A. The need and purpose of research
□While emphasizing changes in education paradigm for future generations that will lead the 4th Industrial Revolution, Korea is preparing a national response to the continuing hyper-low birth rate.
□There is a need for a new policy approach and research direction for the birth and upbringing of our society based on extremely low birth rates, together with the rapidly changing environment of the 4th Industrial Revolution.
□In this study, we are looking for ways to supplement the limitations of existing methodologies through analysis using big data, and how factors related to low birth rates interact in public opinion participants' cognitive structures and link the factors that can activate the quality of life.
□The purpose of this study is to find related issues and challenges through big data analysis on childbirth and childrearing during the 4th Industrial Revolution period, and to build a predictive model for low birth policy resolution to lead future child care policies and contribute to social awareness.
B. Contents of research
□Collecting data and establishing a classification system for social big data
□Development of social big data analysis and predictive model
□Review and Suggestion of the reflux system based on the predictive model
□Discover new policy issues and challenges based on big data analytics
C. Research method
□Research on literature
-Reviewing prior research at home and abroad related to the 4th Industrial Revolution
-Reviewing social big data research trends, research methods, and statistical prior research
-Collecting domestic and international research data on birth and child care
□ Social big data analytics
-Conduct trend analysis, keyword analysis, and correlation network analysis after collecting data on the fourth industrial revolution uploaded online, low birth, birth, child care, early childhood edcuation, child rearing
□ Big Data Analysis
- Derive predictive models
- Quantification of key factors through machine running
□ Expert survey
-Conduct a Delphi survey to identify the predictors required to develop a predictive model
□ Expert Advisory Council and Meeting
-Conducts a meeting of statistical and big data experts to build and utilize Big Data linkages
-Hold a meeting of experts to elicit factors related to childbirth and childcare
D. Limitations of Research
□In this study, the scope of the child care policy was limited to the low birth rate. Thus, kindergarten and childcare center policies were less suggested, and although there were many advantages and disadvantages in terms of policies on birth, child care.
□In this study, the first and second data mining processes were reflected in the results of surveys of big data experts, academia and experts, which focused on the categories of education, genders, jobs, labor and housing, which are the major impact variables that affect low birth rates. Limitations existed at points differentiated from other studies, for example, in the absence of income or other categories.
□As this research focused on online big data, there are limitations to generalizing the results of the policy. To compensate for this, the analysis results of big data need to be verified or compared by profiling the actual numerical variables on the birth rate in the future.
□Although the term '4th industrial revolution' itself appeared relatively rapidly after 2016, there is a limitation of the study in that analysis was somewhat insufficient in terms of the relationship with the parenting policy due to the ambiguity of the concept itself.
2.Analysis of Big Data on Child Care Policy in the Fourth Industrial Revolution
A. Flow of Fourth Industrial Revolution and Child Care Policy
□Government policy regarding the Fourth Industrial Revolution
-The 4th Industrial Revolution Committee under the direct presidential office confirmed the 'I-KOREA 4.0 Plan for the 4th Industrial Revolution' (I-KOREA 4.0) in 2017 and announced it (the 4th Industrial Revolution Committee, the joint project of related ministries, 2017b). Through the 4th Industrial Revolution response plan, the government emphasized that it intended to implement the 4th Industrial Revolution, which everyone participates in and enjoys, and that 'people-centered' is implicit in the principle of the 4th Industrial Revolution response plan (Committee for Industrial Revolution, Jointly, 2017a).
□ Government policy regarding low birth rate
-Establish a basic plan for low birth rate (from 1st to 3rd)
-The 3rd basic plan for a low-birth and aged society is aimed at a happy society with the vision of implementing a happy sustainable development society for all generations.
-As part of its strategy, we are strengthening youth employment and housing policies, realizing social responsibility for the birth of the increase of ovulation, expanding customized care and education reform, and eliminating the harmony and blind spot of family.
B.Pre-research on 4th Industrial Revolution and Child Care Policy
□A Study on the Fourth Industrial Revolution and Early Childhood Education
-The 4th Industrial Revolution is linked to issues such as low growth, low birth rate, education process, industry-academic cooperation, new industries, labor reform, basic income, and small businesses (Hong Jung-woo, Moon Hye-jung, 2017).
