurate teaching, intelligent management, efficient interaction, personalized service, scientific evaluation, etc.

5.1.1

Research on the theme of smart politics in colleges and universities has gradually become a hot spot

Under the background that big data has become another subversive innovation technology following the Internet and has constantly penetrated into all walks of life, the ideological and political education model in colleges and universities has also pushed forward the transformation and upgrading of the traditional ideological and political education model, opening a new era of big intelligence. Smart thinking has gradually entered people's vision and become a development hotspot. The academic research achievements to a certain extent capture the research hotspots, and guide the practical exploration with theoretical achievements. Therefore, sorting out and grasping the academic research results can further reflect the current situation of the construction of the smart ideological and political model in colleges and universities. According to the statistical analysis results of HowNet, the number of papers published by the academic community has exceeded 10 since 2018, as shown in Fig. 7. The number of papers published in recent years has grown significantly, rapidly becoming an emerging hot research topic, and is expected to reach 45 in 2022. As shown in Fig. 8, from the survey results, 27.18% of the respondents believe that universities attach great importance to the construction of a smart ideological and political model, 44.75% of them attach great importance to it, 22.84% of them generally, and only 5.24% of them attach little importance to it or pay no attention to it. It can be seen that the smart ideological and political work in colleges and universities has aroused extensive concern and attention from schools, teachers and students, and is increasingly showing a new development trend. Based on the application of big data and the development of relevant practices, in the next few years, the construction of the smart ideological and political model in colleges and universities will not only be an important academic research growth point, but also the focus of the practical teaching application of ideological and political education in colleges and universities.

5.1.2

Initial completion of the smart ideological and political teaching platform in colleges and universities

As shown in Fig. 9, the platform construction is an important carrier for the implementation of smart thinking and politics, and also a realistic path for the real promotion and application of smart thinking and politics. For colleges and universities, the construction of a smart ideological and political platform should always be based on the needs of teachers and students, take smart learning as the core, intelligent management as the guarantee, and resource integration as the advantage, seize the opportunities of the times, and create a more diversified and dynamic smart ideological and political platform.

The development of the smart ideological and political model in colleges and universities needs to rely on appropriate carriers. Various smart teaching platforms have emerged, providing a strong carrier support. The intelligent teaching platform is not only the center for storing various teaching resources, but also the transit station for information exchange and data circulation, and also the analysis, research and judgment center for educators to make educational decisions. Nowadays, most of the ideological and political teachers in colleges and universities have introduced a smart teaching platform to assist teaching and improve teaching efficiency. Relying on intelligent technology, the smart ideological and political platform fully collects educators' information, intelligently assesses teaching effects, dynamically tracks students' thoughts and behaviors, focuses on early warning of "derailed" objects, and accurately pushes education content, making teaching activities convenient, efficient, and personalized, optimizing the entire education process, and greatly improving the teaching effect.

5.1.3

Build a collaborative education platform with multiple aggregation

Big data has built a collaborative education platform for college ideological and political work with multiple functions, real-time regulation, information sharing and human-computer interaction [27], as shown in Fig. 10. It not only promotes precision teaching and information management, but also optimizes, expands and upgrades the intelligent service function of ideological and political work. Based on the external field, practical logic and technical means provided by big data, building a smart service platform for college ideological and political work collaboration can further enhance the value of services in the process of educating people and improve the college education service system. In the age of big data, colleges and universities should tilt the service of ideological and political work to the direction of intelligence, informatization and networking, and meet the diversified service needs of college students accurately, timely and comprehensively. Colleges and universities should speed up the embedding of big data precise funding services in the platform, and improve the effectiveness of ideological and political work funding in colleges and universities. Use the data traces collected from big data such as college students' origin, family economy, student loan application, all-in-one card consumption, online shopping, takeout ordering, communication payment, book borrowing, work study program and so on to accurately identify poor students, and then calculate based on quantitative standards. According to their poverty level, causes of poverty, and funding schemes, classify and refine the funding objects, and establish a poor students' archive database, We will design customized funding systems and programs for poor students at different levels and with different needs, and coordinate with management, learning, teaching, and research posts to achieve the synergy of material support, ability support, and spiritual support.

