Antifragility and covid-19

Five years ago, I wrote about Nassim Taleb’s book, Antifragility.  The book built on the ideas developed in Fooled by Randomness and Black Swan.  On 26 January 2020, before its outbreak in the West, he wrote a prescient warning about covid-19.

Taleb writes about probability and how humans fail to correctly estimate probabilities, especially the very low probabilities at the tails of non-linear distributions (“fat tails”).  People wrongly tend to extrapolate from a normal situation.  Taleb coined the terms “black swans” (highly rare and unforeseeable events) and “white swans” (highly rare but foreseeable events) to describe such low-probable but highly consequential events.

Landscapes are mostly the result of extreme events in the past (volcano eruptions, tsunamis, ice ages…), much more than the daily grind of erosion and sedimentation.  Similarly, black and white swan events have an outsized influence on our politics, economies and societies.

The concept of antifragility is related to resilience and robustness, but the difference is that antifragile systems become stronger under pressure, like bones or muscles become stronger when they are tested.

The shock of the pandemic has exposed the increased fragility of the western world: tightly integrated global networks and supply chains and a reliance on China for key resources such as pharmaceutical ingredients.  This means that risks such as pathogens, computer viruses, cyber attacks or reckless budgetary management by companies and central banks are likely to cause global rather than local shocks.

Africa’s relative exclusion from global supply networks has so far spared it from the worst effects on the pandemic.

Taleb advocates for more antifragility in the system. “That’s why nature gave us two kidneys.”  Antifragile systems are characterised by optionality, redundancy and variability.  They contain the institutional equivalent of “circuit breakers, fail-safe protocols, and backup systems.

Some elements of such antifragile systems are:

  • distribution of power among smaller, more local, experimental, and self-sufficient entities
  • no monopolies or centralized bureaucracies (against increasing trends towards winner-takes-all markets and data centralisation)
  • public but decentralized health insurance, (universal) basic income or temporary unemployment benefits (bailing out individuals, not companies).
  • “just in case” instead of “just in time” organizational models
  • more cash reserves and less debt (for companies, households and governments)
  • having a safety net of loyal, well-trained and adaptable full-time employees (rather than relying on free-lancers, consultants and gig workers)

Antifragile systems are not necessarily big government systems. People and governments tend to over-intervene on small things and under-intervene for large things.  Governments should plan and provide buffers for extremesThis is especially true in areas with high uncertainty like climate change.  Not the median value of expected temperature rise is so much important, rather than the ‘fat tail’ of improbable but potentially devastating temperature rises.

A way to make systems more antifragile is to make sure that those responsible have skin in the game:

“In the Hammurabi Code, if a house falls in and kills you, the architect is put to death”.  Correspondingly, any company or bank that gets a bailout should expect its executives to be fired, and its shareholders diluted. “If the state helps you, then taxpayers own you.”

For companies, skin in the game means letting the process of creative disruption play out.  If airlines used their cashflow in the good times to buy back shares instead of building reserves, why should they be bailed out?  Governments could be made more responsible by linking salaries and incentives of politicians to debt levels, economic growth, poverty levels and well-being of the population.

Will covid-19 have a permanent impact and lead to more antifragile systems?

Memories are short.  Additional expenses in hospital beds and strategic stocks of materials become harder to justify as memories fade. There will heightened attention for pandemic risks for some time now.  But what about other systemic risks?  The systemic risk of inflation and a corporate and government debt crisis is not high on the agenda now.  Gone are calls for increased expenses in cybersecurity or counterterrorism. Attention for climate change mitigation has waned.

There is an increased focus on making supply chains more resilient, but this trend started before covid-19.  This includes shortening them, reducing reliance on a few suppliers and increase investments in robotics.  The nature of work is changing with more decentralization and distance work, but with a higher reliance on the internet.  The covid-19 pandemic risks increasing the power of global corporations due to their better ability to raise free cash and lobbying powers.

What about education?  Covid-19 has arguably spurred more innovation and investment in online learning than in the last 5 years.  But will it last and will it make education systems more antifragile?

