In my experience, it’s the small tributaries of the river, the overlooked pockets, and the unexpected that offer the most value. Whether you’re a traveller, a student, an explorer, a researcher, or an investigator, what is fresh, what’s genuine and what is original, is the stuff outside the mainstream and off the beaten track. Another aid is in joining up our unexpected insights from one ‘tributary’ with those of another. And by holding two opposing ideas or concepts in your head (as a traveller, reflecting on what you see through local values and through your own cultural values is an example of this). In some ways, stating all this is blindingly obvious, but in others, it’s revealing a pathway to the sublime & subtle.
We make progress as a species, as a culture and as individuals, by pushing our buttons. By pushing our boundaries, making improvements and gathering new insights. So far, we’ve done this faster than any other species, except perhaps viruses. And it’s been high-growth-off-a-high-base too.
Is human love more advanced than the love shown in other species? It’s hard for us to see, even when as researchers and nature filmers, we’re looking hard. The love an animal mother shows for its offspring, given its mental and sensory capabilities, is probably just as valid as human love for other people, given our own mental and sensory capabilities. And arguably, we’re more prone to cruelty and indifference than other species too. Especially since our awareness of the World (and the Universe) is so much greater.
Finally, is it wrong to let our children get bored? On the list of wrongness towards children, I doubt it figures in the top ten, although you may disagree. However, given the direction the World is going, we’re going to need to maximise human creativity like never before.
Like for many things, the earlier you start, the more proficient you can become. Perhaps already, we provide:
-too much of too few types of entertainment and
-entertainment without mental challenge,
to the younger generations (and ourselves). As an aside, we arguably produce too much content that simply feeds our basic emotions and prejudices too.
Technology that encourages people:
-to screen out the complexities of life that we should not ignore,
-to screen out the information we need, to make informed decisions with, as parents, as voters and as citizens,
isn’t something to be applauded and worshipped. Instead, we should be critical of it and demand better. All of us, including our kids need to become those critics.
Premium quality universities may preserve blended learning (and blended research) techniques for their creative interaction value. Meanwhile, Massively Open Online Course (MOOC) providers are likely to try to emulate pure digital retailers, offering their client-base modular learning products for personal up-skilling and continuous professional development (CPD).
Should premium quality universities partner with MOOC providers to offer premium university-branded online learning modules and what kind of demand might emerge?
Regardless of how fast the ‘long tail of higher education demand’ emerges, represented incidentally by supply not just demand, student demand for elite undergrad and graduate programmes will likely remain strong. It’s perceived value comes in helping those students differentiate themselves in the workplace and use premium university content to aid workplace performance.
However, as MOOC providers ‘fatten’ the thickness of the long tail by progressively offering affordable, modularised courses, globally accessible, in multiple languages and able to be studied at a time convenient to the student, premium quality STEM universities need to think more about the post- qualification needs of engineers, doctors and scientists for continuous professional development. Premium STEM universities would also be wise to think about how much of that emerging demand to capture themselves. The set of post-qualification needs could be represented in two dimensions; career seniority skills and career breadth skills.
The faster new professional fields emerge due to global innovation, the harder it becomes for any employee (highly talented or not) to plot a linear career progression that preserves their marketability (embrace sufficient career breadth for what is required). Or have an effective grip over newly-emergent fields that support the organisation’s core mission (enabling them to then achieve hierarchical seniority).
Career seniority skills include; training in budget, project, process and operations management, change management, information & service quality management, business strategy & marketing. Techniques might include; using simulations for planning, improving communication flows and learning risk management practices.
Career breadth skills include; spending time understanding allied innovations and research breakthroughs that have some bearing on the person’s area of greatest experience. For example, for an ambitious doctor going from a large specialist NHS Trust into a small private practice, it may be advantageous to broaden their knowledge of medical imaging techniques and image interpretation.
MOOC’s threat to low quality universities
Unlike for the premium quality university programmes that rely on creative interaction value, MOOC providers can be expected to sooner or later out-compete the low quality universities who can only offer simple lecture-style content of a standardised nature. Such universities have a significant physical cost structure to support, while MOOC providers offer their customers a vastly cheaper price for at worst, the same academic content and (virtual) study group experience.
How can premium quality universities understand market CPD needs better?
A key question to ask might be what step changes will talented and ambitious graduates need to make for their career progression and how can we position to match those needs?
Premium quality universities are arguably in pole position to communicate the value of specific knowledge and problem-solving skills to employers that drives CPD demand back to themselves.
Some business schools already do this well in providing bespoke onsite training courses of short duration to the employees nominated by their client. Therefore, what scope is there to maximise this demand opportunity, not with bespoke organisational courses, but with customised sector training, centred on the generic step changes?
