Keynote talk of Anita Schjoll Brede @twitnitnit @oebconference #AI #machineLearning #oeb18

(liveblog starts after a general paragraph on the two keynotes that preceded her talk, and really her talk was really GREAT! And with fresh, relevant structure).

First of a talk on the skill sets of future workers (the new skills needed, referring to critical thinking, but not mentioning what is understood with critical thinking) and the collective intelligence (but clearly linking it to big data not small data, as well described in an article by Stella Lee).

Self-worth idea for the philosophy session, refer tot he Google map approach where small companies who offer one particular aspect of what it took to build google maps were bought by Google, and as such producing something that was bigger than the sum of its parts). But this of course means that the identity and the self-versus-the-other becomes under pressure, as people that really make a difference at some point, do not have the satisfying moment to think they are on top of the world (you can no longer show off your quality easily… for there are so many others just like you… as you can see when you read the news, follow people online…). While feeling important was easier, or possible in a ‘smaller’ world, where the local tech person was revered for her or his knowledge. So, in some way we are loosing the feeling of being special based on what we do. Additionally, if AI enters more of the working world, how do we ensure that work will be there for everyone, as work is also a way to ‘feel’ self-worth. I think keeping self-worth will be an increasing challenge in the connected, and AI supported world. As a self-test, simply think of yourself, and wanting to be invited to be on a stage… it is a simple yet possibly mentally alarming aspect. Our society is promoting ‘being the best’ at something, or having the most ‘likes’, what can we do to install or keep self-worth?

Than a speaker on the promise of online education, referring to MOOCs versus formal education, the increase of young people going to college… which strangely contradicts what the most profiles of future jobs seems to be like (professions that are rather labour intensive). The speaker Kaplan managed to knock down people who get into good jobs based on non-traditional schooling (obviously, my eye-brows went up, and I am sure there are more of us in the audience pondering which conservative thinking label can be put on that type of scolding stereotype speech, protecting the norm, he is clearly not even a non-conformist).

Keynote by Anita Schjoll Brede 

Here a person in the line of my interest takes the stage: Anita  Schjoll Brede. Anita founded an AI company Iris.ai , and tries to simplify the AI, machine learning and data science for easier implementation. So… of interest.

Learning how to learn sets us human beings apart. We are in the era where machines will learn, based on how we learn… inevitably changing what we need to learn.

She gives what AI is seen by most people, and where that model is not really correct.

Machine learning is based on the workings of a human brain. Over time the machine will adapt based on the data, and it will learn new skills. It is a great model to see the difference. One caveat, we still not sure how the human mind really works.

If we think of AI, we think of software, hardware, data … but our brains are slightly different and our human brains are also flawed. We want to build machines that are complementary to the human brain.

Iris.ai started with the idea that there are papers and new research published every day, humans can no longer read all. Iris.ai goes through the science and the literature process is relatively automated. The process is currently possible with a time decrease of 80%. Next step is hypothesis extraction, than build a truth tree of the document based on scientific arguments. Once you have the truth trees are done, link that to a lab or specific topic, … with an option of the machine learning results leading to different types of research. Human beings will still do the deeper understanding.

Another example is one tutor per child. Imagine that there is one tutor for that child, which grows with that child, helps with lifelong learning. The system will know you so well, that it will know how to motivate you, or get you forward. It might also have filters to identify discriminatory feelings or actions (remark of myself: but I do wonder, if this is the case, then isn’t this limiting the freedom of saying what you want and being the person you want to be… it might risk becoming extreme in either way of the doctrine system).

Refers to the Watson Lawyer AI, which makes that the junior lawyers will no longer do all the groundwork. So the new employees will have to learn other stuff, and be integrated differently. But this relates to critical ideas of course, as you must choose for employing people (but make yourself less competitive) or you only higher senior lawyers (remark of myself: but than you loose diversity and workforce).