A basic problem in the organisation of education is low productivity increases compared to other sectors, a phenomenon referred to as Baumol’s Disease.  A lack of relative productivity growth explains why salaries for educators struggle to keep up with those of other sectors and why pedagogical innovations such as differentiation, project-based learning and personalized learning struggle to find inroads.

The only plausible way to increase productivity in education is by online learning.  By digitizing parts of the education process (explaining theory, showing examples, elaborating on topics, evaluation), time is freed for educators to focus on social support, individual learning support and supporting group activities. Making educators responsible for larger groups of learners allows for increasing their salaries.

Will covid-19 have a lasting influence on education?  Many schools and teachers have experimented and developed skills with online learning. Many may continue developing instructional movies and online Q&A sessions.  However, a real education reform would include moving away from year grades and fixed classes and a specialisation in teacher roles .  Schools have a lot of autonomy in Flanders to experiment with online learning and the organisation of education. If covid-19 can result in lasting innovations in the organisation of education in our schools, it will have had a positive impact after all.

 

 

The rise of Homo deus and the impact on education

Homo Deus: A Brief History of Tomorrow by Yuval Noah Harari, a Professor of History at Hebrew University, Jerusalem, is a thought-provoking book that builds on his previous book, Sapiens.  In Sapiens, Harari described how humans have been successful through our ability to cooperate in large groups, helped by fictional stories like religions and ideologies.  As a result, the challenges of famine, plague and war have largely been reigned in.  In 2020, more people will die from obesity than from covid-19. For most people, McDonald’s and Coca Cola pose a large greater threat to their health than any virus.

Artificial intelligence, made possible through breakthroughs in biology and information technology, is turning our focus to the challenges of achieving bliss, immortality and divinity.  The title Homo deus refers to humankind taking up the powers to create humans and animals according to our wishes, traditionally the preserve of gods.

This has profound implications on our lives. Our humanistic world view changes to an algorithmic world view.  Humanism says that human feelings and our free will are the highest authority in the world. However, stories about free will are likely to be increasingly challenged, because human feelings are just biochemical algorithms shaped by millions of years of evolution.  As we gain more understanding of these algorithms, we will understand people much better than we can understand ourselves.

This integration of computer science and life sciences lead to the uncoupling of intelligence and consciousness. They are coupled in humans, but as we develop artificial cognitive and emotional intelligence, the two will be increasingly separated. We are not going to build machines any time soon that have feelings like us: that’s consciousness.  But we have already built biochemical sensors, machines and data-processing networks that can record our feelings better than we know them ourselves: that’s emotional intelligence.

Humans can’t handle these massive amounts of data generated by biochemical sensors.   So gradually, more and more decisions and responsibilities will be transferred to machines.

Harari calls this trust in the power of data and algorithms dataism (after theism and humanism). Its followers put their faith in information as the highest source of value. As is often the case, medicine is a frontrunner.  Progress in genetics and biotech will make that decisions about our health will be increasingly be made by algorithms.  An early example is the decision by Angelina Jolie to undergo a double mastectomy.

However, we are likely to see a similar shift in authority in other fields. For example, in online learning, data are collected on how long each learner spends on each page, which exercises are done successfully, and which cause problems.  Through learning analytics, we can assess very accurately which students are at risk of dropping out and offer them additional support.  In the next step, which is already possible, online learning will be connected with students’ senses so instructional designers can see where students are really interested and where their attention slips.  In the near future, their learning will be connected to internal sensors, measuring blood pressure, eye movements, heart rate etc.  As a result, a learning provider will be able to see exactly where a student got excited, angry, bored etc.  At the end of a course, the course provider will know the student much better than the student knows him/herself.  Companies will be able to measure the emotional impact of everything you read or watch.  They will know exactly who you are and how to press your emotional buttons.  For now, a teacher or professor still makes the decision to pass or fail the student, but how long still?  If algorithms can get a much better insight in what a student has done and learned, why would you still need a teacher?  Authority in education will shift from individual humans to networked algorithms.