On a related note, could the excess capacity of expensive university research kit (High Powered Laser machines, Wind Tunnels, Wave Tanks, MRI Scanners, High resolution/high speed digital cameras, Big Data Centres) be used in such CPD training courses, perhaps via a fieldtrip visit to the university campus?
If so, two other benefits might arise – with greater ongoing demand, the equipment resources could be scaled up to capture economies of scale for the university. And secondly, the effectiveness of alumni fundraising might rise – offering more CPD courses widens the potential alumni base and for returning alumni, reaffirms the bond with their original institution, which hopefully translates into greater donations.
For cautious people, the guidebooks they follow arguably determine the chapters and book of their life. Their lives can be personally fulfilling and deeply rewarding to them, but may look dull or constrained to outsiders.
For adventurous people (travellers, research scientists, innovative entertainers & artists, Olympic athletes etc), they start with the guidebooks, the text books and the training manuals, but quickly move into ‘pioneering space’ where nothing is codified (yet).
Ironically if the adventurers then succeed (in pioneering space), they go into the record books for posterity, while their contributions become the foundation of new guidebooks, texts and manuals…
Which book would you like to be?
With future advances in intelligent networks and their peripherals (robotics), will the people most valued economically in a society cease being energy-field owners/entertainment celebs/hedge fund managers? And instead be the entrepreneurs – those who can generate and sell brilliant ideas (to the intelligent networks and between each other), in return for goods and services?
If so, smart governments would be wise to plan ahead. They can introduce policies that actively encourage the creation of a nation of designers and inventors – challenge everyone to relentlessly practice their design and inventive skills to benefit themselves & the nation (idea entrepreneurship). Then eventually, with the rise of intelligent networks (self-organising), people will have something of value to trade – the more brilliant the human idea, the greater the resulting payment from the intelligent system.
It’s reasonable to assume that Intelligent networks & their peripherals will eventually do the engineering/commercialisation (simulation, translation, prototype fabrication, scale production and marketing work), leaving people to add value through imaginative design.
Universities, R&D Institutes, Corporate skunkworks & garage inventors (including the maker movement) – they all deserve accolades far in excess of their current image in society. Smart governments will find ways to foster innovation, not just in the ivory towers & skunkworks, but in the channels that link ivory tower innovation with garage invention too (who says university academics are the only ones with good ideas and why can’t the gov funding incentives change to reward research cultures to innovate themselves?).
Design solves inflexibility problems, environmental pollution problems, over-crowding problems, awareness & perception problems and security problems. It both creates opportunities and exploits them. Even politics at its heart is about clever design. Although sadly, too many politicians aren’t clever designers – leading by example and keeping their policy-makers on their toes.
Of course design is hard, taking energy, commitment and persistence. However, as everything else gets automated, hard design is our future as a species. Food for thought?
I read something interesting recently in Nate Silver’s book ‘The Art and Science of Prediction’ about (elite chess) players taking the best of three different computer models to win the game. For them, the task was less about being a player and more about coaching the best contributions from the models that they could then use.
Moving from chess to university research, is this a glimpse of how future university research will be done (develop multiple models that analyse the same vast data-set, then select individually or in combination from those)? One implication is that the demand for big data analysis in this sector will explode as models proliferate like virus mutations.
We’re already living in the age of ‘Big Data’ analysis with researchers crunching massive data-sets to uncover relationships and test out their theories At the same time, statistical theory continues to remind us that correlation isn’t the same thing as causation.
So although the historical data is real (or as real as we can get it using our best available technology to capture it), how much of the resulting output is ‘real’ because of the equations versus ‘more real’ because of the equations and the quality of the programming code? To elaborate, even if a researcher does (unknowingly) formulate the perfect, lengthy set of equations to essentially model something observable, how much is inadvertently ‘lost in translation’ by the data analysis coders? On a related note, perhaps our rate of innovation throughout history has been faster than we realised, it’s just our rate of proof of concept has been slow, since people lacked good tools to test the theories.
Finally, should we take a view that although correlation isn’t causation, perhaps various clusters of correlations can be modelled with the best cluster acting as a proxy for causation.
Why does this matter? Apparently various academic-published research results are ‘false positives’ i.e. hard for an independent set of researchers to repeat and get the identical results. The more this happens, the more it starts to debase all research findings, at least in the eyes of research-grant funders, who grow more skeptical about what they’re really funding. Furthermore, where those grant funders get their funding from fundraising activity (charities, biotech companies issuing shares, research councils asking central government for more funding), the upstream donors also become increasingly skeptical.
If instead, leading researchers were more honest in their published articles (supported by the university establishment in its incentive structure) about declaring the best correlation cluster model found to act as a proxy for real causation, society’s expectations of researchers (as coaches coaxing approximations, not as lab boffins uncovering ultimate scientific truth) would become more realistic?