Index
2019; Linguagem: Inglês
10.1108/s1479-367920190000038016
ISSN1479-3679
ResumoCitation (2019), "Index", Jules, T.D. and Salajan, F.D. (Ed.) The Educational Intelligent Economy: Big Data, Artificial Intelligence, Machine Learning and the Internet of Things in Education (International Perspectives on Education and Society, Vol. 38), Emerald Publishing Limited, Bingley, pp. 269-280. https://doi.org/10.1108/S1479-367920190000038016 Publisher: Emerald Publishing Limited Copyright © 2020 Emerald Publishing Limited INDEX Note: Page numbers followed by “n” with numbers indicate notes. Academy-in-a-box, 226 Access economy, 24 Acquaintance society, 243 Action analytics, 253 Actionability, 224 Administrators, implications of LA for, 261–264 Adult educators, 135 Advanced regression techniques, 238 Agile economy, 24 Airbnb, 23 ALEKS ITS, 153 Algorithm(ic) biases, 118, 120 governance, 5, 226 violence, 245 Amazon, 23 American Civil Liberties Union, 191 American cybernetics, 57–58, 61 American Educational Research Association (AERA), 42–43 Analytics, 256 academic, 253–254 action, 253 predictive, 56, 216 real-time learning, 60 solutions, 252 web, 253 Anthropology, 220 Anti-colonialism, 35 Anticipative thinking models, 7 Anticipatory systems, 89 Apple, 183 Arithmetic operations, 21 Artificial intelligence (AI), 8–9, 53, 62, 89, 110, 115–116, 128, 146–152, 180, 192, 200, 206, 208, 235–237 AMM, 186–188 AST, 186–188 automotive engineers, 183–186 across education and industry, 180 governance, challenges, and responses, 188–192 rebellions, 247n7 reimagining occupations in automobile industry, 182 Winter, 181 Artificial Intelligence in Education (AIED), 115–117, 238–239 Assemblages, 25 Assessment and Teaching of Twenty-first Century Skills (AT C21s), 59 Association of Southeast Asian Nations (ASEAN), 202 Attrition, 77 Austin Peay University, 256 Australian Curriculum, 114 Auto industry, AI across, 180–192 Automation, 184, 186, 189, 191 Automobiles, 180 reimagining occupations in automobile industry, 182 Automotive engineers, 181, 183–186 Automotive Master Mechanics (AMM), 186–188 Automotive mechanics and technicians, 181 Automotive Specialty Technicians (AST), 186–188 Autonomous navigation, 185 Autonomous vehicles, 89 Baidu (Chinese search engine giant), 183 Bedfordshire University, 256 Behavioral economics, 223 Big Data, 1–2, 16, 24, 34, 36–40, 53, 70, 110, 113, 115, 129, 200, 205, 224–225, 234, 239, 252, 259, 261 access to, 5–6 Big Data-based analytics, 115 control and regulation of, 4 European Union as case for comparison in era of, 202–203 fundamentalism, 36–37 indiscriminate use of, 7 intentional deployment, 4 manifest lucrative value, 5 mining, 37 “new” about, 55–57 opportunities and challenges, 115–117 Bill and Melinda Gates Foundation, 260 Biological determinism, 220 Biopolitical concerns, 220 Biopolitics, 55 Biotechnology, 89 Blackboard, 259 Blitzscaling, 18, 24–28 Blockchain, 110, 115 opportunities and challenges, 119 BrainChain, 236 Brave New World (Huxley), 234 Chinese dream, 240–246 UN’s Education 2030 agenda, 235–240 Bridge International Academies, 223–224 Bring Your Own Device Classes (BYOD Classes), 244 Broadening participation, 45 Brontobytes, 16 Business intelligence, 253 “Career Pathways” initiatives, 135 Caribbean Community (CARICOM), 202 Cedefop, 115 Centre for Educational Technology, Interoperability and Standards (CETIS), 253 Charitable foundations, 260–261 Child-Parent Center program in Chicago, 95 China adaptive system, 246 cultural and moral essence, 241 Chinese Communist Party, 241 Chinese Dream, 234, 240–246 Chinese traditional culture, 241 Choice architects, 223 “City Brain” project in