However, access to this is bound to be unequal.  There will be a small super elite with access to the master algorithms and the corporate profits.  The rest of humanity will simply be tools in their vast schemes. The result of this might be that, in the 21st century, most men and women will lose their economic value. Many may find themselves in a new class of useless people. We will need less and less need for people to work in the fields, work in factories and offices or fight in wars.  People will struggle to find purpose in life, as work, parenting and entertainment are regulated by AI.  Will people be happy and how will they find purpose in life?  Might drugs, entertainment and computer games be used to keep people in an eternal bliss?  Chris Andrade’s Dignity offers a glimpse into what it means for communities to lose their sense of purpose, that was traditionally been provided by work, church and family.  This rising socio-economic inequality will gradually translate into biological inequality, as this class of super rich people will have the means to upgrade themselves via genetic engineering and biological implants. This evolution risks making inequality within and between societies permanent, as the traditional ways of bridging the gap (cheap labour, education) risk becoming less relevant.

Does it necessarily follow that AI will take over?  Can we stop this evolution?

Healing and safety are the initial justification for every upgrade.  You can’t be against being healthy or being safe, if not for you, for your loved ones.

Find some professors experimenting in genetic engineering or brain–computer interfaces and ask them why they are engaged in such research. Likely they would reply that they are doing it to cure disease. ‘With the help of genetic engineering,’ they would explain, ‘we could defeat cancer. And if we could connect brains and computers directly, we could cure schizophrenia.’ Maybe, but it will surely not end there.

Secondly, in the days of European imperialism, conquistadors and merchants bought entire islands and countries in exchange for some coloured beads. As observers of the tech industry like Jaron Lanier have been pointing out for many years, in the twenty-first century our personal data is probably the most valuable resource most humans have to offer, and we are giving it to the tech giants in exchange for email services and funny cat videos. Most people have no clue of the value of their data and sleepwalk into the era of dataism.

Thirdly, most important evolutions in our society are not the result of elections.  Nobody voted about the structure of the internet. Similarly, decisions about AI are made by a small group of people.  Technology is not deterministic.  Technological inventions can be used to make a democratic, communist or fascist system, but they are unlikely to be stopped.

What are some of the implications of Homo deus for our education systems?

  1. Inequality is likely to rise. Education for all is a critical counterweight to develop a well-informed and critical mass of citizens that can make sense of the evolutions in our society.
  2. The merger of ICT and biology. STEM subjects will rise in importance. Scientific literacy and basic programming skills are absolutely crucial to understand our society and stay relevant.
  3. The pace of change is accelerating. People need to be trained and re-trained regularly and will need to be able to cope with the uncertainty and have the mental flexibility to re-design themselves.
  4. Many jobs will disappear and might be replaced by new jobs. We mustn’t be complacent and assume that, because previous revolutions did not lead to massive job losses, that it will the same now. Education systems should regularly analyze how our society is evolving and be much bolder in designing curricula.
  5. The separation between a school and work life is blurring. It becomes increasingly ridiculous to separate people’s lives into a school life and a work life. It makes much more sense to have brief periods of intensive learning, followed by internships and jobs, followed by sabbaticals and new periods of learning.

#H809 Research on MOOCs

credit: Freedigitalphotos

credit: Freedigitalphotos

Week 12 in the H809 course and MOOCs – the official educational buzzword of 2012 – couldn’t remain absent.  The focus in this course is not so much on what MOOCs are, their history and the different types with their various underlying pedagogies and ideologies.  I blogged on MOOCs before, as a participant in LAK11, a connectivist MOOC on learning analytics.  In H809 the focus lies on issues such as:

  • What kind of information and research is available on MOOCs?
  • What kind of MOOC research would be interesting to do?
  • What are benefits and limitations of the type of information on MOOCs that is around?
  • What is the educational impact (rather than the press impact) of MOOCs?

