Month: May 2019

Let’s welcome a new dawn of behavioural learning analytics – an article from Jean-Marc Tassetto in TrainingZone

 

This article has been originally published in TrainingZone, which is the UK’s leading learning & development publication, covering everything you need to know to deliver absolute excellence in L&D. It has been written by Jean-Marc Tassetto, co-founder of Coorpacademy.

With the rise of learning experience platforms, L&D teams will be able to measure and assess the behaviour of employees as they progress through their learning journey, taking learning analytics to a whole new level.

Discover the article in its original form here!

Here are some extracts of the article:

“Like it or not, the L&D sector is not where it should be when it comes to proving its worth to the wider business. The good news is that a way of mapping training investment to measurable bottom-line results may be about to become available at last.

Let’s review how important that is.

[…]

“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 [Fosway Group, Editor’s Note] Director of Research David Perring.

Only 14% of respondents could say with confidence that they are effectively measuring the impact of learning, while around half are ineffectively doing so – and a third not measuring impact at all.

No wonder Perring answered a question about how the L&D industry is faring with measuring learning impact, with just one word: “Terribly.”

[…]

“However help may finally be at hand in the shape of learning experience platforms (LEPs), recently formalised as a new market category by Gartner, which has 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 known that they have also revolutionised the analytical L&D palette.

How? Because LEPs track any behaviour traces and use them to test what works and what doesn’t, based on a powerful new way of collecting such data, the ‘Experience API’ or xAPI standard.”

[…]

“Finally, with the LEP much more flexible and interesting ways of working with corporate learning have started to deliver much richer datasets that are the basis for really useful analysis.

The consequence for learning analytics of the LEP is that the data we’re getting back now is much more complete and behavioural.

That’s because in order to constantly improve the learning experience, the most advanced xAPI-enabled LEP tracks any behaviour indicators it can spot and uses them to test what works and what doesn’t, based on tracking the stream of learning activities – and not just completion of modules.

The results are highly promising. For a start, the learner, not just the trainer and administrator, can get a sense of his/her progress and experience. That really matters if we want our teams to be truly autonomous learners able to ensure their own long-term employability.”

[…]

“L&D managers and administrators benefit because they can access all sorts of new types of insight – not only finding out what someone successfully learned, but how the learner got there and which learning approach they chose. […] Take curiosity, identified as an “important variable for the prediction and explanation of work-related behavior” (Mussel, 2013). That is really critical, as motivation to engage in lifelong learning is a sine qua non of employability for today’s worker.

Notably, another important effect of curious collaborators is that they contribute to a company’s innovation potential, particularly in the light of the “death of top-down management” (cf. John Bell, 2013).

Employee learning perseverance is another potential new KPI example. When you next need to decide who to recruit to lead a project, or who to train, it may be useful to select those who are qualified but also the most resilient candidate (cf. Amy Ahearn, 2017).”

[…]

“Could a new ‘behavioural learning analytics’ emerge that will put learning and training back in the centre of your organisation? It’s starting to look that way.”

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

If you want to discover other articles from Jean-Marc Tassetto, co-founder of Coorpacademy, you can click on those links:

Why Training is an Under-Used Source of Employee Insight – Incentive & Motivation

Jean-Marc Tassetto’s interview for French television (BFM Business).

Is LXP the new LMS – Enterprise Times

 

1 million Battles have been played on all Coorpacademy platforms!

 

Learning is difficult.

Learning new skills has always been tough, in school or in corporations. To remedy this situation, we provide on the Coorpacademy platforms features coming from the gaming world to sparks engagement and make training fun, addictive and attractive.

Gaming features provided by the Coorpacademy platform

The Battle mode, one of our most iconic gaming feature, has a significative impact on learning, in the short-term but also in the long-term. What’s a Battle? A mode where the learner can challenge another one in a quick quiz battle.

You think you’re unbeatable on cognitive biases, those thinking traps that can easily trick your mind and ways of thinking? You want to challenge your colleague Anna on the topic? It’s easy: launch the Battle mode, click on “Create a Battle”, choose your Playlist, the course and the course level (in this case the “Always one step ahead!” Playlist and the course Cognitive Biases: Thinking Traps) and answer the questions.

Once the quiz is done, Anna will receive an email inviting her to answer the same questions. The one who has the most right answers wins the Battle, and then Stars to climb up the ranking. If it’s a draw, the one who answered the fastest wins the Battle.

You won? Anna wants her revenge and challenges you again on her favorite course, Inbound Marketing and Growth Hacking! Anna challenges you with the Battle mode

Because you’re doing Battles, Anna and yourself are more engaged in your training courses. It’s been proven that Battles were improving coworkers’ engagement in corporate training.

