People in high and common places seem to agree that the world is in transition, especially workplace learning, as innovations keep changing what is possible. As I am working on one such an innovation (the skill project of InnoEnergy), I am at the one hand very excited about the new opportunities it might open, yet at the same time concerned that the complexity is bigger than expected.
First: have a look at the demo screencast here. It shows the overall idea, and ... this might immediately give rise to questions.
Today the Business Booster event (TBB) is opened, and with it, the skill project demo is launched. The skillproject (we still need to get a brand name for it), is combining AI and learning for the sustainable energy sector. But in essence, once we get the sustainable energy sector mapped with this tool, others can follow.
AI and learning? What does it do: the project identifies industry needs (AI-driven), pinpoints emerging skill gaps in the sustainable energy sector (AI-driven), analysis the existing workforce to know where urgent skills gaps are situated (AI-driven) and then refers employees to a personalized learning trajectory addressing their skills gap (part AI, part human support). The goal of this project is to ensure that employees of the sustainable energy sector stay futureproof in a quickly changing working environment. Let's be honest, it sounds cool, but ... the challenges are multiple.
The emergence of a Learning Engine
The skillproject helps realize the emergence of a learning engine, an intelligent career-oriented engine which knows your own skills and which signposts you to where you want to go with your career by suggesting a personalized learning track.
In the Learning Engine you simply type in “goal: become Director of Innovation’s in offshore wind energy which courses?” and the engine immediately returns a tailored, personalized learning track consisting of a variety of certified, business training from both universities, corporate academies, open educational energy resources and coaching options to send you on your way. This will allow professional learning to surpass the limits of classical, university-based learning.
In order to get our engine to come up with the best, most-tailored courses, we need access to industry academies, as well as university courses.
Learning-to-Learn capacities. Once we signpost learners to a cluster of courses, they need to take them (the familiar 'take the horse to water' comes to mind). But even if the learners are taking the courses,
Granularity for course clustering: clustering courses to keep on top of your field of expertise is one thing, but then what is the granularity of those courses? Micro-learning is an option, and modular learning will become a clear necessity, as all learners have different existing knowledge, which means they all need different parts in order to upskill what they already know.
Ensuring pedagogical continuity, even OU finds that a challenge. Great, so let's cluster modules. But then, how can we link these modules together, Do we believe in the non-pedagogical support (e.g. hole in the wall from Sugata Mitra already dates back 10 years), or do we need to find a solution to provide pedagogical continuity that fits with this new assembly of short modules, and courses coming from different sources (both university and industry)?
Certification across the learning ecologies: to blockchain or not to blockchain. Once we start learning across institutes, we need to keep track of that what we learn, by keeping tabs on the actual learning: corporate academy learning, university modules, hands-on training, workplace learning... one solution is to embed blockchain in education to keep track of all learning. But this is easier said than done, and open standards and trust might be an issue to consider (bit of trust initiative offers good reading).
Feel free to send questions, comments, share your own projects... let's get together.