Tag: learning technologies

The Battle of Premium Content in the Learning and Training Industry

 

On April 23rd, Coorpacademy co-founder Arnauld Mitre was invited by Fabernovel to their MultipLX, the Learning Expedition from your desk, to talk about the key stakes in corporate digital learning.

Video is in French but you can watch it with English subtitles.

Amongst these stakes, content. One of the biggest issues in the learning and training industry – which is a content industry – is to think that 2 contents with the same name are worth the same. Thinking that I would be able to learn how to become a better manager for example, on any support, with any course called ‘How to become a better manager?’ There are no other industries where this is the case.

You wouldn’t say, for example: “I’m going to watch a detective series”. But rather: “Would I watch Columbo?” or “Would I watch Money Heist?” These are not the same profiles; one chooses a particular content.

The first battle for the learning industry is the battle of content, the one you can find nowhere else, the battle of the best content.

In 1996 Bill Gates was already saying: “Content is King.” And it is even more true in the learning industry!

What about you? Are you more Columbo or more Money Heist?

Capturing Learner Data

 

“If somebody describes to you the world of the mid-21st century and it sounds like science fiction, it is probably false. But then if somebody describes to you the world of the mid-21st century and it doesn’t sound like science fiction, it is certainly false. We cannot be sure of the specifics, but change itself is the only certainty”, says futurologist and author Yuval Harari.

Change means disruption – and getting ready for change. And HR leaders need to proactively help people develop, adapt and learn new skills as part of this change if they are serious about retaining their competitive advantage.

This article from Jean-Marc Tassetto, co-founder of Coorpacademy, featured in Training Journal in the September edition – the UK’s most influential Learning & Development publication – looks at how the most advanced learning experience platforms are revolutionising the analytical possibilities for L&D professionals. Allowing them in the end to unlock and consider the full potential of their people: a good thing for business and, most of all, for the future of the employees. Discover the article!

Capturing Learner Data

Jean-Marc Tassetto looks at how the most advanced learning experience platforms have revolutionised the analytical possbilitiés for L&D professionals.

It’s no secret that the global workplace is going through a huge transformation. The arrival of automation, connectivity and artificial intelligence is seeing employees increasingly work alongside complet – not always transparent – technological processes.

As futurologist and author Yuval Harari says, the only thing we can be certain of is that our future in uncertain: “If somebody describes to you the world of the mid-21st century and it sounds like science fiction, it is probably false. But then if somebody describes to you the world of the mid-21st century and it doesn’t sound like science fiction, it is certainly false; We cannot be sure of the specifics, but change itself is the only certainty.”

Change means disruption – and getting ready for change. According to a recent survey by global analysts PwC, for example, 80% of CEOs said securing the right skills for the new digital economy is one of their biggest challenges.

The same survey found that 74% of employees are ready to learn new skills or retain to be employable in the future. 

But HR leaders still need to proactively help people develop, adapt and learn new skills as part of this change if they are serious about retaining their competitive advantage. 

But despite all this context of disruption, there is a positive outlook for humans in the job market. By 2022, says the World Economic Forum, emerging occupations are set to increase from 16% to 27% of the employee base of large firms globally, while job roles currently hit by technological obsolescence are set to decrease from 31% to 21%. THe body estimates that 75 million current jobs roles may be displaced by the shift in the division of labour between humans, machines and algorithms – meanwhile 133 million new job roles may emerge at the same time. 

Jobs going? Yes, but jobs are coming. 

In other words, robots are being added to the workplace but so are people – with new and different skills. US staffing giant ManpowerGroup, for example, has stated that it is reskilling people from declining industries such as textiles for jobs in high-growth industries such as cyber security, advanced manufacturing and autonomous driving. 

Growth is also forecast in frontline and customer-facing roles – which all necessitate interpersonal skills such as communication, negotiation, leadership, persuasion, complex problem-solving and adaptability. 

With talent shortages at a 12-year high and new skills emerging as the world gets more connected, companies are also realising they can’t source the skills they want at short notice. ManpowerGroup found that a staggering 84% of organisations expect to be upskilling their workforce by 2020. What would that look like in practice? The World Economic Forum estimates the average employee will need 101 days of retraining and upskilling in the period up to 2022. 

