Skip to main content Accessibility help
×
Hostname: page-component-cd9895bd7-hc48f Total loading time: 0 Render date: 2024-12-23T06:15:48.941Z Has data issue: false hasContentIssue false

Part VII - Futuristic and Ultramodern Higher Education

Published online by Cambridge University Press:  09 June 2022

Andreas Kaplan
Affiliation:
ESCP Business School Berlin
Get access

Summary

Image of the first page of this content. For PDF version, please use the ‘Save PDF’ preceeding this image.'
Type
Chapter
Information
Publisher: Cambridge University Press
Print publication year: 2022

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

References

Barrett, L. F. and Russell, J. A. (2015) The Psychological Construction of Emotion. New York: Guilford.Google Scholar
Bosch, N., D’Mello, S. K., Baker, R. S., Ocumpaugh, J., Shute, V., Ventura, M., Wang, L., and Zhao, W. (2016) Detecting Student Emotions in Computer-Enabled Classrooms. Proceedings of the 25th International Joint Conference on Artificial Intelligence, 41254129.Google Scholar
Boyd, D., and Crawford, K. (2012) Critical Questions for Big Data: Provocations for a Cultural, Technological, and Scholarly Phenomenon. Information Communication and Society, 15(5), 662679.Google Scholar
Canales, L., Daelemans, W., Boldrini, E., and Martínez-Barco, P. (2019) Emolabel: Semi-automatic Methodology for Emotion Annotation of Social Media Text. IEEE Transactions on Affective Computing.Google Scholar
Culnan, M. J., and Armstrong, P. K. (1999) Information Privacy Concerns, Procedural Fairness, and Impersonal Trust: An Empirical Investigation. Organization Science, 10(1), 104115.Google Scholar
Data Ethics Commission of the Federal Government. Opinion of the Data Ethics Commission. (2018). www.bmi.bund.de/SharedDocs/downloads/EN/themen/it-digital-policy/datenethikkommission-abschlussgutachten-kurz.pdf.Google Scholar
David, B., Chalon, R., Zhang, B., and Yin, C. (2019) Design of a Collaborative Learning Environment Integrating Emotions and Virtual Assistants (Chatbots). IEEE 23rd International Conference on Computer Supported Cooperative Work in Design, 5156.CrossRefGoogle Scholar
Deng, J., and Ren, F. (2021) A Survey of Textual Emotion Recognition and Its Challenges. IEEE Transactions on Affective Computing.Google Scholar
D’mello, S., and Graesser, A. (2013) Autotutor and Affective Autotutor: Learning by Talking with Cognitively and Emotionally Intelligent Computers That Talk Back. ACM Transactions on Interactive Intelligent Systems, 2(4), 139.Google Scholar
Ekman, P. (1984) Basic Emotions. Handbook of Cognition and Emotion, 98(45–60), 16.Google Scholar
Ez-zaouia, M., Tabard, A., and Lavoué, E. (2020) EMODASH: A Dashboard Supporting Retrospective Awareness of Emotions in Online Learning. International Journal of Human Computer Studies, 139, 102411.Google Scholar
Feng, K., and Chaspari, T. (2020) A Review of Generalizable Transfer Learning in Automatic Emotion Recognition. Frontiers in Computer Science, 2(9), 114.Google Scholar
Ghaleb, E., Popa, M., and Asteriadis, S. (2019) Multimodal and Temporal Perception of Audio-Visual Cues for Emotion Recognition. 8th International Conference on Affective Computing and Intelligent Interaction, 552558.Google Scholar