Refers to doctors built by machine learning, used in sub-Saharan settings, to analyse human blood for malaria. Which saves time for the doctors, health care workers… but evidently, this has an impact on the health care worker jobs.

Cognitive bias codex (brain picture with lots of links). Lady in the red dress experiment.

Her take on what we need to learn:

Critical thinking,  refers to source criticism she learned during her schooling.

Who builds the AI, lets say Google will transgress the first general AI… their business model will still get us to buy more soap.

Complex problem solving: we need to hold this uncertainty and have that understanding. To understand why machines were lead to specific choices.

Creativity: machines can be creative, we can learn this. Rehashing what is done, and making it to something of your own is something that is (refers to lawyer commercial that was built by AI based on hours of legal commercials).

Empathy: is at the core of human capabilities like this. Machines are currently doing things, but not yet empathic. But empathy is also important to build machines that can result in positive evolutions for humans. If we can support machines that will be able to love the world, including humans.

#oeb_midsummit Cognitive neuroscience and learning by @BekkeringHarold

Harold Bekkering reminds me of some of my Dutch family, funny feeling. Once he took the stage, it really was like listening to cousin Folkert.

Everything is connected in the brain, and the brain is a predictive machine.

A neuron at work is incoming data is output in just one unit. So in neuroscience we see that multiple inputs triggers a process into one output.

Hebbian learning: everything is connected (Hebb’ theory).

A human can adapt to one specific tone of voice and timbre. Our brain adjusts.

The brain is a prediction machine. Read Karl Friston and Andy Clark.

Distributed knowledge in the brain. Exteroceptive, autonomic (interoceptive), motoric (proprioceptive) given the conceptual multimodel representations. The brain tries to make the best things for you. It is a multimodal representations, which makes it of interest.

The whole brain is summed up in error correction.

E.g. you walk to a door (prediction is going through the door), but if a door is closed you adjust your action based upon the perception after the prediction.

Amal and Giraud, TICS 2012, beta and gamma power. The brain is only active when you make errors. Only after making an error your model is updated [interesting].

We learn more from negative feedback. Oh no! 😃

Creating a safe environment to learn from errors.

Social learning: humans are by nature social, homo imitans.

Dunbar (1998) The social Cortex, it is big because we have huge networks. The Dunbar’s number.

Social learning is needed due to the brain needing to be matured over time (baby to adult).

Good tip for learning: saying: hi (insert name) and then say the action. The brain does not ignore the calling of a name to become active.

Mirror neurons: have revolutionised neuroscience. Motorcortex was investigated, and in the lab, monkeys mimicked humans picking up peanuts. There are cells that only get active if we see someone else moving. Your brain cannot help to observe and react.

Learning analytics: extracting data, predicting data, … data driven education.

It is not useful to only learn in one learning style is the real point of interest, if you want to really learn the best thing to do is to use all styles for learning.  

Lessons for online learning

Hebbian learning: be careful with what you offer

Predictive learning: structure and provoking errors (job aids similar)

Social learning

#OEB16 opening plenary live blogpost on owning learning

Owning learning: A great session with a range of experts on the topic of current learning problems… and possible solutions. This is a live blogpost.

Tricia Wang (designing for perspectives: the secret for learners to thrive in the 21^st Century)

The individuals are getting too much of the blame. We need to design for perspectives, (@triciawang ).

Sally Ride was the first female astronaut. Once she stopped flying she developed earthKAM, which enables teachers to use the camera on the space station. The students can select the coordinates, and it gives the students a hands-on space experience.

The power of using tech and learning, at best the students really feel science. It introduces a new perspective for students. Video and photography provides participatory options for learning. Technology has seen so many innovations now applied to learning, it is mindblowing. Machine learning is a fancy term to describe what we do with computers, where computers get actions from humans.

Machine learning is a 3;7 billion industry, but what are its limitations? Machine learning still requires human designers and quality data. If the humans overseeing the training are not aware of their own biases, these biases result in the output.