Hangzhou, 242 Civic Education Study (CivEd), 20 Classroom technology, 153 Classroom-level “smart” systems, 60 Closed loop, 239 Cloud Classes, 244 Cloud Computing, 234 Code of Ethics, 42 Cognitive universalizations, 58–60 Coherence, 205–210 Collaborative economy, 24 Colonial difference, 219–222 Colonial model, 35 Colonial residues in data-driven reforms, 228–229 Colonial-era school reforms, 226 Commercial presence, 23 Commercialization of education, 152 of LA, 259–260 Commissioned colonial surveys, 218 Commodification data, 35 of knowledge, 163, 165 of LA, 259–260 Communication systems, 52 Comparative and International Education (CIE), 17–18, 34, 37, 40, 43, 110, 202 data collection and IEA, 18–20 data collection and OECD, 20–22 data collection and UNESCO reports, 22 Comparative and International Education Society (CIES), 34 Comparative content analysis, 98–100 Comparative education, 145, 155 Comparative Education Society (see Comparative and International Education Society (CIES)) Complex survey sampling, 137 Compulsory schooling, 8, 88 educational intelligent economy, 89–90 framework for analysis and limitations, 90–91 learning vs. schooling, 90 purpose of, 96–98 scenarios within educational intelligent economy future, 91–100 Computational theories of human behavior, 56 Confucianism, 234, 240, 241, 245 Connectivism and Connective Knowledge (CCK), 24 Consortium for Student Retention Data Exchange (CSRDE), 255 Contamination, 77 Content analysis, 165 Contrapuntal method, 218 Control and regulation of Big Data, 4 Coordiated civic action, 29n17 Coordinated Plan for Artificial Intelligence, 211 Core and periphery model, 36, 40 Craigslist, 23 Credit checks, 242 Cross-border supply, 23 Cutting-edge technology, value of, 110 Cyberbullying, 120 Cybernetics, 53, 61 Cyberpunk world, 246n5 Cyberspace, 236, 246n5 Cyborg dialectic, 163 Danish Board of Technology, 48n5 Dark data, 29n7, 144 Data, 26 analysts, methods, trends, and creating, 135–137 analytics, 135 cleanup, 144 commodification, 35 control, 44–46 data servitization, governance in era of, 23–24 economy, 9, 210 ownership, 117 privacy, 118 security, 120–121 sharing, 42–44 technology in education, 148–150 Data collection and IEA, 18–20 and OECD, 20–22 and UNESCO reports, 22 Data frontierism, 36–38 data sharing, 42–44 electronic colonialism to, 40 neoliberal inclinations, 40–41 Data mining, 23, 146–152, 152–157 educational intelligence, and comparative analysis, 154–155 Data-driven decision-making, 133 Data-driven economy, 206 Data-driven educational governance, 24 Data-driven educational systems American cybernetics and structural objectivity, 57–58 cognitive and “non-cognitive” universalizations, 58–60 intelligence-as-information, 52–53 methodological notes, 53–54 models, minds, and social physics, 54–55 “new” about big data and learning analytics, 55–57 of perceptrons and change, 60–63 Data-driven learning analytic platforms, 54 Data-driven reforms, colonial residues in, 228–229 Data-harvesting machines, 188 Data-Informed Decision-Making approach (DIDM approach), 37 Datafication, 25, 56–57, 63n4 Datum, 28n3, 29n6 “Decentralized city” design, 89, 95 Decolonial frameworks, 34 Big Data, 36–40 educational intelligent economy, 46–47 electronic colonialism to digital frontierism, 40–44 neoliberal scientism, control of data, and NSF, 44–46 notion of CIE, 34–35 stake, 35–36 Deep learning algorithms, 238 DeepBrain, 236 DeepMind, 236 Degree Compass system, 255 Deliberations, 47 Dell Inc., 163 Democratic Republic of Congo (DRC), 37 Dependent races, 220 Dependent variables, 72 Derritorializiation, 25 Desire2Learn, 259 Developmentalism, 220 “Deviancy and moral disorder”, 