Goetz, T., Haag, L., Lipnevich, A. A., Keller, M. M., Frenzel, A. C., and Collier, A. P. (2014) Between-Domain Relations of Students’ Academic Emotions and Their Judgments of School Domain Similarity. Frontiers in Psychology, 5, 1153.Google Scholar
Inventado, P. S., Legaspi, R., Suarez, M., and Numao, M. (2011) Predicting Student Emotions Resulting from Appraisal of Its Feedback. Research & Practice in Technology Enhanced Learning, 6(2).Google Scholar
Jack, R. E., Garrod, O. G., and Schyns, P. G. (2014) Dynamic Facial Expressions of Emotion Transmit an Evolving Hierarchy of Signals over Time. Current Biology, 24(2), 187192.CrossRefGoogle ScholarPubMed
Jivet, I., Scheffel, M., Drachsler, H., and Specht, M. (2017) Awareness Is Not Enough: Pitfalls of Learning Analytics Dashboards in the Educational Practice. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10474 LNCS. New York: Springer.Google Scholar
Kort, B., Reilly, R., and Picard, R. W. (2001) An Affective Model of Interplay between Emotions and Learning: Reengineering Educational Pedagogy-Building a Learning Companion. Proceedings IEEE International Conference on Advanced Learning Technologies, 4346.Google Scholar
Lin, S.-C., Chen, C.-J., and Lee, T.-J. (2020) A Multi-label Classification with Hybrid Label-Based Meta-learning Method in Internet of Things. IEEE Access, 8, 4226142269.Google Scholar
Liu, Z., Pataranutaporn, V., Ocumpaugh, J., and Baker, R. (2013) Sequences of Frustration and Confusion, and Learning. Proceedings of the 6th International Conference on Educational Data Mining, 114120.Google Scholar
Liu, Z., Wang, T., Pinkwart, N., Liu, S., and Kang, L. (2018) An Emotion-Oriented Topic Modelling Approach to Discover What Students Are Concerned about in Course Forums. IEEE 18th International Conference on Advanced Learning Technologies, pp. 170172.Google Scholar
Luzeckyj, A., West, D. S., Searle, B. K., Toohey, D. P., Vanderlelie, J. J., and Bell, K. R. (2020) Stakeholder Perspectives (Staff and Students) on Institution-Wide Use of Learning Analytics to Improve Learning and Teaching Outcomes. In Ifenthaler, D. and Gibson, D., eds., Adoption of Data Analytics in Higher Education Learning and Teaching. New York: Springer, 177200.CrossRefGoogle Scholar
Matlovic, T., Gaspar, P., Moro, R., Simko, J., and Bielikova, M. (2016) Emotions Detection Using Facial Expressions Recognition and EEG. 11th International Workshop on Semantic and Social Media Adaptation and Personalization, pp. 1823.Google Scholar
McStay, A. (2020) Emotional AI and EdTech: Serving the Public Food? Learning, Media and Technology, 45(3), 270283.CrossRefGoogle Scholar
Montero, C. S. and Suhonen, J. (2014) Emotion Analysis Meets Learning Analytics – Online Learner Profiling beyond Numerical Data. Proceedings of the 14th International Koli Calling Conference on Computing Education Research, 165169.Google Scholar
Ocumpaugh, J., Baker, R. S., Karumbaiah, S., Crossley, S. A., and Labrum, M. (2020) Affective Sequences and Student Actions within Reasoning Mind. In International Conference on Artificial Intelligence in Education. New York: Springer, 437447.Google Scholar
Pekrun, R., and Linnenbrink-Garcia, L. (2012) Academic Emotions and Student Engagement. In Handbook of Research on Student Engagement. New York: Springer.Google Scholar