5.2

Countermeasures for the integration of ideological and political education and public health curriculum system

5.2.1

Cultivate students' strong sense of social responsibility

The public health curriculum system mainly enables students to understand the concept, function and significance of public health. In order to enable students to better understand the important role of public health in disease prevention and control, we introduced a video in the teaching. This video is the first episode of the documentary "For the People's Health" - "Prevention of Disease", jointly produced by the Central Radio and Television (CCTV) and the National Health Commission. The experience of gradual control of traditional infectious diseases and endemic diseases, as well as the handling process of new infectious diseases, natural disasters and other public health emergencies were described, so that students could understand the process of gradual establishment and improvement of the public health system since the founding of the People's Republic of China. Through this video material, students can understand the working style of public health doctors, identify with the significance of future work, and have a sense of social responsibility to contribute to public health.

5.2.2

Guide students to understand the advantages of the socialist system

In part of the teaching of public health systems at home and abroad, teachers no longer simply explain the structure and working methods of public health systems in different countries, but integrate multiple documents. Students analyze and summarize the working concepts, working methods and roles of public health institutions in different countries through reading the documents. Through analysis and discussion, students will deeply feel the spirit of responsibility of the local containment measures in the early stage of the epidemic in China to fight for the epidemic time for other countries, and the humanitarian assistance to other countries in the later stage. They will gradually realize that the policies of the Chinese government in the domestic epidemic prevention and control, such as rapid response, mass prevention and mass control, and complete treatment reflect the concept of people-oriented and life first, stimulate students' patriotic enthusiasm, and enhance national self-confidence.

5.2.3

Improve professional identity

The last part of the course is an introduction to the discipline and methodology of public health. In the course of epidemic teaching, the role of epidemiology is expounded from the timely discovery of new infectious diseases by disease monitoring, the tracking of infectious sources by outbreak investigation, the screening of close contacts, and the prevention and control measures of infectious diseases in combination with epidemic hot spots. In combination with the "mystery" of the outbreak of an epidemic in a department store in Tianjin, this paper explains how epidemiological investigation can break through the mysteries and carefully analyze them, finally draw a clear chain of virus transmission, isolate close contacts in time, understand the importance of epidemiological investigation, and stimulate students' interest in learning. In combination with specific practical cases, the students will deeply realize that they need to integrate the knowledge of methodology with the knowledge of nutritional food hygiene and laboratory testing, find the source of disease in time, control the development of events, and also provide guidance for future prevention work. In order to enable students to better understand the actual work of professional departments, students are arranged to go to the district level disease prevention and control center for probation, and visit the health laboratory to increase perceptual knowledge. Through case analysis and field visits, students realized the importance of learning theoretical knowledge at school, stimulated learning motivation, and improved their professional identity and pride.

6

Conclusion

In the era of big data, it is very necessary to integrate ideological and political education into the public health curriculum system. This paper adopts the methods of questionnaire survey and data analysis, applies the UMU platform to the teaching and evaluation of online courses, and studies the comprehensive evaluation of the public health system driven by big data with actual cases. Finally, the following conclusions are drawn:

1) The completion rate of online courses included in the final assessment is higher, accounting for more than 80%, while the completion rate of courses not included in the final assessment is less than 20%. Online courses can reduce students' test anxiety (P<0.01) and improve their cognitive strategies (P<0.01). The application of UMU platform can facilitate students' learning, improve their independent learning ability, and achieve good teaching results.

2) On the whole, the comprehensive evaluation system of public health system driven by big data is relatively reliable, which is worth learning from enterprises and institutions with similar ideas. However, to explore the lack of matching supply and demand of public health services driven by big data, it is the future research direction to deeply integrate ideological and political education with public health services in the big data environment.

3) At present, the research on the theme of smart ideological and political education in colleges and universities has gradually become a hot spot, and the smart ideological and political education platform in colleges and universities has also been initially built. To integrate ideological and political education with the public health curriculum system, we should strengthen the construction of curriculum ideological and political education resources, guide students to understand the advantages of the socialist system, and enhance their professional identity.

Sprache:: Englisch

Zeitrahmen der Veröffentlichung:: 1 Hefte pro Jahr
Fachgebiete der Zeitschrift:: Biologie, Biologie, andere, Mathematik, Angewandte Mathematik, Mathematik, Allgemeines, Physik, Physik, andere

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The construction of the integrated system of ideological and political education and curriculum system based on big data

Yingkun Yu

Li Yuan

Online veröffentlicht: 27. Feb. 2025

Eingereicht: 20. Okt. 2024

Akzeptiert: 30. Jan. 2025

DOI: https://doi.org/10.2478/amns-2025-0122

SchlüsselwörterBig data, ideological and political education, curriculum system of public health, questionnaire investigation, Data analysis

© 2025 Yingkun Yu et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Schlüsselwörter
Big data, ideological and political education, curriculum system of public health, questionnaire investigation, Data analysis