Showing posts with label onlinen learning. Show all posts
Showing posts with label onlinen learning. Show all posts

Monday, 24 June 2019

Can people be pushed into #mandatory #learning? Old myths in new mantra's #learning #pedagogy #instruction

Let's be clear: teachers still are not transforming into guides-on-the-side, contemporary-online-learning is not a fabulous learning utopia (we can build it, but we lack whom we want to reach) and pedagogy is now debilitated through new innovations in learning. At least that is my frustration of the day. Let me explain (picture credit: PhD comics.com).

As I am getting more into the 'AI helps people to be trained in a personalized way'-project (officially called the skills3.0 project, slides here), I am starting to feel uncomfortable with some of the ideas that emerge and resonate with false assumptions found 20 years ago:

  • the old elearning assumption: if you build it, people will come (they did not, at best you need to market it ferociously in order to attract some worldwide learners - confer MOOCs). But when looking at the numbers and the degrees, it is still rather weak in terms of successful tailored learning resulting in professional learning enhancement. In most cases, MOOCs cover the basics, not the advanced side of professional topics.  
  • another one: having to transform instructors (defined as sage-on-the-stage) to guides-on-the-side (something which is repeated by Norris Krueger in his blog article 'from instructor to educator' with a focus on entrepreneurial education). This idea of guide on the side stems from 1981, which means in the last 38 years we haven't managed to get there... this does show it is hard to expand people to embrace a different approach to learning. For in my opinion the best teachers have always been guides-on-the-side, they inspire their students and lift them to their own next level.  
  • The debilitation of pedagogy: I cannot get around this tendency to oversimplify learning, and almost dismiss the proven, evidence-based pedagogy we - the learning researchers - established over the last 30 years. For years fellow researchers in online learning were testing, investigating, reiterating learning options, to see what worked best. And as soon as the market took over, all is reduced to .... classic courses, with one speaker who delivers knowledge but barely listens, clearly a sage-on-the-stage model (MOOCs) and all of us learners discussing and sharing knowledge with each other in the discussion areas in order to tailor what was said to our own situations (social learning, which actually happens in face-to-face courses as well). The only thing that is added to the sage-on-the-stage in most of the MOOC cases is 'fancy video' and a 'new type of Learning Management System' (cfr. Coursera, FutureLearn, EdX... they are basically LMS's with some extra's). Yes, some people learn from the hole in the wall, some do, but most of us don't. So why do learning data scientist and innovators in their new learning tools think that all of humanity will start to learn simply because they say: here it is, this will get you in a better career position. And even if this would be the case, please tell me who would have these actual magic courses, for who can build courses at the speed of the emerging, changing industry? And if we build them, who will be waiting, filled with anticipation and willingness to follow these courses?
I feel frustrated that learning is again be seen as simply a thing that all of us do, and for industry-related reasons. Honestly, I think most of us learn informally (proven!) and if we learn for professional reasons we need to be able to spend time on it (HR enabling time), and if we were to be allocated time to learn, it should be allocated in terms of our own capacity for learning, based on our own background in learning (using a holistic approach to pedagogies). 


In order to move forward with the Skills3.0 project, there are several elements that need to come together and make sense in order to scale the project as well. These elements are:

  1. Using AI to filter out industry needs (which means you look at all the reports from industry, and analyse which new concepts arise from these reports to predict where the industry is going)
  2. Using AI to analyse which true experiences (and related competencies and skills) a person has: based on LinkedIn profiles, current CV's...
  3. Finding the skills gap between both previous steps: getting to know what people might be missing in order for them to answer to upcoming industry needs,
  4. And finally pointing them to training/courses/workshops that might push them to be better for the future jobs. 

The project is taking off (see movie at the end, to see where we are at, I look a bit tired in it, or maybe simply older).
The last step is underestimated by most of the non-educational people. At present learning cannot be put into simple formula's, it is the complexity of life itself, it is why everything evolves in the long and in the short term, including us humans. 

All of the above steps of the Skills3.0 project are laudable. If this works, it has a broader societal meaning, you can even say it provides a way to direct people to a more fulfilling professional life. But... that feels like a Utopian emotion following new innovations. We can see how providing guidance to courses that will help each one of us to perform better, to enhance our careers, to find new professional challenges, ... is a good thing. The only problem is, that humans are also bound to their own learning characteristics (e.g. Big five personality traits, or more academically the learner characteristics guiding their own self-directed learning).

Simply providing courses might not be enough, we need coaching, workshops, orientational sessions which depict which types of learning will benefit you most (e.g. if we look for data science courses online, which ones are useful to each of us individually? that will depend on what we know, where we want to use them for, and how we learn (for me, numbers are a challenge)).

Whether we say learners must self-direct, or self-regulate or self-determine their learning, inevitably this means we are talking about learners that are willing to learn, and are capable of learning. Indeed, in the near future we will ask learners to learn at a speed that is ever increasing, meaning you need to be a really good learner to keep up with your own changing field. Can we do this? And if we can, how does it work?

Short video on the Skills3.0 project recorded during the WindEurope conference in Bilbao. Which will lead to 'building the workforce':


Thursday, 12 February 2015

#Fun testing boundaries of #DeepFace

Since the announcement of DeepFace and its consecutive reasonance in the media, the facial recognition algorithm from Facebook, it aroused both interest and critique. There are many arguments to consider privacy issues before sending out these types of identity related software's out there ... into the public world. But no matter what the status of the philosophical decisions is, DeepFace is now ready to be fully deployed after a successful pilot.

Every type of technology is embedded in a context and ecology, which makes it an integral part of a holistic society. And as a human instrument, it inevitably leads to many discussions whenever it changes contemporary habits. Nevertheless, each technology also adds to a bit of fun. And I see it as an informal duty of each learning technologist, tech geek... or all-round nerd-joker, to investigate the fun-factor of these types of algorithms. And that was what I was thinking about during last night.