Roberts, L. D., Howell, J. A., Seaman, K., and Gibson, D. C. (2016) Student Attitudes toward Learning Analytics in Higher Education: ‘The Fitbit Version of the Learning World’. Frontiers in Psychology, 7, 111. www.researchgate.net/profile/Joel-Howell/publication/311779993_Student_Attitudes_toward_Learning_Analytics_in_Higher_Education_The_Fitbit_Version_of_the_Learning_World/links/586c6dd008aebf17d3a5b7b1/Student-Attitudes-toward-Learning-Analytics-in-Higher-Education-The-Fitbit-Version-of-the-Learning-World.pdf.Google Scholar
Ruiz, S., Klerkx, J., Charleer, S., Fernández-Castro, I., Urretavizcaya, M., and Duval, E. (2016) Supporting Learning by Considering Emotions: Tracking and Visualization. A Case Study. Proceedings of the 6th International Conference on Learning Analytics & Knowledge. New York: ACM, 254263.Google Scholar
Sarmiento, J. P., Campos, F., and Wise, A. (2020) Engaging Students as Co-Designers of Learning Analytics. Proceedings 10th International Conference on Learning Analytics & Knowledge.Google Scholar
Schwendimann, B. A., Rodriguez-Triana, M. J., Vozniuk, A., Prieto, L. P., Boroujeni, M. S., Holzer, A., Gillet, D., and Dillenbourg, P. (2017) Perceiving Learning at a Glance: A Systematic Literature Review of Learning Dashboard Research. IEEE Transactions on Learning Technologies, 10(1), 3041.Google Scholar
Sedrakyan, G., Leony, D., Muñoz-Merino, P. J., Kloos, C. D., and Verbert, K. (2017) Evaluating Student-Facing Learning Dashboards of Affective States. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10474 LNCS. New York: Springer, 224237.Google Scholar
Sedrakyan, G., Mannens, E., and Verbert, K. (2019) Guiding the Choice of Learning Dashboard Visualizations: Linking Dashboard Design and Data Visualization Concepts. Journal of Visual Languages and Computing, 50, 1938.Google Scholar
Sedrakyan, G., Malmberg, J., Verbert, K., Järvelä, S., and Kirschner, P. A. (2020) Linking Learning Behaviour Analytics and Learning Science Soncepts: Designing a Learning Analytics Dashboard for Feedback to Support Learning Regulation. Computers in Human Behaviour, 107, 105512.CrossRefGoogle Scholar
Slade, S., Prinsloo, P., and Khalil, M. (2019) Learning Analytics at the Intersections of Student Trust, Disclosure and Benefit. In Proceedings of the 9th International Conference on Learning Analytics & Knowledge. New York: ACM, 235244.Google Scholar
Unankard, S., and Nadee, W. (2020) Topic Detection for Online Course Feedback Using lda. In Popescu, E., Hao, T., Hsu, T.-C., Xie, H., Temperini, M. and Chen, W., eds., Emerging Technologies for Education. Cham. Springer, 133142.Google Scholar
Verbert, K., Duval, E., Klerkx, J., Govaerts, S., and Santos, J. L. (2013) Learning Analytics Dashboard Applications. American Behavioural Scientist, 57(10), 15001509.Google Scholar
Wang, Q., Jing, S., Joyner, D., Wilcox, L., Li, H., Plötz, T., and Disalvo, B.(2020)Sensing Affect to Empower Students: Learner Perspectives on Affect-Sensitive Technology in Large Educational Contexts. In Proceedings of the 7th ACM Conference on Learning @ Scale. New York: ACM, 6376.Google Scholar
West, D., Luzeckyj, A., Searle, B., Toohey, D., Vanderlelie, J., and Bell, K. R. (2020) Perspectives from the Stakeholder: Students’ Views regarding Learning Analytics and Data Collection. Australasian Journal of Educational Technology, 36(6), 7288.Google Scholar