This is no small ask for HR and L&D departments. And while there is unlikely to be a jobs apocalypse in the future, if organisations don’t take the right steps now there will be a drought of skilled talent, which will have a detrimental impact on the bottom line. What we can be sure of is that technological change will necessitate employees continuing the L&D process throughout their careers, requiring strategic lifelong learning plans.

Where is the hard ROI training data?

Supporting such plans will put pressure on organisations to provide comprehensive and imaginative L&D opportunities to fully support us through these changes. That’s not great news at a time when training budgets are being squeezed and the C-suite is demanding to know its return on training investment. So having the right metrics and guidance to show proof of ROI back to stakeholders is now more crucial than ever. Let’s review how important that is. At the Learning Technologies exhibition and conference in February this year, independent HR analyst firm Fosway revealed the first preliminary results of its annual digital learning realities research, and the verdict was not positive: “By not providing hard evidence of how learning is adding value on an individual, team or organisational level, practitioners are missing a huge opportunity to gain recognition of their contribution to the organisation and much-needed investment for future learning,” warned the organisation’s director of research, David Perring. 

Perring went on to detail how only 14% of the UK HR community can say with confidence they are effectively measuring the impact of learning, while around half are doing so, but poorly, and a third are not measuring impact at all. No wonder, when asked to describe the L&D industry’s progress in measuring learning impact, this analyst responded with just one word: “terribly.”

Help may finally be at hand

The good news is that a way of mapping training investment to measurable bottom-line results may be about to become available at last. That’s in the shape of the learning experience platforms (LEPs), recently formalised as a new market category by Gartner, which have started to become increasingly common in L&D work in the past few years. 

Highly user centric in their delivery model and usability, it’s maybe less well understood that the most advances of this class of edtech software has also revolutionised the analytical L&D palette; 

The advanced LEPs in question track learner behaviour and use that data to test what works and what doesn’t, based on a powerful new way of collecting such data – the Experience API or xAPI standard. That’s a really significant step forward because, until very recently, learning analytics only existed in a very basic way. That was because learning management systems (LMSs) managed access and tracked participation of learners, namely the attendee list – but little else. There may in addition be information on e-learning content downloads, task completions and module completion, but the data was thin to say the least. 

xAPI and activity streams

The gamechanger here in these modern LEPs is the new interface, as xAPI allows us to record any learning experience, including informal learning, providing a much richer picture of an individual’s learning path. The Experience API also prevents data from remaining in the confines of your siloed LMS, as it succeeds the older de facto e-learning standard SCORM (the sharable content object reference model) and is capable of correlating job performance data with training data in order to assess training effectiveness.

Let’s make that a bit more concrete. If you look at someone’s Facebook wall, what you are looking at is a series of activity stream statements; and activity streams are gaining traction as a useful way to capture a person’s activity, both on social networks and in the enterprise.

xAPI uses the same format to capture learning experience data, and as we start to aggregate these streams across an enterprise, or even across an entire industry one day, we can start to identify the training paths that lead to the most successful or problematic outcomes, and so what determines the effectiveness of our whole training programme. 

Doing that would enable organisations to glean new insight into what a learner has successfully learned, how they gained this knowledge and which learning approach they chose to follow. This provides opportunities for strong diagnostic values and advance performance indicators, such as curiosity, or resilience – both hugely valuable people metrics. And, of course, this will ultimately aid the workplace learner as he or she becomes aware of what their own data says about their progress and experience, so as to ensure long-term employability. 

This transformative potential of these new indicators is even greater if you consider that World Economic Forum identified reskilling and upskilling of the current workforce as the number-one strategy companies need to embrace in light of our continuing transformation into a knowledge economy. Knowledge, in the Google age, is easily acquired – while curiosity on the other hand seems less ubiquitous, and many commentators believe we need to boost employee curiosity as well as builder greater resilience and adaptability to change. 

