Digital nomads and skills that matter

Education and employment are interdependent. The current problem is that stakeholders from the education industry either don't acknowledge this fact, or they choose to ignore it and continue to treat education as a singular entity in a vacuum while a generation of digital nomads needs skills that matter. The era of elitist education is drawing to a close, and in much the same way, the idea of permanent employment is being drawn into question, as job-hopping becomes increasingly common. The evidence for this is widely demonstrated by the e-learning market expected to exceed USD 375 million in five years time, as well as the turnover of employees in the U.S. which moved from 27% in 2018 to 36% in 2019. Despite this, the education system and employers have so far missed considering trying to learn from one another. In a fast-moving tech-driven world 1) If employers are to hire quickly and precisely, they need to do the following:

• Easily identify resources

• Easily identify the differentiating elements of resources

2) If education providers are to provide skills that fit the labour market, then they need to:

• Easily measure how differentiation performs so that they can promptly adapt

All of this entails knowing what works and what doesn’t work in real-time for the education system to adapt, allowing employers to perfect their hiring. Many will say “we have AI” to build projections from big data, the impression is that if AI does such an impressive job, why the 37.9% of employees leave their companies within the first 365 days after being hired? The truth is that the compatibility of a candidate for a specific job is determined by skills that can be measured and by other skills that cannot. Projections can only apply to the real world if they are based on sufficiently realistic data, which can define patterns in a sufficiently granular way. Current practices attempt to compensate for this immeasurability with subjective evaluation, which is subject to approximation and involuntary bias. The result of this process is always “the best that could be found”, but not necessarily the correct candidate for the job. Shifting to the ecosystem approach When it comes to shifting to an ecosystem mindset, the dilemma that faces a decision-maker is whether to open up to a crowded multi-actor space, while running the risk of losing direct control.

In nature, an ecosystem is a structure in which plants, animals and other organisms as well as the landscape, work together to form a cycle of life. With emerging technologies massively changing the way we live, move, exchange value, consume goods and work, it is absolutely evident that without adequate skills companies risk losing the competitive race that is now globalized, most importantly, large portions of the population who do not access education might be left behind making global growth unsustainable.

Modern society’s complexity makes it crucial that all stakeholders work together to achieve progress and in most cases also to survive, as none of them can actually achieve their goals without some sort of contribution or interaction with the other.

Ecosystems, both in nature and in society, require some form of governance and agreed rules, the advantage for stakeholders is growth, the disadvantage may be a change from current practices. If we think of learning providers, employers and institutions working together, the problem is mainly represented by data management and privacy, while the advantage may be that members of the ecosystem could agree to shared taxonomy and recognition equalizing the indicators of different forms of learning and impact. The blockchain world solves the problem above with platforms conceived as “trustless ecosystems”. This term refers to a context in which trust is not necessary because the system itself guarantees trust. More precisely such contexts benefit from the immutability and transparency typical of blockchains that enable members to exchange value or information that is anonymized and can be trusted because any information added to the blockchain can neither be changed nor deleted.

The recent pandemic is only the latest of the elements that make the question about the opportunity for employers and the education system to act as a unique ecosystem outdated. Evidence was in fact already there before the pandemic:

1. An agonizing paradigm of "learning-apprenticeship-work": human beings are simultaneously learners and workers; apprentices and experts.

2. A shrinking western working population.

3. A huge talent crisis with HR departments frightened of not having the right skills to deliver the high-level vision of their boards.

In this fast-moving, multifaceted world, there is no time to search for talent. Resources just need to be the right ones and, ideally ready for use: developing skills internally is only part of the solution.

The needs of the industry are the needs of the education system, whose purpose has traditionally been to bring people to self-realization in a society where the identity of the human being is primarily defined by work. Informal learning derived from experience has become equally useful as formal and non-formal education. With the latest technologies helping everyone to continuously access information at any time, anywhere, to develop new skills and to broaden their horizons, informal learning is well-positioned to become the predominant means by which the individual can evolve their skills.

When employers and learning providers come together to recognize and maximize one's potential, the impact of all forms of learning can be set to send back information about its impact. The education space is timidly approaching blockchain platforms where qualifications can be stored and shared. As very often happens when platforms are created by the same industry that populates them, they tend to be exclusive, making it difficult to adequately merge the entire professional and educational path of an individual which is what employers are interested in the most. This said, they will certainly help to give transparency to formal education credentials,

To give peace of mind to those who are not yet familiar enough with the principles of blockchains and to sceptics, the good news is that standalone technologies like Certiif demonstrate how the immutability and transparency of blockchains can be subtle layers embedded and integrated with existing technologies which do not require members of the ecosystem to share the same infrastructure. Moving to blockchain-based certificates, badges or attestations to signal education, achievements and informal learning can be as easy as downloading a Microsoft licence.