References

Andrade, A., Bevilacqua, G., Casagrande, P., Brandt, R., and Coimbra, D. (2019) Sleep Quality Associated with Mood in Elite Athletes. The Physician and Sportsmedicine, 47(3), 312317. doi.org/10.1080/00913847.2018.1553467.Google Scholar
Andrews, M. (2017) Universities Must Adapt to Constant Change to Thrive. www.universityworldnews.com/post.php?story=20170306203023355.Google Scholar
Arkowitz, H., and Lilienfeld, S. (2010) Why Science Tells Us Not to Rely on Eyewitness Accounts. Scientific American. www.scientificamerican.com/article/do-the-eyes-have-it/.Google Scholar
Arksey, H., and Knight, P. (1999) Interviewing for Social Scientists: An Introductory Resource with Examples. http://public.eblib.com/choice/publicfullrecord.aspx?p=1046475.Google Scholar
Barr, R. B., and Tagg, J. (1995) From Teaching to Learning – A New Paradigm For Undergraduate Education. Change: The Magazine of Higher Learning, 27(6), 1226. doi.org/10.1080/00091383.1995.10544672.CrossRefGoogle Scholar
Bélanger, J. J., Pierro, A., Barbieri, B., De Carlo, N. A., Falco, A., and Kruglanski, A. W. (2015) One Size Doesn’t Fit All: The Influence of Supervisors’ Power Tactics and Subordinates’ Need for Cognitive Closure on Burnout and Stress. European Journal of Work and Organizational Psychology, 25(2), 287300. doi.org/10.1080/1359432x.2015.1061999.Google Scholar
Biasi, V., De Vincenzo, C., and Patrizi, N. (2018) Cognitive Strategies, Motivation to Learning, Levels of Wellbeing and Risk of Drop-Out: An Empirical Longitudinal Study for Qualifying Ongoing University Guidance Services. Journal of Educational and Social Research, 8(2), 7991. doi.org/10.2478/jesr-2018-0019.CrossRefGoogle Scholar
Bowers, J. S. (2016) The Practical and Principled Problems with Educational Neuroscience. Psychological Review, 123(5), 600612. doi.org/10.1037/rev0000025.CrossRefGoogle ScholarPubMed
Brooke, M. S., An, M. H.-S., Kang, E. S.-K., Noble, E. J., Berg, E. K., and Lee, E. J.-M. (2017) Concurrent Validity of Wearable Activity Trackers under Free-Living Conditions. Journal of Strength and Conditioning Research, 31(4), 10971106. doi.org/10.1519/JSC.0000000000001571.Google Scholar
Bruer, J. T. (1997) Education and the Brain: A Bridge Too Far. Educational Researcher, 26(8), 416. doi.org/10.3102/0013189x026008004.Google Scholar
Buckhout, R. (1974) Eyewitness Testimony. Scientific American, 231(6), 2331. www.jstor.org.ezproxy.otago.ac.nz/stable/24950236.Google Scholar
Butson, R., and Sim, K. (2013) The Role of Personal Computers in Undergraduate Education. International Journal of Digital Literacy and Digital Competence, 4(3), 19. doi.org/10.4018/ijdldc.2013070101.Google Scholar
Buysse, D. J., Reynolds Iii, C. F., Monk, T. H., Berman, S. R., and Kupfer, D. J. (1989) The Pittsburgh Sleep Quality Index: A New Instrument for Psychiatric Practice and Research. Psychiatry Research, 28(2), 193213. doi.org/10.1016/0165-1781(89)90047-4.CrossRefGoogle ScholarPubMed
Cohen, L., Manion, L., and Morrison, K. (2011) Research Methods in Education. 7th ed. London: Routledge.Google Scholar
Cvejic, E., Huang, S., and Vollmer-Conna, U. (2018) Can You Snooze Your Way to an ‘A’? Exploring the Complex Relationship between Sleep, Autonomic Activity, Wellbeing and Performance in Medical Students. Australian and New Zealand Journal of Psychiatry, 52(1), 3946. doi.org/10.1177/0004867417716543.CrossRefGoogle Scholar
Danner, S. A., Endert, E., Koster, R. W., and Dunning, A. J. (1979) Stress Parameters during the Doctoral Examination in Internal Medicine. Netherlands Journal of Medicine, 22(2), 57.Google Scholar
De Zambotti, M., Goldstone, A., Claudatos, S., Colrain, I. M., and Baker, F. C. (2018) A Validation Study of Fitbit Charge 2™ Compared with Polysomnography in Adults. Chronobiology International, 35(4), 465476. doi.org/10.1080/07420528.2017.1413578.Google Scholar