Technology has not increased our understanding of the world. Example: the white interpretation of google photo’s. It is a result of failing to see outside of our perspective.

These technology mishaps happen a lot. Machine biases, Propublica 23 May 2016. (the high risk offender example).

Machines are directing our learning, as such biases in these machines might result in more biases in learning. No one wants these biases to be embedded in machine learning. But the outcomes reveal the limited perspectives of their creators. And this happens easily, perspective collisions happen.

Representing heterogeneity is a difficult challenge. We are still not truly globally connected. The social part is something all of us have a hard time with. Getting a multiplicity perspective is the challenge. There s a lot of confusion, as everyone gets to speak, this means all of that ends up into the social texture of life. So we need to teach people to navigate their own lives through this new social, machine lead system. Perspective shifting, a new form of media literacy, taking into account people that are not like you. This will be one of the most critical skills, but humans need to be trained for this. It is a learning behaviour, so qualitatively learning these skills is possible and should be a priority to enable a global world with true equality.

Relying only on quantitative data, it risks us to be blind by the known. This is why we need people that can actually address these multiple perspectives. To get outside the binary: replace the binary divide with the connected network, to ask different questions (computers replace humans), but why do we not ask what humans can do to work with computers to reduce biases. Always integrate both quantitative AND qualitative data to eliminate the risk of bias. In a pluralistic society, we need this approach.

Look up caroline synders or sinnders… for work with machine learning and ethonographers.

Andreas Schleiger (supporting learners globally to own learning)

The last couple of years we all got experienced at coping with economic crisis. If you look at who found solutions, skills seem to be the key driver to battle inequality or crisis. People at the high end of the skill spectrum see themselves as actors, while the low end sees themselves as objects.

Even today, corporates tell us there are no skilled workers, yet more people are looking for jobs.

So it is about skills and using them, learning them.

How to be ready for social problems, like jobs that will be erased. It is not about robots, just about automation. But augmented reality can bring the real world into any location. Google knows everything, and there is a huge challenge that is coming our way. There is no longer a digital economy. The economy is a digital economy.

People work harder now, then ever before, but the declining levels of productivity is affecting work. There is a growing divide that people with the right skills have less opportunities, thus those without skills have even less options.

The race between technology and skills.

Digital problem solving skills: finding solutions for every day problems. Only 1 in 5 people above 50 years can do this. Even if we look at people 16 – 24 suffer, as only one in two young people can solve every day challenges.

Lots of people are being left outside. The only area where employment grows is the high skills jobs. This is where the economy is quite stable (admittedly, the pay is decreasing for these jobs).

What skills are important: knowledge, integration of different fields of knowledge (think like historian, philosopher, technology… all at the same time will increase your skills and stability). And then looking at details to solve problems via different viewpoints.  

The world rewards for the opposite, for thinking about systems, not the details.

Digital literacy, global literacy… those different perspectives become the challenge.

Skills that matter today is critical thinking, creative thinking. Solving complex problems, social skills, communication…

But something controversial as skills is: resilience, figuring out problems when you cannot see the solutions, curiosity, mindfulness, ethics, courage, leadership, inclusion, empathy. Making judgments becomes more important, that is complex. Self-awareness.

Everything we do reinforces what we did when we were young. If we think of the science changes… it is amazing, we have developed 3D printing, iphones, google maps… you no longer need to teach people something, but skills.

Fundamental success in life: numerical skills (eg data), there is a direct relationship between low skills and declining jobs.

You no longer need to accumulate degrees, but contemporary skills mentioned above.

Literacy skills, learning to learn, cross-sectional skills. We need to teach people these skills to enable them to be able to find the right jobs. 

Roger Schank (who owns learning, not you, maybe AI can help).

This is a person you just need to talk to. The talk will be hilariously invigorating. 