-Academic interest is growing in the transformation of the education paradigm and the creation of new talent awards due to the 4th Industrial Revolution.
□ A Study on Big Data
-State-run research on education and child care suggests a method for building and applying a theoretical foundation for utilizing big data, or forecast or derive specific results through direct analysis of big data.
-Construct algorithms that predict specific events or phenomena by utilizing big data and develop relevant models.
□ Study on childbirth and child rearing
-State-run studies on birth and child care focus primarily on overcoming low birth rates.
C.Social big data analysis on child care policy during the 4th Industrial Revolution (2008-2017)
□ Construction of data marts
◦Retrieving an existing prior study and creating a classification system that categorizes factors related to birth and child care
-Collect research reports and academic articles found under keywords such as '(low) birth rate', 'fertility' and 'fertility' and search for press releases and research related to keywords such as '4th industrial revolution’
-Construct the classification system by selecting and extracting variables that correspond to each classification criteria after finalizing the primary classification system for '(low) birth', 'rearing', '4th Industrial Revolution', 'infant education' and 'rearing'.
□ Overview of the collection data
-Collect 10,675,342 items uploaded on the Internet search portal Naver's blog, cafe, news articles, etc. using five keywords extracted from the Internet search portal Naver after collecting and reviewing social data collection keywords and channel domestic and overseas prior research.
-Fire control extraction algorithm Extracts data that have been refined to 1,000th place in the fire control, and analyzes the type and categorization work.
-Data extraction and refining process Select whether extraction keywords should be included for data that has gone through the primary data purification process, then select data that is related to five keywords and inject it into the final analysis.
□Extract analysis subject keywords and categorize categories
-In order to improve the explaining power of birth and child-rearing phenomena, five topic keywords - 'education environment', 'gender culture', 'residential problem', 'labor environment' - are extracted additionally and analyzed for big data on social media using the data sets belonging to the subject keywords.
□ Social big data analytics
- housing problem
∙After 2016, the importance of 'low birth', 'government', 'policy', 'marriage', 'job', 'life' and 'working class' was high, and the housing problem was related to the 'birth/rearing' category at 20.8%.
∙A word cloud analysis of keywords between 2018 and 2017 showed that 'low birth', 'government', 'supply', 'home', 'new marriage', and 'birth' are related to housing problems, and 'low birth', 'government', 'new marriage', and 'home' are the most important factors in the analysis of the network
- Educational environment
∙According to the fire control trend analysis, 'education' shows a gradual decrease in the fire control ranking and frequency, while 'low birth rates' and 'government'/policy fire control ('social', 'support', 'welfare', 'policy' and 'President') are high in 2017, indicating that the recent educational environment is related to low birth problems and the government's policy and welfare aspects (4.6% of households, 4).
∙Similar to the analysis of the word cloud and relationship network, 'Low birth', 'support', 'government', 'policy', 'I' and 'birth' are higher than 'education', and 'low birth' is a big part of the correlation network analysis.
- Employment problem
∙According to the analysis of trends in fire control, job-related, employment/labor, employment forms and others were ranked high until 2010, while 'low birth rates' increased since 2015 and even when categories were classified, 'birth/care' accounted for the highest percentage of all topics, showing that anxiety about employment and employment environment are closely related to child care.
∙According to word cloud analysis, 'job', 'low birth', 'support', 'government', 'job creation', ' youth', 'female' were found to be the highest frequency, and 'occupation' was also the most important factor in the correlation network analysis.
- Labor environment
∙Analysis of trends in fire control and word cloud revealed that 'children's vacation' is high over the past 10 years. Even when topic language is categorized by category, 'holiday/holiday' is the highest rate at 43.2%. After 2016, topics such as 'Men, 'Daddy', 'Children' and 'Japanese-family compatibility' topped the list, showing that men's interest in and participation in childrearing and work-life balance began to be valued.