In our Learning Report 2018, we identified a type of learners, the Players (the learners who played at least one Battle) and we realized that Players were more engaged and more efficient in training. The Players are 2x more present: the number of months that a learner is active on the platform during his/her whole learner life cycle is two times higher for Battle players than for non-players. The Players are also 3x more active, with more than 3x more lessons viewed. They also dive deeper into the content: they have started and completed 7 more modules on average than non-players. Finally, the Players are 13% more successful (success rate is measured as the completion rate of started modules) than non-Players.

Our clients are also seeing the difference. In our latest interview with BNP Paribas Asset Management (they launched their Coorpacademy-powered platform Digit’Learning in May 2018), Sylvie Vazelle-Tenaud, Head of Marketing Europe for Individuals, Advisors and Online Banks, told us:

We present the platform as a tool for gaining expertise with a gaming aspect. In our communication, we mainly highlight the functionality of “lives”. We also highlight the fact they can earn stars. This functionality enables us to generate emulation between employees and make them want to take the courses again. Conversely, we didn’t communicate very much about battles but the employees discovered that functionality on their own and loved it! Coorpacademy offers flexibility in learning without being time-consuming, as the average duration of an entire learning journey is 20 minutes. Employees build their expertise in record time while having fun!

Indeed, more than 70,000 Battles have been launched on the BNP Paribas Asset Management platform in only one year. Playing is natural, it doesn’t seem to require a lot of effort and at the same time it helps and favour learning.

Learning becomes easier.

On all our platforms, we reached 1 million Battles played!

Will you launch the 1 million and one?

Ready, steady, challenge!

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

Why Training Is an Under-Used Source of Employee Insight

 

This article was written by Jean-Marc Tassetto, co-founder of Coorpacademy and former Managing Director of Google France, and originally published in Incentive & Motivation. Incentive & Motivation magazine offers the latest news in incentives, employee rewards, employee engagement, motivation and employee benefits. Distributed to HR, Sales and Managing Directors with key industry senior incentive level incentive buyers.

Why Training Is an Under-Used Source of Employee Insight

Here are a few extracts of the article:

Co-founder of Coorpacademy, Jean-Marc Tassetto, outlines how new training analytics could offer unexpected help to HR professionals

Training is, as we know, a key source of workforce engagement – an important component of helping employees feel a real sense of belonging and identification and a tangible way to underline your commitment to their future learning and development as their employer.

[…]

Up until recently learning analytics only existed in a very partial way. That was because the dominant training technology we’ve been using – the Learning Management System (LMS) – managed access and tracked participation of learners, namely the attendee list and the scheduling of trainer time, but little else.

The LMS might offer information on content downloads, task completions and module completion, but the data was very thin to say the least. What’s changed in this picture is the debut of a much more flexible and useful L&D technology tool  – new-style Learning Experience Platforms (LEPs), as recently formalised as a separate market category by Gartner.

What’s different about the LEP contribution, as opposed to the LMS support idea, is that they are all about the learner experience – being highly user-centric in their delivery model and usability. Less well-known is the fact that some of the most advanced have revolutionised the analytical possibilities for L&D professionals because LEPs track delegate behaviour and tests what works and what doesn’t (based on internal new ways of collecting data such as the xAPI).

[…]

What this means in practice is that the HR or Chief Learning Officer is increasingly the recipient of data-based insights and gets to exploit all sorts of new types of insight – not only what someone has learnt, but how the learner got there and which learning approach they chose. This opens up the possibility for new performance indicators, such as Curiosity, or Resilience – both hugely valuable HR metrics. And of course, this will ultimately aid the workplace learner – as the learner become aware of what her own data says about her progress and experience so as to ensure long-term employability.

The transformative potential of these new indicators is even greater if you consider that the World Economic Forum identified re- and up-skilling 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, curiosity on the other hand seems less ubiquitous, and many commentators believe we need to boost employee curiosity as well as to build greater resilience and adaptability to change.

[…]

So let’s help prepare our teams for this uncertain but dynamic future and see what LEP and xAPI-enabled training feedback and KPIs can give us: a new source of analytics that means that HR professionals and incentives professionals can use multiple, appropriate, data sources to properly consider the full candidate potential of a person for a specific job – not only in terms of their knowledge and skills, but also their curiosity and aptitude for change. Not only are these traits important ones to cultivate, but they are also important ones to keep.”

You can read the entire article here.

You can also read these other articles from Jean-Marc Tassetto.

Jean-Marc Tassetto’s interview for French television (BFM Business).

Is LXP the new LMS – Enterprise Times

Computational Thinking: a key skill in the 21st century

 

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