Much information on MOOCs consists of the so-called grey literature.  Main information sources include:

  • blogs from practitioners and academics, with an overrepresentation of academics from Athabasca Un. and the OU.
  • blogs from participants in MOOCs, sharing their experiences
  • articles in open academic journals such as IRRODL, EURODL, Open Praxis
  • articles in more popular education magazines such as Inside Higher Education and The Chronicle of HE.
  • articles in the general press such as The Economist and The New York Times

Some comments on these sources:

  1. The term ‘grey literature’ may sound a bit disparagingly.  However, as Martin Weller writes, notions of scholarship and  academic publishing are evolving.  Blogs and open journals constitute alternative forms of scholarship with more interaction, less formality and shorter ‘turnaround’ times.
  2. Information and research on MOOCs is heavily Anglo-Saxon centred (or perhaps better Silicon Valley-centred?).  I couldn’t hardly find any articles on MOOCs in Dutch, although that might not be so surprising.  Although MOOCs (xMOOCs) are often touted as a ‘solution’ for developing countries, there are few perspectives from researchers from developing countries.  As Mike Trucano writes on the EdTech blog from the World Bank:

    “Public discussions around MOOCs have tended to represent viewpoints and interests of elite institutions in rich, industrialized countries (notably the United States) — with a presumption in many cases that such viewpoints and interests are shared by those in other places.”

  3. It’s interesting to see how many of the more general news sources seem to have ‘discovered’ MOOCs only after the Stanford AI course and the subsequent influx of venture capital in start-ups such as Coursera, Udacity and edX.  The ‘original’ connectivist MOOCs, that have been around since 2008, let alone open universities are hardly mentioned in those overviews.  A welcome exception is the Open Praxis paper from Peter and Deimann that discusses historical manifestations of openness such as the coffee houses in the 17th century.
  4. The advantage of this grey literature is that it fosters a tremendously rich discussion on the topic. Blog posts spark other blog posts and follow-up posts. Course reflections are online immediately after the course. Events such as a failing Coursera MOOC or an OU MOOC initiative get covered extensively from all angles. This kind of fertile academic discussion can hardly be imagined with the closed peer-review publication system.
  5. The flipside of this coin is that there are a lot of opinions around, a lot of thinly-disguised commercialism and a lot of plain factual mistakes (TED talks!).  MOOCs may be heading for a ‘trough of disappointment’ in Gartner’s hype cycle.  Rigorous research would still be valuable.  For example, most research is descriptive rather than experimental and is based on ridiculously small samples collected in a short time.  Interrater reliability may be a problem in much MOOC research .  Longitudinal studies that investigate how conversations and interactions evolve over time are absent.
  6. Sir John Daniel’s report ‘Making Sense of MOOCs‘ offers a well-rounded and dispassionate overview of MOOCs until September 2012.

Interesting research questions for research on MOOCs could be:

  • What constitutes success in a MOOC for various learners?
  • How do learners interact in a MOOC? Are there different stages?  Is there community or rather network formation? Do cMOOCs really operate according to connectivist principles?
  • What are experiences from MOOC participants and perspectives of educational stakeholders (acreditation agencies, senior officials, university leaders) in developing countries?
  • Why do people choose not to participate in a MOOC and still prefer expensive courses at brick-and-mortar institutions?
  • What factors inhibit or enhance the learning experience within a MOOC?
  • How to design activities within a MOCO that foster conversation without causing information overload?
  • How do MOOCs affect hosting institutions (e.g. instructor credibility and reputation) and what power relations and decision mechanisms are at play (plenty of scope for an activity theoretical perspective here).

A few comments:

  • High drop-out rates in MOOCs have caught a lot of attention.  Opinions are divided whether this is a problem or not.  As they are free, the barrier to sign up is much lower.  Moreover, people may have various goals and may just be interested in a few parts of the MOOC.
  • MOOCs (at least the cMOOCs) are by its nature decentralized, stimulating participants to create artefacts using their own tools and networks, rather than a central LMS.  cMOOCs remain accessible online and lack the clear start and beginning of traditional courses. This complicates data collection and research.
  • Although MOOCs are frequently heralded as a solution for higher education in developing countries, it would be interesting to read accounts from learners from developing countries for whom a MOOC actually was a serious alternative to formal education. The fact that MOOCs are not eligible for credits (at the hosting institution) plays a role, as well as cultural factors, such as a prevalent teacher-centred view on education in Asian countries.