Feehan, L. M., Geldman, J., Sayre, E. C., Park, C., Ezzat, A. M., Young Yoo, J., Hamilton, C. B., and Li, L. C. (2018) Accuracy of Fitbit Devices: Systematic Review and Narrative Syntheses of Quantitative Data. JMIR mHealth and uHealth, 6(8), e10527. doi.org/10.2196/10527.Google Scholar
Haynes, C., Bulosan, M., Citty, J. M., Grant-Harris, M., Hudson, J., and Koro-Ljungberg, M. (2012) My World Is Not My Doctoral Program & Or Is It?: Female Students’ Perceptions of Well-Being. International Journal of Doctoral Studies, 7, 117.Google Scholar
Hershner, S., and Chervin, R. (2014) Causes and Consequences of Sleepiness among College Students. Nature and Science of Sleep, 6, 7384. doi.org/10.2147/NSS.S62907.Google Scholar
Hesse-Biber, S. N., and Leavy, P. (2011) The Practice of Qualitative Research. Thousand Oaks: SAGE.Google Scholar
Hill, J. D., and Smith, R. J. H. (2009) Monitoring Stress Levels in Postgraduate Medical Training. Laryngoscope, 119(1), 7578. doi.org/10.1002/lary.20013.Google Scholar
Kearns, H., Gardiner, M., and Marshall, K. (2008) Innovation in PhD Completion: The Hardy Shall Succeed (and Be Happy!). Higher Education Research & Development, 27(1), 7789. doi.org/10.1080/07294360701658781.Google Scholar
Kellehear, A. (1993) The Unobtrusive Researcher: A Guide to Methods. Crows Nest: Allen & Unwin.Google Scholar
Kelly, F., and Brailsford, I. (2013) The Role of the Disciplines: Alternative Methodologies in Higher Education. Higher Education Research & Development, 32(1), 14. doi.org/10.1080/07294360.2012.751864.CrossRefGoogle Scholar
Kelly, W. E. (2003) Worry Content Associated with Decreased Sleep-Length among College Students (Sleep Deprivation Leads to Increased Worrying). College Student Journal, 37(1), 93.Google Scholar
Lagemann, E. C. (2000) An Elusive Science: The Troubling History of Education Research. Chicago: University of Chicago Press.Google Scholar
Lagopoulos, J. (2007) Electrodermal Activity. Acta Neuropsychiatrica, 19(5), 316317. doi.org/10.1111/j.1601-5215.2007.00247.x.Google Scholar
Lee, J.-M., Byun, W., Keill, A., Dinkel, D., and Seo, Y. (2018) Comparison of Wearable Trackers’ Ability to Estimate Sleep. International Journal of Environmental Research and Public Health, 15(6), 1265. doi.org/10.3390/ijerph15061265.CrossRefGoogle ScholarPubMed
Lund, H. G., Reider, B. D., Whiting, A. B., and Prichard, J. R. (2010) Sleep Patterns and Predictors of Disturbed Sleep in a Large Population of College Students. Journal of Adolescent Health, 46(2), 124132. doi.org/10.1016/j.jadohealth.2009.06.016.CrossRefGoogle Scholar
Mackie, S. A., and Bates, G. W. (2019) Contribution of the Doctoral Education Environment to PhD Candidates’ Mental Health Problems: A Scoping Review. Higher Education Research & Development, 38(3), 565578. doi.org/10.1080/07294360.2018.1556620.Google Scholar
Meriläinen, M., and Kuittinen, M. (2014) The Relation between Finnish University Students Perceived Level of Study-Related Burnout, Perceptions of the Teaching and Learning Environment and Perceived Achievement Motivation. Pastoral Care in Education, 32(3), 186196. doi.org/10.1080/02643944.2014.893009.CrossRefGoogle Scholar
Norris, G. (2015) Neuromyths, Neuroeducation and Neurosophisms [video]. https://youtu.be/hE_hl4qXb0I.Google Scholar
Offstein, E., Larson, M., McNeill, A., and Mwale, H. (2004) Are We Doing Enough for Today’s Graduate Student? International Journal of Educational Management, 18, 396407. doi.org/10.1108/09513540410563103.Google Scholar
Peach, H. D., Gaultney, J. F., and Ruggiero, A. R. (2018) Direct and Indirect Associations of Sleep Knowledge and Attitudes with Objective and Subjective Sleep Duration and Quality via Sleep Hygiene. Journal of Primary Prevention, 39(6), 555570. doi.org/10.1007/s10935–018-0526-7.Google Scholar