Who owns learning is my question. Everyone but yourself. As the system tells you what to learn, with similar requirements, interpretations of what is best. 

Eliminate testing. The politicians support the testing industry. And forcing testing, forces what teachers need to teach.

Let’s built online learning that does not suck and really teaches us a lot of useful skills. 

Artificial Intelligence: at a certain moment it was put in the freezer due to over-expectation at some point. But now it is again a big business.

At present models human intelligence, but it is not. Schank mentions AI mentor (look up). 

Looking forward to @EmotivInsight for self-determined learning

Brainwear that would enable enhanced learning monitoring? Oh, I would love to get my hands on that, so I immediately registered for the Emotiv Insight headset that is said to come out around summertime 2014 (new release date April 2015).

The US based design teams who wanted to work with the Emotiv Insight headset got together during a Designathon in February 2014, which led to 20 projects being selected that could promote an innovative use of the upcoming Emotiv Insight headset. No European event set up as yet it seems. The inventor is Tan Le, an Australian innovator with a growing impact and interest. Tan Le and her team have been working towards the emotiv insight brainwear for years, a first notion of this type of headset was featured in a Ted Talk of 2010, which already featured the Epoc headset that allowed users to move objects in the virtual world. 

So, waiting patiently for any message from the Emotiv team coming my way. In the meantime, let me share why I find this bit of brain gear of interest for research and (online) learning. The basic set up is: a portable EEG that allows the user of the brain-wear to better understand their own brain functions (all be it, brainwaves), which can be connected to focus, state of creativity, suggestions (telekinetics), and such.
The nice hook of the Emotiv Insight headset being developed, is that its predecessor the Emotiv Epoc can now be bought for less money (if you add the promotional code mentioned on Tan Le's Facebook page - Emotiv-FB-14  for a 100$ discount on the Epoc).  

Practically, what does it say it can do: 

"Emotiv Insight is a sleek, 5 channel, wireless headset that records your brainwaves and translates them into meaningful data you can understand. Designed for everyday use, Insight has advanced electronics that are fully optimised to produce clean, robust signals any time, anywhere. We've also developed cutting edge, dry polymer sensors that give you the same great electrical conductivity without the setup or saline"

You just pop the headset on your skull, and you can start monitor your brainwaves, and from their build options to either research your brainwaves, use them to guide objects, or simply look at what specific emotions trigger in your brain. The Emotiv Insight was funded through Kickstarter and reached well over its target funds. And I must agree that I wished I had known about this a couple of months ago, as the research Insight gear set could be bought for 399$ through Kickstarter (really sorry I missed this opportunity!). 

Would love to get my hands on an Emotiv Insight brainset, as it would allow me to test out the research SDK and investigate learning patterns connected to informal learning, procrastination, creation, knowledge resolution... Would need to dig into the SDK, but I can see how this might link in with the Quantified/qualified input I was looking for in a previous post. 

Nice stuff it seems.

#mLearn12 Lauri Jarvilehto on #games the learning playing brain

Learning as Fun: introducing gaming pedagogy by Lauri Jarvilehto.
www.filosofianakatemia.fi

Wonderful keynote linking gaming to pedagogy to neuro-science!

We can reproduce relevant information, because we engaged in it previously.

Games that truly engage children and high quality substance, might result in the best possible learning.

Flow channel: learning and engagement (Milhaly Csikszentmihalyi) sits on an axis of challenges and skills.

Boredom: (interesting Tedtalk Robinson related to ADHD non-accurancy) versus anxious.
Flow = total immersion of something that is happening now (cfr the Zone).

As teachers, you can redirect children into the flow.
We all have the neural mechanisms to get into the flow, but we need to be lucky!

Learning and the brain
Learning => new neuron connections.
The brain is constantly evolving.

Concentrated work: you need to switch of outside impulses.
Creative thinking is pushed by games, colorful things...
=> due to constant dynamic of our brain and how it is wired.