References

Overview of posts on MOOCs from Stephen Downes: http://www.downes.ca/mooc_posts.htm

Overview of posts on MOOCs from George Siemens: https://www.diigo.com/user/gsiemens/mooc

OpenPraxis theme issue on Openness in HE: http://www.openpraxis.org/index.php/OpenPraxis/issue/view/2/showToc

IRRODL theme issue on Connectivism, and the design and delivery of social networked learning: http://www.irrodl.org/index.php/irrodl/issue/view/44

Armstrong, L. (2012) ‘Coursera and MITx – sustaining or disruptive? – Changing Higher Education’,

Peter, S. and Deimann, M. (2013) ‘On the role of openness in education: A historical reconstruction’, Open Praxis, 5(1), pp. 7–14.
Daniel, J. (2012) ‘Making sense of MOOCs: Musings in a maze of myth, paradox and possibility’, Journal of Interactive Media in Education, 3, [online] Available from: http://www-jime.open.ac.uk/jime/article/viewArticle/2012-18/html

2012, Year of the MOOC?

funny-farm-animals-04In various places (such as the New York Times) 2012 has been heralded as the year of the Massive Open & Online Course, also called MOOC.  Although MOOCs have been around since 2008 or so, developed by researchers like Stephen Downes, David Courmier, George Siemens, Jim Groom and others.

“In the summer of 2008 I invited George Siemens and Stephen Downes to come to edtechtalk and tell us about the new course they were teaching. They had 25 people registered (paid), at the university of Manitoba, but they had opened the class for online registration to whomever wanted to come along. Hundreds (and then a couple thousand) people took them up on it. We started talking about what it meant to have lots and lots of people learning together… somewhere in there, i called them a massive open online course… for which i have been often chastised :)” (from Dave Courmier’s blog)

They are based on a connectivist pedagogy, characterised by distributed content, network formation, creation of artefacts outside course-related structures and superfluous course boundaries.  MOOCs based on these principles are often dubbed cMOOCs, to distinguish them from their less salubrious nephews.

The main in change in 2012 has been the entering of Ivy League institutions in the MOOC fray.  As OU vice-chancellor Martin Bean notes, the arrival of great brands, lots of (venture capital) money has vastly increased the forces of disruption.  The entrance of Silicon Valley in MOOCs has been spearheaded by Coursera, Udacity (both offshoots from an open Artificial Intelligence course at Stanford University) and edX (grown from MITx after investment and participation from Harvard University and UC Berkeley).  Online courses from these providers routinely attract tens of thousands of people (although drop-out rates are stratospheric).  Mass media have picked up the phenomenon (New York Times, The Economist, Financial Times).  Coursera has been gradually expanding its offer to non-US universities and currently offers more than 200 courses from 62 universities and 14 countries, including France, The Netherlands, Hong Kong and Italy (no, not from Belgium yet, no surprises there).  It’s interesting to note that these institutions have largely missed out the evolution to online learning so far and their  Silicon-centredness and lack of regard for 40 years of research in distance and online learning has been derided by researchers.  In the UK the Open University (OU) has recently announced its own MOOC platform, Futurelearn – here is a worthwhile reflection from OU researcher Doug Clow – and in March a ‘regular’ online course (h817) will be offered partly as a free open course.

Dubbing 2012 the ‘year of the MOOC’ may seem condescending to institutions and researchers who have been active on the topic for years.  However, there’s no denying in the worldwide appeal of the Ivy League institutions and their disruptive power.  Many challenges remain, in terms of business models, learner interaction, accreditation and quality.  It will be interesting to watch if also this disruptive innovation, like radio and television before, will evolve from an open, bottom-up structure full of creativity towards a commercialised and closed system, as described so beautifully by Tim Wu in ‘The Master Swith’ (blog post on the book).

#H807 E-learning Models and their Implications for Activity Design

Copyright: Oliver Merkel

With the submission of TMA03 the focus in H807 shifts to the design of e-tivities (Salmon, 2000).  The ultimate block starts with a study of the theoretical foundations that underpin activity design explicitly or, more often, implicitly, as pedagogic assumptions.  The key text is a review of e-learning theories by Mayes and de Freitas (2004), complemented by e-books from Terry Anderson (2008) and Peter Goodyear (2001).