In conclusion

Summing up, the demands of the modern workplace mean we now need to move to a far more learner centric model, where classroom training is supported by virtual training, available on demand, wherever and whenever the learner wants to access it. Such learner centric approaches and leading edge xAPI-enabled technology are proven to work – and most importantly, secure high levels of user engagement. 

Together with the benefits this new generation of LEP-derived behavioural learning analytics could bring, this puts training back at the centre stage in business. Exactly where it needs to be to satisfy the growing and diverse skills requirements of a digital future. 

The result: HR and training professionals can finally use multiple data sources to consider the full potential of their people for specific roles within the organisation and business outcomes. And this has got to be a good thing – for the business and, most of all, for the future of the employees.

Jean-Marc Tassetto is co-founder of Coorpacademy and a former head of Google France; Find out more at coorpacademy.com

 

5 minutes to learn – an article from Jean-Marc Tassetto, co-founder of Coorpacademy

 

A study by Bersin by Deloitte paints the portrait of corporate learners today. Overworked: two thirds of them said they don’t have enough time to do their job. Impatient, too: they won’t spend more than 4 minutes on a video and have an attention span of 5 to 10 seconds on a website. Lastly, learners today are distracted, unlocking their smartphone up to 9 times an hour and logging online 27 times a day on average. The study also shows that employees are likely to be often interrupted by outside elements, such as virtual or real interactions, or even by themselves, while they are focused on a task. Sometimes, every 5 minutes. A short period of time.

Those 5 minutes are not a lot of time to complete a task, let alone receive training. And it is often difficult to devote entire days to training sessions. At the same time, a growing need can be observed among employees to take control of their training journey and develop skills progressively, on the topics they want and whenever they want. And Human Resources managers are becoming aware of this. 

Coorpacademy, as a new player in the online training market, is trying to solve this equation through shorter training modules and sessions of “microlearning” and even “nanolearning”. The idea is simple: it’s a relevant answer to the chronic lack of time from the traditional corporate training model.

The content is divided into several shorter, more accessible sessions, with the creation of opportunities and contexts as a background. A session of microlearning should be seen as an opportunity to create special and useful “moments” for learning, particularly on mobile, while waiting for a meeting to start or a plane to take off. It’s during these moments that employees will want to integrate a few useful notions.

We launched “5 minute learning”: short content, editorialized and contextualized according to what’s going on and what our customers need, and delivered on mobile, which allows the creation of these short learning “moments”. All of this is supported by an engaging user experience.

Several studies have shown the efficiency of microlearning. An article in the International Journal of Educational Research Review states that a group following a “traditional” training program is less efficient than a group with the same content in a microlearning format (64% vs. 82%), and that microlearning can increase an individual’s learning capacities by 18% compared to traditional methods.

In order to create moments that are favorable for learning and thus transform the training process and make it a truly effective tool for the transformation of companies, a change in our ways of thinking is fundamental.

Heads of human resources and training, you should look at the training experience as a whole before you start scheduling training programs: every coworker must be able to manage their own learning time and pace.

Managers, you have a fundamental role to play: to accompany the training of your teams. Allow and encourage new ways of learning!

Finally, employees, don’t forget that for every moment spent in training, even if it’s only for a few minutes, you are developing your skills and knowledge, and therefore your current and future employability.

Coorpacademy in the Top 20 finalists of EdTechXGlobal Awards 2019 Scale-Up category!

 

Coorpacademy has been selected in the Top 20 finalists of EdTechXGlobal Awards 2019 Scale-Up category!

20 finalists included those education and training companies who have shown the most revenue growth momentum over the last 3 years.

Along with Coorpacademy (Switzerland), other finalists include Learning Technologies Group (UK), Toppr (India), Busuu (UK), ApplyBoard (Canada), Amity University Online (India), Le Wagon (France), Openclassrooms (France), Twinkl (UK), Unicaf (Cyprus), Virtual College (UK), Touch Surgery (UK), FutureLearn (UK), Learnship (Germany), Ducere (Australia), Upgrad (India), Circus Street (UK), Mindtools (UK), Seagull (Norway), Clio Online (Denmark).

We are proud at Coorpacademy to have been recognized by EdTechXGlobal as an innovative, fast-growing and impactful company transforming the future of learning and work!