Ranasinghe, A. N., Gayathri, R., and Vishnu Priya, V. (2018) Awareness of Effects of Sleep Deprivation among College Students. Drug Invention Today, 10(9), 18061809. www.scopus.com/inward/record.uri?eid=2-s2.0-85051572624&partnerID=40&md5=ce555cac0f0c1b2cfe04068429739c96.Google Scholar
Rios-Aguilar, C. (2014) The Changing Context of Critical Quantitative Inquiry. New Directions for Institutional Research, 2013(158), 95107. doi.org/10.1002/ir.20048.CrossRefGoogle Scholar
Roenneberg, T., Zerbini, G., and Winnebeck, E. (2019) Chronotype and Social Jetlag: A (Self-) Critical Review. Biology, 8(3), 54. doi.org/10.3390/biology8030054.CrossRefGoogle ScholarPubMed
Routledge, C. (2017) Why Social Scientists Should Not Particpate in the March for Science. Quillete. https://quillette.com/2017/03/03/why-social-scientists-should-not-participate-in-the-march-for-science/.Google Scholar
Scullin, M. K. (2019) The Eight Hour Sleep Challenge during Final Exams Week. Teaching of Psychology, 46(1), 5563. doi.org/10.1177/0098628318816142.Google Scholar
Scutt, C., and Hobson, J. (2013) The Stories We Need: Anthropology, Philosophy, Narrative and Higher Education Research. Higher Education Research & Development, 32(1), 1729. doi.org/10.1080/07294360.2012.751088.CrossRefGoogle Scholar
Suhlmann, M., Sassenberg, K., Nagengast, B., and Trautwein, U. (2018) Belonging Mediates Effects of Student-University Fit on Well-Being, Motivation, and Dropout Intention. Social Psychology, 49(1), 1628. doi.org/10.1027/1864-9335/a000325.Google Scholar
Summers, L. (2017) Larry Summers on Macroeconomics, Mentorship, and Avoiding Complacency. https://medium.com/conversations-with-tyler/tyler-cowen-larry-summers-blog-secular-stagnation-twitter-421a69ed84c8.Google Scholar
Svensson, T., Chung, U. I., Tokuno, S., Nakamura, M., and Svensson, A. K. (2019) A Validation Study of a Consumer Wearable Sleep Tracker Compared to a Portable EEG System in Naturalistic Conditions. Journal of Psychosomatic Research, 126, 109822. doi.org/10.1016/j.jpsychores.2019.109822.CrossRefGoogle ScholarPubMed
Thacher, P. V. (2008) University Students and ‘The All Nighter’: Correlates and Patterns of Students’ Engagement in a Single Night of Total Sleep Deprivation. Behavioral Sleep Medicine, 6(1), 1631. doi.org/10.1080/15402000701796114.CrossRefGoogle Scholar
Thomas, G. (2012) Changing Our Landscape of Inquiry for a New Science of Education. Harvard Educational Review, 82(1), 2651. doi.org/10.17763/haer.82.1.6t2r089l715x3377.Google Scholar
Tight, M. (2013) Discipline and Methodology in Higher Education Research. Higher Education Research & Development, 32(1), 136151. doi.org/10.1080/07294360.2012.750275.Google Scholar
Waghachavare, V. B., Dhumale, G. B., Kadam, Y. R., and Gore, A. D. (2013) A Study of Stress among Students of Professional Colleges from an Urban Area in India. Sultan Qaboos University Medical Journal, 13(3), 429436. www.ncbi.nlm.nih.gov/pmc/articles/PMC3749028/; www.ncbi.nlm.nih.gov/pmc/articles/PMC3749028/pdf/squmj1303-429-436.pdf.Google Scholar
Wells, R., Kolek, E., Williams, E., and Saunders, D. (2015) ‘How We Know What We Know’: A Systematic Comparison of Research Methods Employed in Higher Education Journals, 1996–2000 v. 2006–2010. The Journal of Higher Education, 86, 171198. doi.org/10.1353/jhe.2015.0006.Google Scholar
Wing, Y. K., Chan, N. Y., Yu, M. W. M., Lam, S. P., Zhang, J., Li, S. X., Kong, A. P. S., and Li, A. M. (2015. A School-Based Sleep Education Program for Adolescents: A Cluster Randomized Trial. Pediatrics, 135(3), e635e643. doi.org/10.1542/peds.2014-2419Google Scholar
Woodgate, P. (2017) Universities Must Innovate to Adapt and Succeed. Times Higher Education (THE). www.timeshighereducation.com/hub/pa-consulting/p/universities-must-innovate-adapt-and-succeed.Google Scholar