54–55 Differences and exclusions, 217 Differential mode of association, 243 Digital colonialism, 36 competency, 113–115 data, 70 exclusion due to algorithmic biases, 118 governance, 234 landscape, 200 platforms, 114 revolution, 208, 210 Taylorism, 26 technologies, 203 transformation of education, 238 Digital education data mining, machine learning, and AI, 146–152 data mining and, 152–157 economic security and educational intelligence, 145–146 Digital Single Market, 205–208 Digitization, 24, 56–57, 63n4 Discourse analysis, 210 Disruptive innovation, 23 Document literacy data, 21 Drivers of Student Success, 170 Dusty, dangerous, or dull work (three Ds), 148 Dystopic machine, 246 East London University, 256 EBay, 23 ECAR program, 260 Economic intelligence, 27 Economic security, 145–146 Economies, 56 Edinburgh University, 255 Edu-business, 5, 163 Education, 20, 111, 144, 154, 201 AI across, 180–192 export business, 3 markets, 5 policy, 139 primary and secondary, 113 Education for All Global Monitoring Report (see Global Education Monitoring Report (GMR)) Educational Big Data governance, 24–28 Educational borrowing and lending, 158 Educational brokers, 28 Educational data, 24 educational data-driven governance, 23 governance, 25–26 Educational data mining (EDM), 238–239, 253 Educational Department, 220 Educational fundamentalism, 18 Educational intelligence, 1–3, 6, 17, 22, 27, 34–35, 52, 88, 145–146, 164, 200, 201, 203, 209–212, 236 Big Data, 17–18 in CIE, 18–22 educational Big Data governance, 24–28 in era of data servitization, 23–24 interwoven worlds of, 245–246 methodology, 165–166 theoretical framework, 164–165 Web of Battelle, 166–170 Educational intelligent economy, 1–4, 7, 28, 34, 37, 62, 89–90, 217 digital competency, literacy and, 113–115 EU policy challenges in, 210–211 Haunted data of, 226–228 scenarios within educational intelligent economy future, 91–100 technology and, 115–122 Educational policy-making, 16 Educational programs, 181 Educational regionalism, 18 Educational technology (EdTech), 148 Educators, implications of LA for, 261–264 Educators use technology for tasks, 162–163 EDUCAUSE, 253, 260–261, 263 Edusphere, 239 Electronic colonialism theory, 37 Electronic colonialism to digital frontierism, 40–44 Employability, 154 Energy storage, 89 England’s Higher Education Funding Council’s funding, 260 Enrollment rates, 152 Epistemic communities, 39–40 ePrivacy Regulation, 203–204 European “science” of pedagogy, 52 European Commission, 111, 208, 210, 211 European Institute of Innovation and Technology (EIT), 209 European policy space for data regulation with global implications, 203–205 European Statistical Office (EUROSTAT), 203 European Statistical System, 203 European Training Foundation (ETF), 115 European Union (EU) as case for comparison in era of big data, 202–203 competing policy narratives, 205–210 Data Protection Directive, 203 further research directions, 211–212 policy challenges in educational intelligent economy, 210–211 policy riposte to unrestrained data flows, 200–201 “Evidence-based” decision-making, 75 Exceptional Student Experience Initiative, 259 Experience economy, 24 Facebook, 16 Fast policy, 135 Federal Automated Vehicles Policy, 182 Fine grain of practice, 91–93 First International Conference, 257 First International Mathematics Study (FIMS), 19 FISITA (International Federation of Automotive Engineering Societies), 184 Fitness App Polar, 16 Flipped curriculum, 154 Formative Instructional Practices (FIP), 169 Forward looking, 88 Fourth Industrial Revolution, 6, 16, 89, 128, 130–132, 144, 157, 201 Fragmentation, 205–210 Freelance economy, 24 Fulbright Teacher Exchange, 