New hubs of learning

Unlike traditional formal learning, non-formal and informal learning is now happening in different spaces and contexts at work or home. Informal learning is defined as learning which typically takes place voluntarily in real-life situations and often through peer interactions and participatory approaches, such as in talks, meetups and events. Organizations offer more and more upskilling, reskilling, orientation courses and training on the job, while apprenticeships are one of the sources of informal learning for young people. It is not clear yet who will earn the recognition for becoming the new learning hub; whether it will be employers leading the way with informal learning and providing non-formal learning through their academies, formal education rebranded into ecosystems of partners or global platforms with distributed content providers.

Most likely it will be a combination of all three.

While boundaries between the different kinds of education are fading away, and the world of education and employers are building a new partnership, collaborations seem more focused on the delivery of education rather than on tracking the utility of the learning provided. In the lifelong learning world, learning and working happen dynamically and often overlap, while the flow can potentially generate information at each step that can be considered to improve results, this doesn’t take place because of legacy methods and data management concerns.

The efficacy of learning is not measurable right now

While analytics help the formal education world to improve the student experience within the university, no real-time data is informing the education provider about the value that the market attributes to each piece of education delivered. Today, the best business schools measure the added value of their learning by mentioning the salaries earned by their best alumni. They define their courses based on the global demand for specific skills, or how well their university ranks against several criteria: teaching environment, research volume, citation and influence, international outlook and industry income. This last one being a minimal fraction of the overall scoring. When I asked the dean of a notable business school how they knew how relevant is the learning provided from an employer perspective, he responded that there is actually no way for them to measure it. When I asked e-learning platforms the same question, the answer was that "the client is king", which still doesn't measure the impact of learning.

So far, education institutions have built their reputation by scoring aspects such as the volume of research, the number of citations, the income generated by research, and productivity, all of which are examples of output. However, if education is needed to prepare for a skills-driven economy, then the value of education is not only given by the score of an institution's output. It should measure and add to its scoring system the outcome: that is, how well did the output perform? This information would help to redefine programs and content to fit the needs of the labour market. Unsurprisingly, the growing number of learning content platforms, which were initially focused on delivering content and tracking the students’ experience, are now focusing on improving their algorithms to read correlations in users’ interests. Platforms start extracting useful information to build further content. Unfortunately, their ability to define a learner’s persona can only be as precise as the user is loyal to the platform itself.

The challenge for new hubs of learning will be building their reputation, adding the relevancy of output for the labour market. A figure that will make the difference between one learning hub and another.

It is the law of the market that when the supply increases the price decreases, and when this happens explaining the value to the customer and to demonstrate why it's worth the extra money becomes more and more important. Making the impact of learning measurable is key for all stakeholders in the future of work.

Soft skills as the holy grail As an individual continuously learns, their ability to see connections between subjects grows, as does their ability to connect the dots and contribute in a complex world. In a world where access to information is just a click away, sifting through overwhelming quantities of information is the major challenge. Curiosity, tenacity, critical thinking and time management are part of the soft skills that help individuals to navigate, learn and continually acquire information. While hyper-specialization is likely to remain a much-demanded skill, the ability to find links between apparently disparate pieces of information and to identify patterns and connections between them is the rare skill that leads to unique perspectives and the ability to navigate complexity. A skill that grows with the volume of acquired notions and ideas and is proportional to their quality. The noise and bad information that haunts the web are the major threats to these two dimensions.

For a long time, universities have been proud of gathering the best minds and providing the best quality of education, pre-formatted educational programs may only represent a portion of one's educational path. With information and learning everywhere, the new learning hubs organized in ecosystems will have an interest in transmitting quality through all the channels used and in allowing the user to grow their value by switching easily from one to another. While the need to move to personalized learning is widely accepted, the assessment of an individual and their potential is linked to their results. In the future of work, where we will be all students continually expanding their ability to connect the dots, to assessing the potential of each one, the results by themselves will mean less than the path used to achieve them. It’s the exposure to ideas and experiences, and the quality of such experiences that need to be traced back, to understand one’s potential. Potential is dynamic and fluid, as opposed to the hard skills that are static. Signalling it properly is key for individuals, learning providers and employers.

The ability to dynamically represent this potential is linked to the openness for for-profit and non-profit organizations, institutions and governmental agencies to act as transparent, inclusive ecosystems adhering - if not to the same tools and infrastructures - at least to the principles that traceability and privacy are equally crucial.

Digital nomads in a meritocratic future of work

By digital nomads we usually mean the professional who works remotely, the recent pandemic has transformed everyone into digital nomads. Although a post-pandemic return to normalcy is desirable, seems that it accelerated the redesign of flows of work and learning that all now understand must easily happen also outside the legacy framework of companies and institutions.

With cyber-risks on the rise, the risks involved in depending on a few big players managing everyone’s data and much more fragmented information, the digital nomad appears to be the natural owner, carrier and sharer of any information that concerns their potential. This is the technical need that inspired TiiQu. The purpose has been to provide a way to the inclusive meritocratic ecosystems to foster everyone's fulfilment.