Who’s EdTechXGlobal?

EdTechXGlobal connects the global learning community through insight, investment and thought leadership event series – including the flagship summit, EdTechXEurope in London and local Ecosystem Events in Africa, Asia and Europe. These curated EdTechXGlobal events bring together executive level investors, innovators, and industry influencers from worldwide education technology companies from across 60+ countries. EdTechX is also the founder of London EdTech Week, a connected, curated event series featuring 40+ event hosts powering events across London each June.

Read more here!

Coorpacademy in the top 20 Finalists of EdTech Scale-ups

Ever Heard of Machine Teaching?

 

This article is part of our new Learning research and innovation series, offered by Coorpacademy in association with the EPFL’s (Federal Institute of Technology of Lausanne, Switzerland) LEARN Center. The author is Prof. Pierre Dillenbourg, Professor at the EPFL, Head of the CHILI Lab (Computer-Human Interaction for Learning & Instruction) and Director of the Swiss EdTech Collider.

The terms Machine Learning, Deep Learning, and Artificial Intelligence are on everyone’s lips. But what if we extended this list to something we call ‘Machine Teaching’ – and then speculate on what it might mean for education?

Towards ‘Machine Teaching’

Let’s imagine an algorithm that needs to learn how to identify elephants in pictures. In supervised Machine Learning, it gets an example – e.g. picture-3465 – and a label, such as ‘elephant’ or ‘non-elephant’. Picture-3465 may just be the next in a set of thousands of labelled pictures. But if the 3,464 previous pictures were all of African elephants, the system would learn less from yet another African elephant picture, than if an Asian elephant picture was introduced for the first time.

Similarly, if all the previous pictures showed mostly mature elephants, it would be better for the algorithm’s training to select a younger one. Again, if most of them were side on pictures, a frontal view would improve the knowledge acquired by the algorithm.

In other words, if the examples were not fed to the learning algorithm randomly, but strategically selected, one could optimize the machine’s overall learning performance. In a classroom setting, selecting examples is the role of the teacher: she knows that if all examples of squares given to learners are in a horizontal position, learners will logically infer that a square with a 45 degree rotation is not a square.

Any algorithm that determines the optimal sequence of examples such that they are diverse and sufficiently dissimilar from what has been shown previously to a Machine Learning system can be called a Machine Teaching algorithm.

Why Should We Care about Machine Teaching?

If an algorithm receives random examples as inputs, with no strategic consideration of the type of example and what the algorithm will go on to learn from exposure to this example, then clearly problems will arise. First, we should not confuse the size of the sample data with its intrinsic usefulness: merely feeding big data to a Machine Learning algorithm is not enough to guarantee the AI has learnt well and will perform well in its tasks. Secondly, the algorithm could tend towards taking wrong or biased decisions. Let’s reuse the above example of the identification of elephants from pictures: if the only pictures labeled as “non-elephant” are pictures of white animals, the algorithm might infer that only white animals are to be categorised as non-elephants. Sounds silly, but this kind of biases creep in, and matter. Biased algorithms can reinforce gender stereotypes (as was the case in Google’s translation service), or might suggest wrong decisions about humans (as, for example, decision support systems for judges which over-estimated the probability of recidivism for African-American people).

How Does All This Apply to Education?

The impact of AI on education spreads over three layers: (1) Method: AI may enhance the effectiveness of learning technologies where it is expected to enable a fine adaptation of instruction to individual learner needs: over time, a system may learn which learning activity is optimal for a certain learner profile. (2) Content: AI is changing what students should learn or should not learn and is also accelerating the production of learning material, for instance generating questions from Wikipedia. (3) Management: AI and especially data sciences offer new ways to manage education systems (e.g. predicting students’ failure).

Machine Teaching turns out to be relevant in all of those applications. Personalised learning, based on recommender systems, can only be well adapted to the personal needs of a learner if the data set on which the recommendation is based on is large and equilibrated enough. That means we need non-random data selection in any machine learning, i.e. the algorithm needs to be fed with data on what is effective for all types of learners.

