OECD DIGITAL EDUCATION OUTLOOK 2021 : pushing the frontiers with artificial intelligence, block... chain and robots.

Description

Editorial Table of Contents Executive Summary Chapter 1. Frontiers of smart education technology: Opportunities and challenges Smart data and digital technology in education Current frontiers of digitalisation in education Key opportunities Effectiveness Equity Efficiency Policy pointers Smart technologies as socio-technical systems Algorithm accuracy Designing for use Smart technology and data governance: transparency, fairness and ethics Infrastructure and public good Research and development Concluding remarks References Chapter 2. Artificial intelligence ineducation: Bringing it all together Introduction Smart education technologies: Definitions and context Educational technology Educational data Artificial intelligence and machine learning Artificial Intelligence in Education (AIED) Learning analytics The uses of artificial intelligence in classrooms and educational systems New educational technologies and approaches for the classroom New educational technologies and approaches for educational systems Future potentials References Part 1 Chapter 3. Personalisation of learning: Towards hybrid human-AI learning technologies Introduction Hybrid human-AI systems: specifying the role of teachers and technology Speculations for the future: the ultimate role of AI Detect: follow learners and track their environment Diagnosis: assessing learners’ current states Act: select appropriate actions Personalisation based on students’ knowledge Adjusting problems at the task level Adjusting feedback at the step level Adjusting units at the curriculum level Positioning current learning technologies in the levels of the automation model Personalisation of learning based on self-regulated learning Challenges for the future of personalisation of learning Recommendation 1. Govern ethics by design, transparency and data protection Recommendation 2. Improving learning technologies with public-private partnerships Recommendation 3. Engaging teachers and education professionals in R&D Notes References Chapter 4. Improving student engagementin and with digital learning technologies Introduction The importance of engagement Defining engagement Measuring engagement Traditional measurement approaches An automated digital measurement approach Examples of engagement measures with the advanced, automated, analyticapproach Improving engagement Proactive approaches Reactive approaches Reactive technologies in the classroom Conclusions and future directions References Chapter 5. Classroom analytics: Zooming out from a pupil to a classroom Introduction The vision: Classrooms as a digital system How did we get there? Classroom as the input Classroom as the output System functions Monitoring and intervention Data propagation Team formation Debriefing Timing transitions Teacher self-regulation Orchestration Perspectives Acknowledgements References Chapter 6. Serving students with special needs better: How digital technology can help Introduction Education, technology and special needs Some examples of learner-centred approaches to smart technologies The ECHOES environment Dysgraphia: diagnosis and beyond Visual impairment and interactive tactile graphics Looking to the future Holistic smart systems Smart systems for all Blending human and artificial intelligence Acknowledgements References Chapter 7. Social Robots as Educators Introduction Why robots as educators? The different teaching roles of robots Robots as telepresence devices for teaching and learning Robot effectiveness: age and learning domains Technical aspects of robots in education Attitudes of teachers Commercial offerings Future prospects References Part 2 Chapter 8. Learning analytics for school and system management Introduction Organisational benefits from learning analytics Three examples Case example 1: Adoption of learning analytics at the University of Wollongong, Australia Case example 2: Teachers’ Diagnostic Support System (TDSS) (Stuttgart, Germany) Case example 3: The LAPS project using machine-learning techniques for early student support at the Stuttgart Media University, Germany Learning analytics’ challenges for organisations Moving forward with learning analytics Conclusion References Chapter 9. Early warning systems and indicators of dropping out of upper secondary school: the emerging role of digital technologies Introduction Early Warning Systems and Indicators (EWS/EWI) Accurate predictors of dropping out Application of emerging digital technologies: pattern analytics and data science Dropout versus discharge Machine-learning predictors: decision trees and random forests Hierarchical Cluster Analysis (HCA) heat maps Issues of machine learning and data mining: generalisability, open access, and accuracy Conclusion and future directions The four A’s of early warning indicators: Accurate, Accessible, Actionable andAccountable. Future directions for early warning indicator research and practice: References Chapter 10. Game-based assessment for education Introduction Why game- or simulation-based assessment in education? Application of modern psychological theory to assessment The need for assessment to be better aligned with curriculum and instruction Increasing assessment coherence Measuring different constructs - “hard-to-measure” skills Measuring constructs differently - interaction and engagement How do we build game-based tests? Designing from the ground up Games for assessment vs. “gamification” Telemetry and the question of “stealth” How hard is this to do? How do costs compare to more traditional approaches? New psychometric methods – and challenges The promise of AI and machine learning Some examples of game-based assessment in education SimCityEDU: Pollution Challenge (GlassLab) Crisis in Space (ACTNext) PEEP – Project Education Ecosystem Placement (Imbellus) What is the long-term promise of this approach and what is necessary to get us there? References Chapter 11. Blockchain for Education: A New Credentialing Ecosystem Introduction Understanding blockchain technology A breakthrough in computer science Blockchains and distributed ledgers Decentralisation vs. centralisation Costs and limitations Open standards Benefits of blockchain for educational credentialing Eliminate records fraud Streamline and reduce the cost of records sharing and verification Return control of personal data to individuals and reduce institutional risk Summary Real-World implementations United States Canada European Union and United Kingdom Middle East and North Africa Latin America and the Caribbean Asia Pacific Driving change What can policymakers do? What can educational institutions do? Conclusion Annex 11.A. Appendix: Technical glossary Note References Authors