Dopamine is the most rewarding hormone => works at optimal level, giving a good feeling, it pushes our prefrontal areas to concentrate better. The neural links are strong and lasting, so being engaged and happy strengthens your learning and knowledge durability.
But overactivating the dopamine level => getting anxious.
Brain study of emotion has shown that emotion pushes learning (ex. sick by soup => no longer soup).

2 successful learning: having fun or dictatorship system => both result in emotionally constructed learning. Example: tiger mom approach (VERY strict raising, ex. Agassi's dad, forced to play tennis).

So how do we get our children into learning and play
Playing is an evolutionary requirement of every living creature (Stuart Brown: how it shapes the brain, 2009).

Experiment: stop playing (no intrinsical motivation), only do 'serious work'. By the end of one day, the majority of people showed sever symptoms of depression. Playing is at the very heart of what we are.
Play is also optimal for getting into the flow. And play can push us just a little bit further, hence learning.
Neuroplasticity is one of the major neuro-science fields of today (e.g. the brain can restructure, if a certain area is disconnected, other parts of the brain can take over, BUT the challenges first need to be lowered to get back into the flow).

Games (and horror) rise the dopamine levels in the brain => affecting learning.
Good games automatically adjust to the gaming actors.

Danger of non serious games: a game without substance will lift the dopamine level, but without a learning award (e.g. Angry birds (classic), there is Angry birds Space (substance)).

Substance is VERY important to get a meaningful, rewarding experience.
Example dragonbox: algebra application with really complex algebra, and based on dragons and boxes.

We all have all the tools available, so let's see what we can out of this.

(Inge, look at this flow model for your own learning!)

The importance to stimulate children/student/youngsters #brain #medialearning2011

Peter Adriaenssens (expert in Child and Adolescent Psychiatry at KULeuven) speaks at the conference of Media and Learning in the Flemish Parliament in Brussels, Belgium. Some notes from his speech.

Brain and educational practice
All starts at the neuron level. These neurons and their branches will form a neural network. From birth to 2 years, this neural network is growing at a quick speed.

this process is genetically determined, but not all will be needed, this is were the genetic will meet nurture. Parts of these will be pruned as theings are 'not needed'. So it is imperative to take this neural plasticity into account when stimulating children from early on.

These synapsis will keep growing all the way to half of the twenties. This is the work that needs to be done for the biggest part in the first 18 years. Exposing the young brain to a wide variety of stimulus will decide the quality of emotion and behaviour. It is a continuous process of adaptation of the brain. this brain needs meaningful interactions to grow, so exposure to media, creativity, ... will actively stimulate growth (language, concentration, ...). This will result eventually in adolescent behaviour.

Children that are not exposed to stimulae, will result in a very poor set of neural networks.

As such society and all of us in it, are responsible for brain stimulus.

Exposure to stimulae are at the center of the discussion about violent exposure by media etc. But why do not we set up research centers about 'good media' instead of looking at the negative impact (personal note: good point!).

The Brain
cosists of many different parts that work together to make meaning of the world
nees appropriate nutrituion to reach it maximal potential
needs appropriate sleep in order to work effectively
needs ongoing developmentally appropiate stimulation
learns most effectively when information is provided in the child's prefrered larning or thinking style

Important to explore the language at which children feel at their best, might be dance, physical, creativity.... this push for positive stimulae is important for their brain as children have different preferences (or their brains have).
Provide children with camera's, internet, media tools...

Critical though of myself: lack of infrastructure will result in a sustained digital divide with low resource areas and/or groups.

Are we able to prove that media stimulus will result in an improved child development?
We do not have the data to found this, but we can set out some guidelines.
Growing importance of social skills. And making them open to democratical, critical analysis to be ready for this world. Lifelong learning is a need, but does education taking care of this need? Are schools redesigned to allow them to provide lifelong learning?