In terms of content, when learning about data science and machine learning, learners need to also learn how to design the optimal dataset that the algorithm will learn from. Engineers are becoming teachers of algorithms by default, because you cannot simply program a Machine Learning algorithm. We need to better facilitate the correct decision-making of the algorithm – the same way a good teacher helps her students to develop problem-solving and critical thinking skills.

Innovation in Learning Science and Educational Technologies are top of our agenda at Coorpacademy, as we see them as critical to our mission to continuously improve the learning experience on our platform, making it even more personalized, flexible and enjoyable for learners.

The author Pierre Dillenbourg

Coorpacademy in the Global EdTech Landscape 3.0 by Navitas Ventures!

 

Coorpacademy is proud to have been recognized in the Global EdTech Landscape 3.0 by Navitas Ventures, the venturing arm of Navitas, a leading global education provider.

This mapping defines 8 steps in the next-generation learner cycles, divided into 26 clusters that are shaping the evolution of education. Coorpacademy can be found at the “Learn” step of the cycle, in the “Open Online” cluster, where “old and new learning methods collide to meet the needs of demanding global learners and changing workforce priorities.”

Mapping Coorpacademy Navitas Ventures

We are proud of this new recognition!

You can download the full report here.

If you want to discover other Coorpacademy’s recognitions, it’s here: Find out about 6 of the best latest news and awards for Coorpacademy’s Learning Experience & Upskilling Platform!

Coorpacademy in the Top 5 hottest startups in Switzerland

 

Coorpacademy has been selected by The Next Web in the Top 5 hottest startups in Switzerland! TNW showcases the hottest young scale-ups in all European countries and Israel based on their performance, growth, and potential, which will all be represented at the TNW Conference in Amsterdam on May 9th 2019.

As Switzerland has been named the most innovative country in the world for eight years in a row by the Global Innovation Index, and ranked the second best startup ecosystem by the Global Entrepreneurship Index, Coorpacademy is proud to thrive in this startup heaven!

The Next Web describes Coorpacademy: “Coorpacademy is one of the fastest growing Edtech companies in Europe. The platform allows companies to train their employees through fun and interactive online courses on soft skills available off the shelf. The start-up is at the heart of research into new learning methods and is an expert in adapting its customers’ training content to new learning methodologies. Since its founding, they’ve raised €14 million, provided their services to over 150 enterprise clients and signed partnerships with more than 40 content partners. They are currently expanding internationally into the UK market and are growing their presence in France and Switzerland.”

Discover the full article on The Next Web website!

How Thinking Like A Computer Will Help Save Our Jobs

 

This piece has been written by Jean-Marc Tassetto, co-founder of Coorpacademy, and originally published on minutehack.com. To read it in its original form, it’s here!

Historically, IT training has focused on coding skills. Now we need to think more like machines as well.

According to Mary Meeker’s much anticipated, just published 2018 technology predictions, you can expect the pace of the disruption of technology on the way we work to just accelerate – not slow down.

But does that mean fewer jobs, as so many fear – or a completely new set of career opportunities?

The evidence of history points to the latter, as the famous Internet trend analyst herself says: ”New technologies have created and displaced jobs historically… Will technology impact jobs differently this time? Perhaps, but it would be inconsistent with history, as new jobs and services plus efficiencies, plus growth typically are created around new technologies.”

And it’s true technology is disrupting the job market. As the World Economic Forum’s 2016 Future of Jobs report and a recent OECD study also found AI (Artificial Intelligence) in particular looks set to take over more and more tasks.

Some authors claim that only as little as 35% of current skills will still be relevant in five years – others say less, and it’s white collar jobs facing automation upheaval this time round, not just blue.

Step forward Computational Thinking

It seems we are on the cusp of a new automation age for sure. And as the robots move into our workplaces, our job roles will adapt – and with it, the skill sets to remain relevant. Everybody will need to have abilities complementary with digital technology.

But not everybody will be in need of hard programming skills: the future will require more than just being to code in Python or deal with malware.

This could mean skills associated with the Cloud, analytics, mobility, security, IoT and blockchain certainly, but there is a growing consensus that, as a culture, we have to introduce a computational/programming-like approach into all of our approaches to work.