121 Fundamental inequality, principle of, 221 Funding, 163–164 Gender Equality Scorecard (GE Scorecard), 29n14 General Agreement on Trade in Services (GATS), 23 General Data Protection Regulation (GDPR), 5, 24, 118, 203, 205, 209 General documents about ethos, 98–99 General Education Management Systems (GEMS), 163 General equilibrium effects, 79 Generalizability lack in RCTs, 78 Generalized linear mixed models, 137 Geo-mapping, 156 Geopbyte, 16 Gig-economy, 24 Global Education Inc., 6 Global education industry (GEI), 27 Global Education Monitoring Report (GMR), 22 Global education study, 169–170 Global governance of education, 70 impact evaluation, 70–71 methodological limitations of impact evaluation, 71–80 Global North, 7, 34–36, 40, 43, 46–47, 217 Global South, 7, 36, 40, 43, 224 Google, 23, 163, 183 Governance in CIE, 18–22 in era of data servitization, 23–24 Governing clouds, 236–237 Governing rationalities, 132–135 Government policies, 119–122 Governments, 260–261 Grades, 156 Group differences, 77 Groupminds, 246n3 Hauntology, 226 Hazelwood East High School in St. Louis, 153 Hegemonic empire, 37 Higher education (HE), 252–253 Higher education institutions (HEIs), 252 Higher education managers and administrators, 257–259 Hive mind, 236, 247n7 Horizons Report, 258 Huddersfield University, 255 Human algorithmic boundary-making assemblages, 53 Human Nature Club, The (Thorndike), 63n1 Human-To-Machine (H2M), 148 Humanitarian ends, 235 Ideological innumeracy, 139 IEA’s large-scale international assessment (ILSA), 19, 58–59, 63n3 Immersive AIEd, 245 tutoring, 238 Impact evaluation, 70–71 attribution, 71–72 methodological limitations of, 71 RCTs, 75–80 regression analysis, 72–75 Imperialism, 40 Inclusion, 45 Independent Learning Centre (ILC), 145, 155 Independent variables, 72 Individualization, 153 Industrial megamachine, 236 Industry 4. 0, 16, 128 Influential policy-informing entities, 7 Information and communication technology (ICT), 20, 202 Information barriers, 205 Information Technology (IT), 144, 183 Infra-spectrality, 226 “Integrity awareness and creditworthiness” of Chinese people, 242 Integrity collapse, 243 Intelligence, 52 of educational intelligent economy, 223 intelligence-as-information, 52–53 Intelligence Unleashed, 238 Intelligent data, 37 Intelligent economy, 16, 27, 89, 162 Intelligent environments, 245 Intelligent Tutoring Systems (ITS), 144, 152 Intentional deployment of Big Data, 4 Interactive governance, 29n15 International Adult Literacy Survey (IALS), 20 International Civic and Citizenship Study (ICCS), 20 International comparative target achievements (ICTA), 27 International Educational Data Mining Society, 257 International Evaluation of Educational Achievement (IEA), 18, 28 data collection and, 18–20 International Federation of Automotive Engineering Societies, 184 International Labour Organisation (ILO), 115 International large-scale assessments (ILSAs), 53 International Monetary Fund, 18 Internet, 89 Internet for All initiative, 237–238 Internet of Things (IoT), 89, 128, 144, 200, 206, 208 Interuniversity Consortium for Political and Social Research (ICPSR), 43 Jeanes School, 221–222, 226–227 JISC CETIS Analytics Series of publications, 260–261, 263 Joint Research Centre (JRC), 119 Journal of Learning Analytics Research, 255 Kenya Colony’s Education Department, 220 Killer robots, 189, 191 Knowledge economy, 206 Knowledge-based economy, 28, 162 Knowledge-based enterprise, 27 Lamarckian impressibility, 220 Learner role, 91–94 Learning, 90 management, 112 self-regulation of, 114 skills, 24 Learning analytics (LA), 53, 59, 115, 252–254 charitable foundations, technology