Adults should think more like babies, as a babies brain is the most powerful #learning computer with amazing #cognitive skills

Informal learning is one of the key topics in this knowledge era. We all use it, and informal learning comes as natural to us human beings as eating. If this quest for knowledge is rooted in each one of us, it starts from the moment we are babies.

Ever since I became a parent (four months and a half now) I have been wondering at times why I put in that much effort into this small baby that barely gave me any signal of recognition in the beginning. But although I did wonder about that from time to time, I kept/keep giving; telling myself that it will pay in the long run. But at times – especially when he has one of his more difficult days – I just think, babies grow up no matter what I do, so why should I invest my precious time anyway!!! *angry, unreasonable fit*. It is on those moments that my learning network can come up with answers that south my soul, telling me it does make a difference and that no matter how intense raising a baby can be at times, investing and nurturing life is the best option no matter what.

Luckily for my son, my partner is the one really giving at this moment – at home 24/7. Always there, almost always patient and with a remarkable – truly remarkable – sense for stimulating the cognitive brain of our preciously young baby (she’s a teacher at heart). Learning him to grab, crawl, speak on the basis of a never relentless stream of examples and motivational acknowledgment when our son takes action.

Alison Gopnik was the one enlightening me this October morning. She works on the subject of cognitive development and draws on psychological, neuroscientific and philosophical works to create and prove her own research findings. She is a professor at Berkeley.

In this 18 minute session on the cognitive characteristics of babies and preschool children Alison says:

How is it possible that babies know so much more then we think they do?

The answer lies in evolution. There is a relationship between the duration of the childhood of a species and how big the brain is of that species. Crows, rooks… are very smart birds (like chimps), they have a longer lasting childhood than for example chickens. And when looking at these two species the crows are much capable of solving complex dilemma’s then chickens.

So the duration of childhood is the connection to knowledge and learning. This also means that evolution had to come up with a solution to allow learning to happen along many years before autonomy steps in. This might be why children are so dependent for such a long time (in general and looking at western society).

A revolution has been taken place in the understanding of our brains and how it evolves. This is based on findings of reverent Bayes on machine learning (based on probability theory) and started from why scientists come to results, and he put it in a mathematical model.Babies use the same mathematical model: babies make complicated mathematical equations that allow them to distill how the world works. To proof this hypothesis, Alison did some simple preschool statistic tests (with four year old's). In a short time, the 4 year old's found the correct probability measure, and they use these results to shape their world. The interesting thing here is that 4 year old's actually are better at finding improbable theories than adults do (GREAT stuff!).

Children actually do experimental research, but it is called ‘playing’.

Adult cognition is different from child’s cognition, as the adults are much less open, very focused, purposed driven span of attention. Children are less focused, but more open, holistic information intake. So they are flooded by synapse dynamics.

Until you have done all that learning, you are vulnerable. Evolution enables us to learn.

At times adults consciousness can expand and meeting the children’s consciousness: this happens when we are in a completely new situation (in love in Paris after 3 double espresso’s).

So if we – as humans want to achieve the openness of mind that will enable us to come up with new hypothesis in a matter of instants, we – as adults need to think more like children again.  

Big Question: The twitter generation: skimming to get content? No thx, I am a struting!

In the Big Question launched by Tony Karrer this month, he wonders how we look at instruction in an information snacking culture of skimming content. With twitter, mobile msn, and sms rushing through our lives, and knowledge growing exponentially, one could wonder if we pick up content in a more and more abridged form and whether this is affecting our learning. I think it is, so I am turning down once and awhile and I am … strutting through education. Staying alive!

The way in which I point towards interesting content has been changing, I used to comment more, but nowadays I just retweet or ‘like’ and ‘digg’ stuff more often. This omits me from adding personal remarks to the content I am redirecting. The fact that I do not add possible question marks to the redirected content means that I do not go through all the processes Bloom so eloquently described. With redirecting I skip the highest digital learning level, namely: creating. So I wonder if I am selling myself short with this byte-size content assimilation.