∙Results of word cloud and network analysis show that 'parents', 'workers', 'children', 'children's', 'rearing leave' and 'payment' are closely related to the birth and upbringing of the caregiver workers
- Gender culture
∙As a result of analysis of trends in fire control, topics such as 'sex discrimination', 'sex equality', and 'fertility' have been consistently ranked at the top over the last 10 years, and 'low birth rate' and 'birth' have been ranked at the top since 2016. In the fire control category, 'positive equality' is the highest with 26.6 percent, and 'birth/fertilization' is also the same level with 22.6 percent, which suggests that awareness of gender inequality and disadvantages are linked to childbirth.
∙Results of word cloud and network analysis show that 'female', 'low birth', 'birth', 'male', 'marriage', '(sex) discrimination' are high, and 'female' is linked to 'fertility', 'housewife' and 'discrimination' to indicate that the burden of women's childcare, family, and discrimination in the labor environment is affecting the birth of women.
D.Big Data Forecasting Model for Low Birth in the Fourth Industrial Revolution
□Verification and extraction of key variables through machine learning
-Derive variables by using machine learning techniques for data sets extracted through morpho analysis and category classification
-As a result of applying the random forest model, three of the five keywords - 'Gender culture', 'Housing problem', 'Work environment' and a combination of 'Education environment 3 housing problem' and 'Education environment 3 labor environment' appear as independent variables that describe the birth environment
□Establishment of predictive statistical models and verification through simulation
◦Application of data processing and analysis results in conjunction with predictive models
◦Predicts and interprets changes in birthrates by applying four categories of topics: housing, housing × education, employment and labor environment to the predictive model.
◦If a predictive model has a response variable with a probability and a probability rate and an annual employment rate and an income per household is added to the explanatory variable, only 'residential x-education' and annual employment rate show significance in the model
◦Using the Poisson automatic regression model based on 'residential × education' and annual employment rate, it is estimated that premature birth rate will stagnate at a lower level in 2019-2020 after a slight rebound. This seems to be due to the recent slight improvement in employment rate.
◦From the big data prediction results, we can see that measures should be prioritized for various issues arising from the interaction of housing and education problems rather than simply increasing income or employment rate.
3.Issues and Tasks of Child Care Policy in the Fourth Industrial Revolution
□Issues of parenting policies focusing on improving the residential-education environment
◦Understanding the conflict structure by analyzing key words of residential-education environment
-As a result of semantic network analysis on 'housing problem' and 'housing problem 3 education environment' categories, the main focus is on the need for government policy support around 'low birth rate' in the middle
-Meaning network analysis shows that 'unsatisfaction' and 'household debt' in the workplace and housing space are typically derived from keywords with high parameter values, and that 'unsafe' keywords are linked to 'young' and 'new couples' meaning that giving up marriage due to unstable environment can be a major factor in low birth and conflict environments.
-(Low) income is directly linked to 'household debt', which indicates that household debt problems associated with housing include income problems and housing issues include jobs.
-Structured semantic clustering, 'female' clusters have issues such as 'residential', 'job position' and 'adult' associated 'hardening' directly with 'private' and 'cooperative’
-For a 'child' cluster, it includes related terms such as 'child', 'burden', 'economy', 'education' and 'private education' and refers directly to 'housekeeping costs' and 'private education costs' similar to machine learning outcomes
◦Relationship between the 4th Industrial Revolution and the Residential × educational environment: Smart City
-Based on Google Trend, domestic "smart city" search trends from 2016 to 2018, related searches are increasing gradually
-As a result of semantic network analysis of related documents that include keywords for "smart city" over the past three years, 'game', 'technology' and 'business' are highlighted in terms of frequency, but 'platform' and 'development' are the main medium of interest on PBS
-If you look at the topical cluster network, discussions about 'Smart City' were linked to 'enterprise' and 'technology' topic clusters
-Discussions in Korean newspaper articles on education and childcare environment linked to smart cities are focused on the interests of companies. Based on foreign media text analysis during the period, the technology and services provided by companies are designed to influence the "life" of people and the functions expected to be different from the domestic media.
-In regard to the use of smart cities and future technologies, consideration should be given to the possible changes in the educational environment and the lives of people in cities with new technology environments.
-The importance of the physical education environment, such as school districts, can be weakened, and new AI-focused mobility can lead to mitigation of housing trends or regional de-collections.