networks, and governments, 260–261 commercialization and commodification, 259–260 community of research and practice, 256–261 drivers of learning analytics development, 256–261 higher education managers and administrators, 257–259 “new” about, 55–57 past, present, and future, 252 in practice, 254–256 for students, educators, and administrators, 261–264 Learning management systems (LMS), 144, 259 Lex specialis, 203 Libertarian paternalism, 223 Lifelong learning, 111–112, 114, 117–118 partners, 238 Literacy, 113–115 Little analytics, 56 Loughborough University, 255 Machine learning, 53, 62, 110, 115, 144, 146–152, 154, 246n4 on education policy and practice, 234 Machine-To-Machine (M2M), 148 Manchester Metropolitan University, 255–256 Market fundamentalism, 22 Massive Open Online Courses (MOOCs), 23, 110–111, 113, 121, 144 Master Algorithm, 236 Materials science, 89 Measurement systems, 262 Megamachine, 236, 239 Mental development, 220 Mental labor, 54 Meta-governance, 29n18 Meta-steering, 26–27 Metro Early College High School (MECHS), 168 Metro Institute of Technology (MIT), 168 Microsoft Corporation, 163 Microsoft Word, 113 Minds, 54 Hive, 236, 247n7 models, 53 MindSphere, 236 ML+AI in Educational Technology, 148 Mobile applications, 243 Modus operandi, 17 governance, 23–24 Moral ends, 235 Multidimensional Adaptive Assessment, 94 Multilevel and longitudinal linear modeling, 137 Multimedia industries, 40 Multinational corporations, 163 Multiple-choice assessments, 111 Mumford’s dystopic machine, humanism of, 246 Nanotechnology, 89 National Assessment of Educational Progress (NAEP), 170, 173 National Center for Education Statistics (NCES), 19 National education systems, 27–28 National Highway Traffic Safety Administration, 182 National Science Board (NSB), 168 National Science Foundation (NSF), 34, 44–46, 168 Native men, 221–222 Neocolonial discourses, 35 Neocolonialism, 40 Neoliberal scientism, 44–46 Network ethnographic analysis, 165 governance, 29n16 Neural network, 62 Neuroengineering, 92 Neuromancer, 246n5 New Story of Stone, The (Wu Jianren), 241 “New Taylorism”, 26 New Zealand, 88 “Non-cognitive” universalizations, 58–60 Non-educational players, 28 Non-profit organizations, 8 Non-spatial process, 25 Noosphere, 236, 239 North American educational providers, 144 Nottingham Trent University, 255 Nudges, 223 O*Net, 183 Office of Educational Technology on analytics in education, 260 Ohio State University (OSU), 167–168 Ohio STEM Learning Network (OSLN), 168 Omega point, 236 “On demand economy”, 24 Ontario Secondary School Diploma (OSSD), 155 Open learning management systems, 23–24 Open Method of Cooordiantion (OMC), 29n17 Open talent economy, 24 Operational Technology (OT), 144 Optimization logics, 60 Organisation for Economic Cooperation and Development (OECD), 20–22, 28, 70, 115, 202 Organizational forgetting, 99–100 Organizational management theories, 223 Partners in Learning program, 163 Partnerships for International Research and Education (PIRE), 46 Paternalistic articulations, 222 Pedagogy, 90 Pedagogy of relatedness”, 94 Perceptron, 53 and change, 60–63 Performance-based governance systems, 29n19 Personal Learning Environments (PLE), 24 Personalizations, 56 Personalized knowledge, 112 Persuasive technology, 239 Philanthropy, 163 “Place-based” teaching modes, 24 Planning Outline for Construction of Social Credit System, The (2014–2020), 243 Policy-relevant knowledge, 70, 75 Poundbury project, 98 Poverty, 227 “Predict and control” narratives, 56 Predictive analytics, 53, 56, 216 Predictive Analytics Reporting (PAR), 260 Predictive Analytics Research Framework, 255 Private organizational involvement, 