I found myself rushing through much more than just those short messages, I was skimming articles, skimming posts, skimming social media, skimming newspapers, skimming… everything but my milk. And although it gave me the idea that I was doing things much more efficiently as I was speeding up in this speeding age, I also found myself becoming more stressed.

So is speed reading and content snacking the way to go? Sometimes I feel it is very save to take this option: personal life, social contact, procrastination… but for my professional life I have come to a turning point, I have started to slow read again. Now, I did not yet go to tortuous mode yet, but I am aiming to get there. For did not the tortoise outrun the hare in the end?

Snacking content might well be the way our brain processes content anyway, in that case we do not even need to care, but on the other hand a lot of educators expressly tell us that narratives add to a deeper understanding. So how do we link skimming content with a deeper understanding of that same content? Lots of questions, but I still belief narratives and in-depth reading add to long-term understanding, even if we live in an ever rapidly changing world, understanding needs to account for something? And so I feel instruction should also allow people to take time and know they do not need to rush or skim. Taking time is okay.

So instead of running, I decided from this year onward to strut content. Struting in a new world Grand Theft auto IV style.

Joining the Web Research 4 Beginners online conference next week... take a look

For the researchers out there that are interested in using online sources to build up their research, this is a great meeting point: the Web Research 4 Beginners Online Conference.

What can you expect?
Starting 22 June up until 26 June, during five days, two presentations a week will be given by a variety of speakers. The conference is hosted by the University of Manitoba, home of Web Research conference.
They even have a conference tab for any kind of following purposes: WR4B09.

The conference is multidisciplined, with international speakers and it can all be followed wearing your pyjama's or any which way you want to.

Let me just copy from their homepage:

"Is web research for me?

Ever consider online data collection for your research? Can web research be practical for longitudinal studies? Could one implement a clinical treatment online? Are ethical issues tractable? What features are important in survey research software? These and other questions will be addressed by this online conference, which is designed to provide the curious web research novice with an overview of the issues, promise, and pitfalls of online research.

The convenient conference format will allow you to:

• participate from a personal computer anywhere,
• attend two presentations each day for five days,
• hear expert speakers talk live,
• watch their slides at the same time, and
• ask questions by text or voice.

Compelling reasons to attend:

• Speakers with academic and private sector backgrounds from 6 countries;
• Really inexpensive registration fee — CAD$40;
• No travel required (well, OK, that could be a drawback);
• No accommodation costs (use the saved money for something fun);
• Multitasking opportunities if things get slow;
• Small carbon footprint;
• Minimal pandemic risk; and
• Flexible dress code (pyjamas?).

If the topics look interesting and the schedule works for you, register now."

I do not know about you, but I am enrolling myself immediately...

AG08 session Evidence-based eLearning Methods to Build Creative Thinking Skills by Ruth Clark

(For the live blogging info look below the AG08 synopsis)

wikibook Ruth Clark

(AG08 synopsis) In a global economy, organizations must increasingly rely on adaptive expertise for innovative competitiveness. What evidence do we have on using e-Learning to accelerate expertise and build a more innovative workforce?

Session participants will join Ruth as she guides you through research and practice on creative skills training, drawing from the 2008 edition of e-Learning and the Science of Instruction. You will see examples, identify best-practices, review evidence, and apply a design template for problem-based e-Learning to accelerate expertise and build thinking skills.

In this session, you will learn:

• The features of learning environments proven to build creative thinking skills
  
• The research evidence on effectiveness of problem-based learning
  
• To use your own content to plan a problem-based learning lesson

Live from room Palm 3 at Hilton, Orlando, Florida
(my thoughts are in italic)

You can download the handouts (ppt) at this link, look at session 206.