-Need to re-evaluate the socio-economic potential of the Fourth Industrial Revolution on the reality of housing-education in Korea and draw a viable strategy.
□Issues of parenting policies centered on quality of life
◦Two Delphi surveys conducted for 40 experts on low birth and child care
-In the first survey, five factors (education, genders, housing, jobs, and labor) that were related to the low birth rate were investigated.
-Based on the results of the first survey, the importance of policy measures to overcome low birth rate by 5 factors is corrected.
◦After calculating the observed values, means, and standard deviation for determining the importance of each question and each question, calculate the coefficient of variation (CV) for comparing the relative scatter between the questions, and calculate the content validity ratio (CVR) for determining the validity of each question.
◦Results of the first Delphi survey
-According to the first Delphi survey, 92.5% (37) of experts agreed on whether the five factors (housing, education, jobs, and labor genders) account for the low birthrate, and among them, the 'job' problem was recognized as having the greatest influence on the low birthrate. The 'Gender × Labor' and 'Education × work × work' were the highest for factors where interaction between factors is likely to affect low birthrates.
◦Second Delphi survey results
-In the second Delphi survey, we looked at the priorities of importance of five factors. In the case of housing, we looked at 'stable housing', 'joint rental housing expansion', 'building a friendly urban environment' and 'education' problems, 'strengthening public education for preschool and children', 'increasing quality of preschool and children's homes', and 'extended childcare leave' on the day.Important options include gender, maternity leave and parental leave system, and guaranteeing continued employment of women.
□Possibility of building a smart-based social childcare community
◦Given the relationship with the Fourth Industrial Revolution, the synchronization of housing and education was important in big data, and these issues need to be considered in recent years.
-In contrast to overseas cases, discussions on smart cities in Korea are mainly focused on cutting-edge technology. There is a high possibility of inequality, mainly linked to keywords such as campus, apartment and Gangnam.
-There are arguments that when considering changes in housing and education, the problem of parenting should be considered by linking it to the quality of life. In other words, it is also persuasive to argue that technology, human beings, and well-being should be solved.
-Planning child-centric, play-oriented, and child-friendly smart cities and building a joint child-rearing system using state-of-the-art technology can be a good example.
-Through information and technology innovation, we can reduce child care costs by sharing child care information, space and toys, link social joint childcare and play groups, and create smart cities that improve the lives of residents.
◦As a teaching method for developing creative talents, learning effects can be improved by utilizing state-of-the-art equipment.
-Teaching methods, robot-based education, STEAM education, etc. are likely to be important based on core competencies for creative human resources development during the 4th industrial revolution.
4. Policy Suggestions
□ Direction of policy
◦Removing income and class gaps by securing publicity in housing-education
◦Improving the quality of life of the people by resolving the crisis factors in terms of housing/education/job/labor/gender
◦Securing an accessible digital and technical network
◦Preparing for the 4th industrial revolution by fostering creative core human resources and innovating education for the future society
□ Policy challenge
◦Addressing the social crisis factors: housing, education/care, jobs, labor, and Gender
-Significant expansion of public rental housing to reduce educational gap
-Reinforcing public character of education and education by establishing a seamless integrated child education, child care, and school care system
-Active support for smart work/television/living in public offices, maternity and paternity leave
-Improving the flexible working system, quality and work culture without distinction
-Eliminates gender wage gaps and supports various family types
◦Response to future social changes: fostering creative key human resources, innovating future education
-Expanding diverse education courses where freedom of play and experimentation is alive
-Innovation in future education: Establishment of education innovation system for kindergartens, childcare centers, and schools
◦Technology and industrial innovation: establishment of smart childcare network
-Connection and integration of super-connected intelligent childcare information network
-Establish a joint child centered smart city network (playing, space, and toy sharing)
-Establishing a teleworking, smart working system
-Support for R&D related to smart, co-parenting, and networks
Table Of Contents
Ⅱ. 4차 산업혁명 시대 육아정책에 관한 빅데이터 분석
Ⅲ. 4차 산업혁명 시대 육아정책의 이슈와 과제
Ⅳ. 정책 제언