E-learning theories are not new theories, but rather e-enhancements of existing learning theories (Mayes and de Freitas, 2004).  They form “sets of beliefs: about the nature of knowledge and competence, about the purposes of learning, about how learning occurs, about how people should and should not be treated, etc” (Goodyear, 2001, p.51)

Consecutive learning theories don’t replace, but rather complement each other, each contributing its legacy to learning.  Theories are situated at various levels of aggregation, with associative/behaviourist approaches addressing observable factors, cognitive approaches focusing on the ‘detailed structures and processes that underlie individual performance’ and situative approaches taking into account the social and cultural aspects of learning (Mayes and de Freitas, 2004).

Activity designs are usually a blend of different learning theories.  Being aware of the main learning theories helps building a consistent design and clarifying what type of learning and interaction is intended. An example provided by Goodyear (2001):

It is not uncommon to find some members of a team believing that learners are poor at organizing themselves and learn best by being fed information in small amounts, while other members of the team want to promote active, student-managed learning.

The table below summarizes key concepts of different learning theories and their implications for online learning, taken from the publications from Anderson, Mayes and de Freitas and Goodyear.

Associative/ Behaviourist approaches Design principles
Looking for observable behaviour Explicitly mentioning course outcomes
Behavioural objectives Ability to test achievement of learning outcomes
Instructional Systems Design (ISD) Decomposing learning into small chunks
Routines of organised activity
Learning hierarchies (controversial!) Sequencing learning materials with increasing complexity
Giving direct feedback on learning
Individualized learning trajectories
Cognitive psychology (constructivism)
Types of memory (sensory – short term – long term) Maximize sensations: strategic screen layout
Research on memory, perception, reasoning, concept formation. Maximize sensations: well-paced information
Learning is active Maximize sensations: highlighting main elements
Learning is individual (knowledge construction) Relate difficulty level to cognitive level of learner: providing links to easier and more advanced resources
Use of comparative advance organizers
Use of conceptual models
Importance of prior knowledge structures Pre-instructional & prerequisite questions
Experimentation toward discovery of broad principles
Promote deep processing Use of information maps zooming in/ out
Cognitive Apprenticeship (Brown et al, 1989) Interactive environments for construction of understanding
Metacognition (reflection, self-regulation) Relate to real-life (apply, analyse, synthesize)
Learning styles (controversial!) Address various learning styles
Cognitive styles Let students prepare a journal
Dual coding theory Use both visual information and text
Motivate learners (ARCS model) Use techniques to catch attention, explain relevance,  build confidence and increase satisfaction
Situated learning (constructivism)
Personal knowledge construction Personal meaning to learning
Situated learning: motivation Relate to real life (relevance)
Holistic/ Systemic approaches Conduct research on internet
Build confidence with learners
Identity development Use of first-hand information (not filtered by instructor)
Communities of Practice (Lave & Wenger) Collaborative activities
Zone of Proximal Development (Vygotsky) Fostering the growth of learning communities
Learning as act of participation Legitimate (peripheral) practice, apprenticeships
Lifelong learning Authentic learning and assessment tasks
 Connectivism
Information explosion Digital literacies
Learning in network environment Keep up-to-date in field
Knowledge base Multi-channel learning
Distributed learning Build diversity, openness in learning (different opinions), autonomy
Personal Learning Environment  self-directed learning, just-in-time

Some comments on the table:

1. It’s difficult to draw sharp lines between these theories.  Some authors distinguish between cognitive constructivism (based on the work from Piaget) and social-cultural constructivism (based on the work from Vygotsky).  The work of Vygotsky formed the basis for the anthropological work from Jean Lave and the concept of ‘communities of practice’. The work of Engeström on activity theory forms a bridge between situative learning (with the activity system, it takes a more social unit of analysis than the individual) and constructivist approaches.

2 .Constructivism doesn’t really fit into the overview.  Goodyear (2001, p.75) mentions the following description of constructivism:

“…learning is a constructive process in which the learner is building an internal representation of knowledge, a personal interpretation of experience. This representation is constantly open to change, its structure and linkages forming the foundation to which other knowledge structures are appended….this view of knowledge does not necessarily deny the existence of the real world..but contends that all we know of the world are human interpretations of our experience of the world….learning must be situated in a rich context, reflective of real world contexts…” In other words, constructivism states that knowledge is relative and is different for every user.  Learning, in this position, means actively building a personal and contextualised interpretation of experience.

References