164 Private sector, 162, 167 Product-as-a-service providers, 23 Programme for International Student Assessment (PISA), 3, 7, 21, 58–59, 62, 70, 73–75, 113, 122, 170, 173, 238 Programme for the International Assessment of Adult Competencies (PIAAC), 3, 21–22, 134 Progress in International Reading Literacy Study (PIRLS), 20, 170, 173 Prose literacy data, 21 Public Library of Science (PLOS), 43 Public or charitable investment, 261 Quantitative literacy data, 21 Quantitative methods, 70, 71–72, 75 Quantitative techniques, 135 Quantum computing, 89 Randomization, 75, 77 Randomized control trials (RCTs), 75–76 group differences, 77 lack of generalizability, 78 unbiasedness and impact, 77–78 variance of treatment effects, 78 Rational actors, 23 “Ravaging” anti-colonial movements, 35 “Real-time” assessment, 59 “Real-time” learning analytics, 60 Recidivism, 191 Reclaim colonialism, 35 Red, Amber, Green ratings (RAG ratings), 255 Regression analysis, 72–75 Report on Government Work (2015), 241 “Research”, 41 Reterritorialization, 25 Right to Repair movement, 187–188 Robotic process automation (RPA), 145 Robotics, 89 School design, 94–96 School reforms, 219–222 for Africa, 216 School website analysis, 99 coding, 104–105 Schooling, 20, 90 Science, Technology, Engineering and Mathematics (STEM), 19, 144, 151, 166 Learning Networks across US, 169 Scientization, 165 2nd AI for Good Global Summit, 237–238 Second Information Technology in Education Study (SITES), 20 Second International Mathematics Study (SIMS), 19 Self in Confucianism, 244 “Self-driving car” engineering, 182, 185, 187 Self-regulation of learning, 114 Sharing economy, 24 Sharing Research Data, 42 Signals Project, 255 “Single-layer” perceptron, 62 Slack resources, 100 Smart Cities, 240–246 Smart governance, 235 under benevolent AI clouds, 235–240 Smart Schools, 240–246 Smart technologies, 5 Snowball sampling, 165 Social credit, 240–246 justice, 35 physics, 54–56 social-commercial behavior ratings, 242 transition, 243 Socialist Core Values, 241 Society for Learning Analytics Research (SoLAR), 257 Sociological neo-institutionalism, 164–165 Sociology, 220 Sociotechnical assemblages, 53–54 Sovereign individuals, 236 “Spaghetti bowl” of educational governance, 26 Spatiotemporal data, 28n5 Spillover effects, 77 Spiritual transcendence, 228 STEMx, 169 Challenge Grant Program, 169 STEMxChange annual conference, 169 STEMxSuperstars, 169 Structural objectivity, 57–58 Student implications of LA for, 261–264 records, 253 student-as-customer model, 18 success, 252, 255 Student Experience® Survey, 169 Subjectification, 54 Sui generis phenomenon, 6 Sustainable Development Goals (SDGs), 27, 234 Swarm intelligence, 247n7 Synthesis of Optical Materials for Bioapplications: Research, Education, Recruitment and Outreach (SOMBRERO), 46 Systems-thinking, 56 Taoism, 234, 240 Teacher psychological principles of teacher reflection, 225 role, 91–94 Teacher Education and Development Study in Mathematics (TEDS-M), 20 Teaching and Learning International Survey (TALIS), 21 Techno-solutionism, 25 Technological/technology advancements, 35–36 change, 182 and educational intelligent economy, 115–122 networks, 260–261 technological-driven disruption, 27 Territorializiation, 25 Textual analysis, 151 Theories of cognition, 61 Thinking devices, 205 Thinking machine, 236 3-D printing, 89 Time-stamped datum, 28n3 Top-down approach, 39 Top-performing systems, 170 Totalitarian utopia, 237 Traditional “stop-and-start” approaches, 238 Trans-individual intention, 224 Transcendence through technology, 234, 236 Transnational school reforms colonial residues in data-driven reforms, 228–229 data as