This is being formalised around the movement around Computational Thinking (CT), where the focus is not just on the machine but on the human, whose thinking and learning is enhanced by the machine as job roles involve more and more working with computers.

Computational Thinking is basically the approach we take when we consider how a computer can help us to solve complex problems – i.e. algorithms, the way a Machine Learning program can learn from the data it gets, the limits of computation and so on.

But it also shapes what the person involved in the business process does, like preparing a relevant data set for that task, dividing a problem in useful chunks resolvable for a computer, detecting configurations where automation and parallelisation can be introduced, designing digitally, and so on.

What does this look like in the real world? Say you’ve agreed to meet your friends somewhere you’ve never been before. You would probably plan your route before you step out of your house. You might consider the routes available and which route is ‘best’ – this might be the route that is the shortest, the quickest, or the one, which goes past your favourite shop on the way.

You’d then follow the step-by-step directions to get there. In this case, the planning part is like computational thinking, and following the directions is like programming.

With this definition, it’s immediately clear Computational Thinking is not just for computer scientists. Being able to think through a problem in a similar, logical manner and come up with a solution in the digital world is what matters, and what we may all need, and as our professional lives become increasingly automated, CT related skills will grow in importance.

Whether it’s computational contracts, education analytics, computational agriculture or marketing automation, success is going to rely on being able to work fluently with IT, but always to have your eyes on the bigger picture.

Some forward-thinking policymakers are beginning to try and put this digital extension to traditional education on the horizon.

The US, for instance, is among the early adopters of CT, with its National Research Council and US tech university Carnegie-Mellon has its Microsoft-sponsored Center for Computational Thinking that provides seminars, workshops, research activities on computational thinking in any domain of life.

Leading European Higher Education institutions are following suit, like the Federal Institute of Technology in Lausanne, Switzerland, which has been introducing dedicated CT lessons in all entry-level courses across all disciplines.

In the UK, the Open University is also running introductions to CT, while the National University of Singapore has made CT compulsory for higher education students, regardless of what course they are studying. Globally, Google is pushing hard for the democratisation of CT at early years to 12 education globally, providing a variety of teaching material to educators.

The call to action

But what should the world of business be doing about this huge momentous shift? How do firms incorporate CT approaches into their curricula to help their staff? What can we do to help employees successfully transition and acquire these new skills?

First, it’s absolutely key that you insist employees take time out for education and establish continuous learning programmes. To ensure success, you need to get away from the ‘top-down’ approach of old.

The old method of scheduling fixed hours for input needs to be discarded in favour of a learner-chosen model and a virtual learning environment in which all lessons and material are digital and available, 24×7 and increasingly via mobile and in short bursts.

In addition, incorporating gamification and collaboration features will increase employee engagement by activating the joy of competition and the desire for socialisation and exchange.

Employees are also time-poor and required to face rapid changes in their industries and jobs. What they learn must therefore meet their immediate needs and be adjustable to their level.

Asking them questions before any teaching takes place (the flipped pedagogy model) is a great way to pinpoint their level and means they’ll be offered the lessons they need. Finally, this is the foundation of a move towards adaptive learning, in which content and teaching frameworks are customised to the individual.

Such learner-centric approaches work, and can secure user engagement levels of more than 80%. One of our customers, Schneider Electric, places user centricity at the heart of its training efforts: “Individuals are able to self-pace their learning, and we are experimenting with mobile learning as the next frontier in this journey. Digital learning is now a way of life here.” None of this will succeed if employees don’t see the results for them.

According to a Gartner report published in May, “Place the learner’s experience and the solution’s usability at the top of the priority list for any new learning project.”

Training, be it CT-oriented or not, has to be about the learner experience, encouraging employees to develop all their skills to their full potential and to future-proof their careers – and employers need to offer skills like CT if they are to flourish, too.

Embracing a computational thinking mindset will prepare us to meet anything the digital world of the future can throw at us.

Jean-Marc Tassetto is the former CEO of Google France and co-founder of Coorpacademy, a growing force in the provision of user-centric corporate digital learning solutions. 

To read this piece in its original form, it’s here!

MENOU

Voir l'étude de cas