The future of PISA: perspectives for innovation Yuri Belfali Head of Early Childhood and Schools

Collaborative problem solving PISA 2015 Science Math Reading Collaborative problem solving Student Well-being Financial Literacy Optional Surveys

Trends in science performance 570 Student performance 550 Below Level 1 Level 1 Level 3 Level 4 Lev5 Level 2 530 510 OECD average 490 470 450

PISA 2018 Science Math Reading Global Competence Financial Literacy Optional Surveys

Responding to change, addressing the emerging areas of learning

The future of education and skills: Education 2030

PISA 2018 Global Competence Global Competence Take perspectives Skills Use evidence critically Analyse perspectives Adapt communication and behaviour Evaluate actions and consequences Knowledge Knowledge of global issues Intercultural knowledge Values Valuing human dignity Valuing cultural diversity Attitudes Openness Respect Global-mindedness PISA 2018 Global Competence Examine issues Take perspectives Act for well-being and sustainability Interact across cultures Global Competence

Social and Emotional Skills Persistence (UK) Perseverance (NZL) Responsibility (KOR, NZL) Locus of control (US, UK, KOR) Extraversion (NOR) Sociability (NZL) Self-esteem (US, UK, CAN, SWI) Self-confidence (NOR)

For Life and careers… For Citizenship…. Fairness Resilience Integrity Courage Adaptability Creativity Curiosity Respect Integrity Initiative Entrepreneurship Leadership Self-awareness Empathy

Making PISA globally relevant

PISA 2015: 72 countries and economies OECD Partners

PISA and SDG Education target 4 Targets 1 2 3 4 5 6 7 a b c Indicator 4.1 Percentage of 15-year-old students performing at Level 3 or higher on the maths scale (PISA, 2012) 4.2 Enrolment rate in pre-primary education at age 5 (INES, 2014) 4.3 First-time tertiary entry rates (INES, 2014) 4.4 Percentage of 24-64 year-olds in Group 3 or 4 of skills proficiency and readiness to use ICT for problem solving (PIAAC, 2012/2015) 4.5 PISA inclusion index (PISA, 2012) 4.6 Percentage of adults performing at Level 3 or higher on the literacy scale (PIAAC, 2012/2015) 4.7 Percentage of students at level A, B or C in the environmental science performance index (PISA, 2006) 4.a Computers for educational purposes per student, ,mean index (PISA, 2012) 4.b Scholarships and student costs in donor countries (USD millions, difference between 2012 and 2014) 4.c Percentage of ISCED 2 teachers having completed teacher education or training programmes (TALIS, 2013)

Students’ proficiency in science

15-year-olds’ proficiency in science (out-of-school adjusted)

Enhancing PISA for Development Adjusting the PISA tests to better measure and discriminate at lower levels of proficiency More relevant contextual instruments Out-of-school youth in the assessment Tackle financial and technical challenges through partnerships with donors and peers and through capacity building and peer support Use of assessment results for policy dialogue and decision making through increased outreach to stakeholders, support for national analysis and reporting and sensitivity to local context

PISA for Development and scale-up INFORMED BY: PISA EXPERIENCE PARTICIPATING COUNTRIES OTHER ASSESSMENTS PISA PISA-D Outputs of PISA-D informing and enhancing PISA from 2021 cycle onwards

Raising the power of analysis: for policy making and for practices

Deep dive in data Possible insights from log-file analysis: How much time did students spend to solve this item? How many «experiments » did they set up (varying one thing at a time)? Did they press all the controls « at random », or according to a systematic plan? Did some students try to guess the right answer, without gaining first all the relevant information? (Source: Samuel Greiff)

Way forward Increase explanatory power: PISA and other research Continuously develop policy relevant questions: teacher quality, what works, education to address emerging issues (citizenship, sustainable development …) Different outcome areas Take advantage of technological advancement

Learning time and science performance Figure II.6.23

Factors associated with a higher science performance Figure II.7.2 Positive association with science performance Multilevel regression models of education systems, schools and students The z-scores for "all countries and economies" are generally lower because the uncertainty surrounding the relationships is significantly higher. No association: External evaluations exist at the school Index of school autonomy Index of shortage of education staff Pre-primary attendance School is located in a rural area

Enquiry-based teaching practices and science performance, OECD average

Innovate test mode and instruments Potential of computer-based test: adaptive testing Inclusion of students with special needs More timely leaner’s data? Tool for whom? : policy makers, teachers, school leaders, parents students…. Alibaba.com

Collaboration Stakeholders Researchers OECD

Fall 2016 Call for Proposals: Apply by 4 January 2017

Thank you! www.oecd.org/edu Yuri.Belfali@oecd.org Follow us on: @OECDEduSkills @EduSkills OECD @ EduSkills OECD