infra-spectral and algorithmic governance of difference, 222–226 haunted data of “educational intelligent economy”, 226–228 school reforms and colonial difference, 219–222 ungrounding data’s colonial residues, 218–219 Trends in International Mathematics and Science Study (TIMSS), 3, 7, 19, 73, 113, 122, 170, 173, 238 Tribal consciousness, 227 Truth-as-data, 226 Twitter, 23 Uber, 23, 183 UN’s Education 2030 agenda, 235 on bright lights and dark shadows, 235–237 smarter global governance, 237–240 Unbiasedness and impact, 77–78 UNESCO, 22, 28 Ungrounding data’s colonial residues, 218–219 United Nations Children’s Fund (UNICEF), 22 United Nations Development Programme (UNDP), 22 United Nations Population Fund (UNFPA), 22 University of London Computing Centre, 256 University of Washington Tacoma, 255 US Department of Education, 260 Utopia-dystopia dialectics, 235 Variety, 115–116 Velocity, 115–116 Virtual and augmented reality (VR/AR), 144, 151–152 Virtual learning environments (VLE), 144, 253, 259 Vision for future, 111–112 Volume, 115–116 Volume, variety, and velocity (3Vs), 25, 37–38, 63n3 Volume, Variety, Velocity, Value, Veracity (five V’s), 37–38 Walmart, 128 War-inspired Operations Research and American cybernetics, 52 Web analytics, 253 Web of Battelle, 166–167, 171 Center for Science, Engineering, and Public Policy, 167–168 Education, 167 FIP, 169 global education study, 169–170 for Kids, 167 MECHS, 168 Memorial Institute, 166 OSLN, 168 STEM Learning Networks across US, 169 Student Experience® Survey, 169 Workforce development policymakers, 135 Workforce Innovation and Opportunity Act (2014), 135 World Bank, 18 World Education for All (WCEFA), 22 World Education Services (WES), 156 Yottabytes, 16 Zettabytes, 16 Book Chapters Prelims Introduction: The Educational Intelligent Economy, Educational Intelligence, and Big Data Part I: (Re)Conceptualizing Data in Comparative and International Education Chapter 1: Big “G” and Small “g”: The Variable Geometries of Educational Governance in an Era of Big Data Chapter 2: The Educational Intelligent Economy and Big Data in Comparative and International Education Research: A Decolonial Vision Part II: Revisiting Methodologies Chapter 3: The Perceptron: A Partial History of Models and Minds in Data-Driven Educational Systems Chapter 4: Best Practices from Best Methods? Big Data and the Limitations of Impact Evaluation in the Global Governance of Education Chapter 5: What if Compulsory Schooling was a 21st Century Invention? Part III: Workforce Participation, Transformation, and Industry 4.0 Chapter 6: The Educational Intelligent Economy – Lifelong Learning – A vision for the future Chapter 7: Humanistic, Innovative Solutionism: What Role do Data Analytics Play in Developing a More Responsive and More Intelligent Adult and Workforce Education Policy? Chapter 8: Data Mining and Predictive Analytics in Digital Education: Lessons We can Learn from Big Data that are Often Discarded Chapter 9: The Intricate Web of Educational Governance: The Cyborg Dialectic and Commodification of Knowledge Chapter 10: Engineering the Mechanism/Repairing the Robot: Artificial Intelligence at the Intersection of Education and Industry Part IV: Case Studies Chapter 11: Policy Development for an Educational Intelligent Economy in the European Union: An Illusory Prospect? Chapter 12: Haunted Data: The Colonial Residues of Transnational School Reforms in Kenya Chapter 13: Brave New World(s): Governing Clouds, Smart Schools, and the Rise of AIEd Chapter 14: Learning Analytics for Student Success at University: Trends and Dilemmas Index
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