References

Agrawal, A., Gans, J., and Goldfarb, A. (2019) The Economics of Artificial Intelligence: An Agenda. Chicago: University of Chicago Press.CrossRefGoogle Scholar
AI Council (2021) UK AI Council: AI Roadmap. Office for Artificial Intelligence, Department for Business, Energy & Industrial Strategy, and Department for Digital, Culture, Media & Sport.Google Scholar
Aoun, J. E. (2017) Robot-Proof: Higher Education in the Age of Artificial Intelligence. Cambridge, MA: MIT Press.Google Scholar
Autor, D. H. (2018) The Shifts – Great and Small – in Workplace Automation. In Michelman, P., ed., What the Digital Future Holds: 20 Groundbreaking Essays on How Technology Is Reshaping the Practice of Management, chapter 7.CrossRefGoogle Scholar
Bothewell, E. (2020) Amsterdam’s AI lab Seen as Test Case for Huawei Collaboration. Times Higher Education. www.timeshighereducation.com/news/amsterdams-ai-lab-seen-test-case-huawei-collaboration.Google Scholar
Bruder, J. (2017) Infrastructural Intelligence: Contemporary Entanglements between Neuroscience and AI. Progress in Brain Research, 233, 101128.Google Scholar
Cath, C. (2018) Governing Artificial Intelligence: Ethical, Legal and Technical Opportunities and Challenges. Philosophical Transactions. Series A, Mathematical, Physical, and Engineering Sciences, 376(2133). doi: 10.1098/rsta.2018.0080.Google Scholar
Centre for Strategy & Evaluation Services (2020) Towards a 2030 Vision on the Future of Universities in Europe Policy Report. doi: 10.2777/510530.Google Scholar
Elbanna, A., and Engesmo, J. (2020) A-Level Results: Why Algorithms Get Things So Wrong – and What We Dan Do to Fix Them. Parenting for a Digital Future, 2. http://eprints.lse.ac.uk/106894/.Google Scholar
European Commission (2020) White Paper on Artificial Intelligence: A European Approach to Excellence and Trust. https://ec.europa.eu/info/files/white-paper-artificial-intelligence-european-approach-excellence-and-trust_en.Google Scholar
Fa, G. (2017) State Council Notice on the Issuance of the Next Generation Artificial Intelligence Development Plan. State Department, China.Google Scholar
Goldfarb, A., Gans, J., and Agrawal, A. (2019) The Economics of Artificial Intelligence: An Agenda. https://milgrom.people.stanford.edu/sites/g/files/sbiybj4391/f/the_economics_of_artificial_intelligence_-_chapter_23_0.pdf.Google Scholar
Goodfellow, I. et al. (2014) Generative Adversarial Nets. In Ghahramani, Z. et al., eds., Advances in Neural Information Processing Systems. Red Hook: Curran Associates, pp. 26722680.Google Scholar
Hall, W., and Pesenti, J. (2018) Industrial Strategy Artificial Intelligence Sector Deal. HM Government, UK. https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/702810/180425_BEIS_AI_Sector_Deal__4_.%20Pdf.Google Scholar
Hall, W., and Pesenti, J. (2017) Growing the Artificial Intelligence Industry in the UK. Department for Digital, Culture, Media & Sport and Department for Business, Energy & Industrial Strategy. Part of the Industrial Strategy UK and the Commonwealth. http://ftp.shujuju.cn/platform/file/2017-10-18/782c432045784854a04e458976aef0bf.pdf.Google Scholar
Kaplan, A. (2021) Higher Education at the Crossroads of Disruption: The University of the 21st Century. Bingley: Emerald.Google Scholar
LeCun, Y., Bengio, Y., and Hinton, G. (2015) Deep Learning. Nature, 521(7553), 436444.CrossRefGoogle ScholarPubMed
Litan, R. E., and Rivlin, A. M. (2001) Projecting the Economic Impact of the Internet. The American Economic Review, 91(2), 313317.Google Scholar
McCarthy, J., Shannon, C., and Minsky, M. (1955) A Proposal for the Dartmouth Summer Research Project on Artificial Intelligence. Stanford University.Google Scholar
Nilsson, N. J. (2009) The Quest for Artificial Intelligence: A History of Ideas and Achievements. Cambridge: Cambridge University Press.Google Scholar
OSTP (2016) The National Artificial Intelligence Research and Development Strategic Plan. US Government. www.nitrd.gov/pubs/national_ai_rd_strategic_plan.pdf.Google Scholar
Reuters (2020) Google and the University of Oxford Agree Extension in Support for Digital News Project to August 2020. Reuters Institute. https://reutersinstitute.politics.ox.ac.uk/risj-review/google-and-university-oxford-agree-extension-support-digital-news-project-august-2020.Google Scholar
Rumelhart, D. E., Hinton, G. E., and Williams, R. J. (1985) Learning Internal Representations by Error Propagation. https://apps.dtic.mil/sti/citations/ADA164453.Google Scholar
Shah, H. (2011) Turing’s Misunderstood Imitation Game and IBM’s Watson Success. Keynote in 2nd towards a Comprehensive Intelligence test (TCIT) symposium at AISB. www.academia.edu/download/12576969/HShah_TCIT2011_York.pdf.Google Scholar
Shumin, L. (2019) Facebook, Alibaba Team Up on PyTorch, Machine Learning in the Cloud, YiCai Global. www.yicaiglobal.com/news/facebook-alibaba-team-on-pytorch-machine-learning-in-cloud-to-expand-ecosystems.Google Scholar
ITV (2021) The Chancellor’s Budget.Google Scholar
Turing, A. M. (1950) ‘Mind’, Mind; a Quarterly Review of Psychology and Philosophy, 59(236), 433460.Google Scholar
US Government (2019) The National Artificial Intelligence Research and Development Strategic Plan: 2019 Update.Google Scholar

Save book to Kindle

To save this book to your Kindle, first ensure [email protected] is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

Available formats
×