Ruth Clark is immediately diving into it and ... she is more active then I felt she would be based on the synopsis. She really gets people thinking right at the beginning. She uses visuals and humour, so really the advocate of what she is talking about.

well designed creativity training programs typically induce gains inperformance... What makes creativity programs stick?
there were between 100 and 200 different types of programs, but they can be divided in 3:
puzzle problems: think outside the box (or like Nicola Avery said last month: the box is bigger then you think!)
critical thinking skills: (brain busters creative programs)

The most effective creative thinking is domain based problems (so this is really good because it means problem based problem in a job-specific context).
meta analysis of 70 studies proved this (Mumford, 2004)

Weisberg studied what made creative, famous people creative geniuses.
We are building on the creativity of others. For example: DNA a lot of scientists looked at DNA, but nobody until the (three) scientists worked on a model DNA science did not have a break through.

Domain specific scenario-based learning environments (this is where the ustream recording starts)
a very simple example: a medical ethics example on life ending topics (she very nicely tells people that this is a very sensitive topic) the example is text based with the option to choose your role and/or sex. The example shows what people can be confronted with in case a relative is in a life threatening situation and the family has to decide on whether or not the patient will be taken of life suport. An American example with legal differences according to the different state legislations.
At the end of the module you can type in what you think would choose and you can reflect on your choice and if necessary change your choice.
This focused on a specific job focus and was job-focused, so good.

case based versus traditional example: 163 medical students in 3 week rotation in orthopedics. Test group: case based online plus faccetoface discussion from traditional didactic methods
Comparison group: traditional program based on lectures besides turorials.
Because the test group could look at a lot more (simulated) cases, that group scored better and was more motivated because it is relevant.

Sherlock: accelerating expertise sherlock (troubleshooting aircraft electronic troubleshooter). Gott & Lesgold, 2000 (exposed learners 25 - 20 hours on sherlock had the same knowledge growth as people with 10 years of experience) => so this can accelerate expertise.

Job-specific problem scenario: jot down a job role in your organization that involves creative problem solving, list some of the types of problems that workers in that role solve, describe the setting in which problems are solved.

metacognition is a priority for problem solving (see Allen Schoenfeld)

model thinking process: modeling the own learning skills
practice making decisions: practising making decisions on

put students in little groups and going around asking: why are you doing it, what other answers are there, what could you have overlooked... this approach (reflection) will augment learning outcomes.

next case: for your selected problem, sketch some ways you can use eLearning to
1 model thinking skills
2 give practice in thinking skills
3 give feedback in thinking skills
(everyone is using her or his brain to solve this next interactive question)

Ruth mentions virtual agents to guide you through simulations, collaboration, guidance worksheets (a plee for legal professionals)

think about: for your selected problem, sketch some ways you can use eLearning to provide guidance. (everyone is thinking again, only a few dropouts at this point, probably because they do not see why they should think if nothing is done with the thinking process - if Ruth would have told the goal in the beginning, I wonder if the dropouts would still be thinking at this point?)

building creative thinking skills
focus on a few specific skills - translate vague requirements into action items;
create job-specific scenarios that integrate ...

Now a high media example of simulations (again an army example, I think simulations were really done at first in the defense departments around the world - also a little bit logical as you cannot prepare for combat anywhere else, but ... still, it is a pity that violence is a technology boost) a multimedia simulation preparing people to be prepared in war zones... (a lot of macho, military talk, I just believe in peace and investing in peace - I am a gandhiïst) This approach allows inexperienced soldiers to be a bit better prepared in a foreign situation and it is .

challenges
- collecting job scenarios
- defining cognitive and metagcognitive processes
- training design trade offs (dollars and time)

• scope?
• interface?
• media intensity?
• complexity?

unknowns for future research
how realistic?
can integrate type 2 (brainstorming) into domain specific learning?
(and another thing, did not get this... phew what pace!)

conclusion: this was a knowledge packed session with a LOT of reflective moments. This made it more difficult to liveblog because... it took a lot of multitasking in my brain.