The DeepFaced-facts
  • Deep Face recognises more people than I ever would (I have trouble recognizing faces, and not coming anywhere close to the 97,5 % average of most people), and has almost reached human recognition stats (97,25 %).  
  • The rotation challenge: DeepFace uses a 3D model for rotating faces virtually so that the person in the photo appears to be looking at the camera. 
  • The algorithm draws its power from Deep Learning, a visual as well as audio (language) recognition system set-up by Google. Where deep learning has reignited some of the grand challenges in artificial intelligence, due to its use of computational power, use of big data, and adaptation capacity.
So take DeepFace to the challenge
Provide DeepFace with some additional challenges, while at the same time expand your EdTech tool-use
History is being rewritten, we all know this, and most of the time history is written by the victors (Churchill). It will never be different, nevertheless, it might be fun to try and contaminate some of history's facts with us - the normal people. Which also makes it into a nice 'how would you use this tool'-action for any multimedia class, online or face-to-face. Some options:

  • Photoshop yourself into (Facebook) history. It almost feels like old-school this photo-shopping, but it never hurts to rethink old options. By placing yourself into histories key moments, Facebook might pick-up your presence at these key points, and of course Deep Learning might adjust itself to 'this person could not have been here!?!', but then again it might start to calculate you must have been here if you work yourself into these picture from different angles (in doing so, making yourself even more experienced with photoshop). Me with Ghandi, me with the new Greek president Alexis Tsipras (would love this), me with ...
  • Exploring the boundaries of morphed images and DeepFace. Another fun activity, that will allow you to see how much tweaking you can do to your own face, before DeepFace stops recognising you. As a test I already morphed me with my son. Quick online morphing option: 3Dthis.com .
  • Finally an answer to 'does everyone on earth have a (or more) look-alike/s'. If facial recognition is indeed working, it might reveal that there is another Ignatia out there somewhere... and I would like to meet her, facebook might make this possible (what is the return rate for DeepFace on successfully recognising twins?). But I do hope my look-alike is not mixed up with too much hustling though... how dangerous could that be for my identity? and what if people make masks mimicing my face?... 

As you can see: fun guaranteed. I feel that I should add this concept of the Fun-test to my repertoir on getting and screening new technologies. 

Friday, 16 January 2015

#Googleglass out, other smart tech in? Where is #educational bonus?

The wearable technologies are booming business, but a lot of it is still very expensive. And with Google just releasing today that Google glasses will be reinvented, if not stopped, it got me thinking about cost and educational options. Just think about all the developers that bought the Google glasses (1500$) and now get the news that the project is being reinvented. Or about those schools that purchased one set allowing students to research its functionality?
Certainly when looking at smart glasses, there is a lot of expensive material (coming) out. As multiple options are being launched (or are on the verge of being launched) I do wonder what to go for, budget wise. For if the half-life of tech is only about a year... it might not be wise to invest in it? Time or budget wise.

Cheap virtual reality and smart glass solutions are increasingly being rolled out, but as with all technology: multiple companies are trying to corner the market, but in the end only a few will keep on standing (and it seems tech launches and halts happen quicker than ever). A couple of nifty options: the 'classic' Google glasses which is now being rethought, the more advanced Meta space glasses, the more stylish looking (yet with wire hanging from ear) Antheer lab option, of course for gamers the Oculus rift or the about to be released Sony's Morpheus, and for the more cognitive oriented among us the EmotiveInsight headset which is said to be available in April 2015 and which monitors brain activity. But it does cost a lot of money (ranging from cheapest 350 $ to 1500$).The latest from Microsoft is Microsoft's Hololens which merges virtual and IRL nicely together.

On the one hand it is clear that smart-everything is the way forward, but the cost of each item makes it tricky to test all of them in order to find its educational value. Using such tech in classrooms or global courses is at the moment cheer impossible, unless... you choose for the cheaper option: e.g. Google cardboard. This virtual reality app/option allows everyone to either build a Google cardboard from the Google cardboard kit which turns a mobile into a virtual machine, or to buy a cheap cardboard box to be used with a smartphone (and apps which you can search for depending your mobile operating system: e.g. android - Google play, but also to be said working with iPhone ).

What is interesting when looking at all these smart technologies, is all of them rely on crowd-development to provide more meaningful features or applications for their hardware solution. That of course does have a very interesting educational bonus, as it is clear that this supports peer knowledge creation based on a API's or other boundaries provided by a couple of experts. An interesting shift that has been increasingly growing the last couple of years. The same is possible for the cheaper options as well, as such it makes these options (like Google cardboard) a nice jumping board for young developers with a knack for programming or creative solutions.

Looking at some options that are out there for Google cardboard (some of which are also available on the more expensive gears, like oculus rift): Tuscany house: a nice application that shows the opportunities for design and architectural simulations that can be made in class. A more old school tech option: hang gliding and flight sim(ulation) app: decades ago, I was using flight simulator to get a feeling of what it took to fly a plane. It was (and is) fun, and it is instructional as simulations allow a more authentic preparation for the actual IRL action. Or more subject matter related options: e.g. moon, which takes you to a virtual moon surface.

All of the apps can offer educational value, but I keep wondering what the extra bonus would be. What can it teach us that we were not able to be taught in the past. What does it allow me to do, that really lifts learning to the next level? All in all, I see the smartglasses as performance enhancers, more than re-imagining education. The simulations bring real life, authentic learning closer to home; designs can be viewed in 3 dimensions, ... but it seems they all keep within learning/teaching that already existed. Just wondering what it could be, what I am missing.

Google cardboard assembly picture from here. And a really nice, short description of the Google cardboard in this YouTube movie: