Society 5.0 and New Forms of Educational Inequality: The Case of - PowerPoint PPT Presentation

Department of Sociology and Nissan Institute of Japanese Studies Society 5.0 and New Forms of Educational Inequality: The Case of Japan Takehiko Kariya (takehiko.kariya@sociology.ox.ac.uk) Department of Sociology and Nissan Institute of Japanese Studies University of Oxford

Japan’s future shown in the policy seeking for ‘Society 5.0‘ ‘ We aim at creating a society where we can resolve various social challenges by incorporating the innovations of the fourth industrial revolution (e.g. IoT, big data, artificial intelligence (AI), robot, and the sharing economy) into every industry and social life. By doing so the society of the future will be one in which new values and services are created continuously, making people’s lives more conformable and sustainable. This is Society 5.0, a super-smart society. Japan will take the lead to realize this ahead of the rest of the world. Realizing Society 5.0: https://www.japan.go.jp/abenomics/_userdata/abenomics/pdf/society_5.0.pdf

Continued • Abundant accumulation of real data • Technology cultivated from “monozukuri” ‘By taking advantage of these unique factors, Japan will overcome social challenges such as a decrease in the productive-age population, aging of local communities and energy and environmental issues ahead of other nations. We will realize a vibrant economic society by improving productivity and creating new markets. By doing this Japan will play a key role in expanding the new Society 5.0 model to the world.’

Goals of education to realise Society 5.0 Skills commonly needed: • Ability to accurately interpret and respond to writing and information • Ability to engage in and apply scientific thinking and inquiry • Sensitivity and ability to discover and create value; curiosity and the inquisitiveness Human Resource Development for Society 5.0 http://www.mext.go.jp/b_menu/activity/detail/pdf2018/20180605_001.pdf

Human resources to lead a new society: • Human resources who discover and create leaps in knowledge that are the sources of technological innovation and creation of value • Human resources that create platforms that connect technological innovations to societal issues and create platforms • Human resources that can leverage and extend AI and data in various fields

At all stages of learning • Lack of self-guided, independent learning in collaboration with others while steadily mastering fundamental academic abilities • Thus provide a variety of learning opportunities and spaces to achieve “fair, individually optimized learning” • Here fairness and individualised learning are recommended, which are linked with ‘active learning’ in the new national curriculum

Goals for school education • Compulsory Education: Ensure that all children and students acquire fundamental academic abilities—e.g. basic reading comprehension, mathematical thinking, etc.—and information competency • Upper Secondary: Transcend the humanities/sciences divide

As a part of that: Attainment of information competency • Discussions will be initiated regarding the addition of “information” as a subject to be tested on the Common Test for University Admissions (from 2024). • Data science and statistics education will be strengthened across elementary, lower and upper secondary school. Teaching ‘programming’ from elementary school to high school

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Problems arose or to arise • Who can teach them effectively? • A ‘positive-list’ approach to education with under-resourced situations: Teaching jobs in school as ‘black’ jobs • Working conditions of teachers: Overloaded work in Japanese schools • Under under-resourced conditions, who will suffer the most in such reforms?

The Asahi Shimbun OECD 2018 Teaching and Learning International Survey; Asahi Shimbun June 20, 2019

Elementary school teacher • 'Their weekly work hours totaled 54.4, of which 5.2 hours were spent on general administrative work and 8.6 hours were used to prepare lessons. All three numbers were the highest among participating countries and regions.’ (Asahi, June 20, 2019) • How can these busy teachers teach ‘information’ ? • Furthermore, teaching English conveys a new burden to teachers, despite having no official certificate to teach English. Plus moral education as a subject will start in 2020 • Work-life balance of teaching professions does huge matter, resulting in a shortage of new teachers

Who suffers most in under-recourse conditions? この写真 の作成者 不明な作成者 は CC BY のライセンスを許諾されています

Regression analysis (OLS) for Internet Access per week Coerricient Sig. B S.E Beta Constant -1.034 0.588 0.079 Age in 2007 -0.035 0.009 -0.069 0.000 Female -0.709 0.106 -0.121 0.000 Year of education 0.288 0.035 0.183 0.000 Occupation: Professional 0.276 0.134 0.039 0.039 Occupation: Managerial 0.863 0.559 0.028 0.123 Size of company 4.221E-05 0.000 0.010 0.585 Father with HE degrees 0.161 0.141 0.024 0.251 Mother with HE degrees 0.283 0.219 0.025 0.196 Father professional- 0.196 0.144 0.027 0.173 managerial Household wealth at 15 0.187 0.068 0.052 0.006 JHS Grade at 15 0.185 0.051 0.075 0.000 N=2824; Panel survey ISS, Tokyo Univ. 2006

Regression analysis (OLS) for Internet Access per week (31-41 years old) Coerricient Sig. B S.E Beta Constant -0.986 1.162 0.396 Age in 2007 -0.012 0.026 -0.011 0.645 Female -0.788 0.145 -0.134 0.000 Year of education 0.241 0.048 0.157 0.000 Occupation: Professional 0.604 0.183 0.085 0.001 Occupation: Managerial 1.026 0.575 0.044 0.075 Size of company 0.000 0.000 0.026 0.308 Father with HE degrees 0.424 0.210 0.058 0.044 Mother with HE degrees -0.072 0.352 -0.005 0.838 Father professional- 0.201 0.205 0.026 0.327 managerial Household wealth at 15 0.166 0.096 0.043 0.085 JHS Grade at 15 0.109 0.071 0.043 0.125 N=1554; Panel survey ISS, Tokyo Univ.

Regression analysis (OLS) for Internet Access per week (20-30 years old) Coerricient Sig. B S.E Beta Constant -1.603 0.967 0.098 Age in 2007 -0.056 0.025 -0.061 0.024 Female -0.600 0.156 -0.104 0.000 Year of education 0.347 0.052 0.211 0.000 Occupation: Professional -0.113 0.196 -0.016 0.565 Occupation: Managerial 0.022 2.750 0.000 0.994 Size of company -3.513E-05 0.000 -0.008 0.764 Father with HE degrees -0.080 0.191 -0.013 0.673 Mother with HE degrees 0.575 0.281 0.060 0.041 Father professional- 0.175 0.204 0.025 0.391 managerial Household wealth at 15 0.221 0.096 0.064 0.021 JHS Grade at 15 0.267 0.072 0.111 0.000 N=1270; Panel survey ISS, Tokyo Univ.

A measurements of problem solving skills: ‘I am able to find solutions to difficulties and problems • that arise daily,’ ‘I think it is worth facing and tackling many of the • problems and difficulties that arise in life,’ • ‘ I can understand and predict the difficulties and problems that tend to occur in my daily life.’ • 7-point-Likert-scale for each

Regression analysis (OLS) for Problem solving skills (20-40 year old) Coerricient Sig. B S.E Beta Constant 10.464 0.683 0.000 Age in 2007 0.030 0.011 0.052 0.005 Female -0.218 0.123 -0.033 0.075 Year of education 0.075 0.041 0.042 0.065 Occupation: Professional 0.670 0.155 0.083 0.000 Occupation: Managerial 1.468 0.647 0.042 0.023 Size of company -1.174E-05 0.000 -0.002 0.896 Father with HE degrees 0.296 0.163 0.039 0.070 Mother with HE degrees -0.085 0.253 -0.007 0.737 Father professional- 0.126 0.167 0.015 0.450 managerial Household wealth at 15 0.354 0.079 0.086 0.000 JHS Grade at 15 0.460 0.059 0.164 0.000 N=2821; Panel survey ISS, Tokyo Univ.