OECD Digital Education Outlook 2021 Pushing the Frontiers with Artificial Intelligence, Blockchain and Robots

OECD Digital Education Outlook 2021 Pushing the Frontiers with Artificial Intelligence, Blockchain and Robots

How might digital technology and notably smart technologies based on artificial intelligence (AI), learning analytics, robotics, and others transform education? This book explores such question. It focuses on how smart technologies currently change education in the classroom and the management of ed...

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Detalles Bibliográficos
Autor principal: Organisation for Economic Co-operation and Development (author)
Autor Corporativo: Organisation for Economic Co-operation and Development, author, issuing body (author)
Formato: Libro electrónico
Idioma:Inglés
Publicado: Paris, France : OECD Publishing [2023]
Edición:First edition
Colección:OECD Digital Education Outlook
Materias:
Artificial intelligence > Data processing.
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009704612606719
Tabla de Contenidos:
  • Intro
  • Editorial
  • Acknowledgments
  • Executive Summary
  • Frontiers of smart education technology: Opportunities and challenges
  • Current frontiers of digitalisation in education
  • Key opportunities
  • Policy pointers
  • Concluding remarks
  • Artificial intelligence in education: Bringing it all together
  • Smart education technologies: Definitions and context
  • The uses of artificial intelligence in classrooms and educational systems
  • Future potentials
  • Personalisation of learning: Towards hybrid human-AI learning technologies
  • Hybrid human-AI systems: specifying the role of teachers and technology
  • Speculations for the future: the ultimate role of AI
  • Personalisation based on students' knowledge
  • Personalisation of learning based on self-regulated learning
  • Challenges for the future of personalisation of learning
  • Improving student engagement in and with digital learning technologies
  • The importance of engagement
  • Def ining engagement
  • Measuring engagement
  • Improving engagement
  • Conclusions and future directions
  • Classroom analytics: Zooming out from a pupil to a classroom
  • The vision: Classrooms as a digital system
  • How did we get there?
  • Classroom as the input
  • Classroom as the output
  • System functions
  • Perspectives
  • Serving students with special needs better: How digital technology can help
  • Education, technology and special needs
  • Some examples of learner-centred approaches to smart technologies
  • Looking to the future
  • Social Robots as Educators
  • 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.
  • Learning analytics for school and system management
  • Organisational benefits from learning analytics
  • Three examples
  • Learning analytics' challenges for organisations
  • Moving forward with learning analytics
  • Conclusion
  • Early warning systems and indicators of dropping out of upper secondary school: the emerging role of digital technologies
  • Early Warning Systems and Indicators (EWS/EWI)
  • Accurate predictors of dropping out
  • Application of emerging digital technologies: pattern analytics and data science
  • Conclusion and future directions
  • Game-based assessment for education
  • Why game- or simulation-based assessment in education?
  • How do we build game-based tests?
  • Some examples of game-based assessment in education
  • What is the long-term promise of this approach and what is necessary to get us there?
  • Blockchain for Education: A New Credentialing Ecosystem
  • Understanding blockchain technology
  • Benef its of blockchain for educational credentialing
  • Real-World implementations
  • Driving change
  • Conclusion
  • Authors
  • Figures
  • Figure 1.1 Digitalisation at the Luwan No 1 Central Primary School in Shanghai
  • Figure 1.2 The "digital classroom" system at Tongji University's first demonstration high school
  • Figure 3.1 Six levels of automation towards the self-driving car
  • Figure 3.2 Six levels of automation of personalised learning
  • Figure 3.3 Three challenges for high-performance education with AI
  • Figure 3.4 Multimodal data source to track learners and their environment
  • Figure 3.5 Three types of interventions in domain independent actions
  • Figure 3.6 Problems and dashboards in Snappet
  • Figure 3.7 Problems and dashboards in MATHia
  • Figure 3.8 Personalised remedial activities to learn decimals based on error patterns
  • Figure 3.9 The Moment-by-Moment Learning Algorithm.
  • Figure 4.1 Components of engagement
  • Figure 4.2 Time course of engagement and influence of context
  • Figure 4.3 Major categories of measures of engagement with examples (last row)
  • Figure 4.4 Overview of the AAA-approach.
  • Figure 4.5 Using consumer-grade eye tracker (left) to monitor visual attention while students interact with Guru (right) in classrooms
  • Figure 4.6 Sample video (left) in the Algebra Nation platform along with a self-report engagement questionnaire (right)
  • Figure 4.7 Organisation of activities with respect to expected engagement and learning (from left to higher) as per the ICAP framework
  • Figure 4.8 Example of a problem and pendulum solution in Physics Playground
  • Figure 4.9 GazeTutor interface with animated agent (0), image panel (1), and input box (2). Blank screen areas on the bottom are not displayed
  • Figure 4.10 Eye tracking during reading. Filled circles display fixations (where eyes are focused) and lines display saccades (rapid eye movements between fixations)
  • Figure 4.11 SEAT interface displaying the overall class-view (left) and student-specific view (right).
  • Figure 5.1 The physical-digital blur
  • Figure 5.2 One Mouse per Child for Basic Math
  • Figure 5.3 Estimating the average level of attention in a classroom by analysing head movements
  • Figure 5.4 Three types of interventions in domain independent actions
  • Figure 5.5 Teaching dashboard of a logistics lesson
  • Figure 5.6 Use of a dashboard to increase learning gains
  • Figure 5.7 Using dashboards to form dynamic teams
  • Figure 5.8 Progression chart of the time extension gain
  • Figure 5.9 Progression chart of the time extension gain
  • Figure 5.10 Showing teachers their location in the classroom
  • Figure 6.1 The ECHOES environment
  • Figure 6.2 Making it rain in ECHOES.
  • Figure 6.3 A child interacting with ECHOES shares his joy with one of the researchers
  • Figure 6.4 A screenshot from the Dynamico app
  • Figure 6.5 Using a combination of auditory and vibratory feedback to convey graphical information
  • Figure 7.1 English-as-a-second-language teaching with the help of a social robot
  • Figure 7.2 A class with a robot assistant
  • Figure 7.3 Classrooms in Australia and Japan were connected by a telepresence robot in real time
  • Figure 7.4 A telepresence robot facilitated the second-language learning of students
  • Figure 7.5 Senior people can give a lecture from their homes by making use of a telepresence robot
  • Figure 7.6 Experimental setup
  • Figure 7.7 TELESAR VI. (2019)
  • Figure 8.1 TDSS workflow and information
  • Figure 8.2 The LAPS process
  • Figure 8.3 Making it rain in ECHOES
  • Figure 9.1 Hierarchical cluster analysis of K-12 student subject-specific grades identifies student dropout
  • Figure 9.2 Only a small fraction of a schooling organisation's early warning systems are composed of predictor identification
  • Figure 10.1 SImCityEDU: Pollution Challenge (GlassLab)
  • Figure 10.2 Crisis in Space (ACTNext)
  • Figure 10.3 PEEP - Project Education Ecosystem Placement (Imbellus).

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