The Big Lebowski

The Big Lebowski by the Coen brothers is my all time favourite movie. I am not the only one who feels this way. The movie has inspired a whole movement of followers. I’m a Lebowski, you’re a Lebowski, a book describing this movement, gives a wonderful insight into why thousands of people come together every year for a Lebowski fest where they watch the movie on a big screen, dress up like characters from the movie, host a trivia competition and announce books that are published about the film. In one of these books, Mary Zophres, responsible for costume design, talks about dressing the protagonist:

I’ve used a lot of drop shoulders on him because when somebody has higher seams, it somehow improves the posture and makes their look seem more put-together and tidy, which of course we didn’t want. [..] I know this all seems like a very subtle thing, but from a costume designer’s point of view it does make a difference. And if you make sure that you’re doing it the right way down to the basics, then you’re assured of getting the overall effect you want.

This shows the extraordinary high level of authorship of the Coen brothers. The quote made me realise that one of the reasons that this movie gets better every time I see it, is because every single element in the movie is put there by the directors for a purpose. Nothing is there by chance or by the fact that it was just there when they came around to shoot a scene.

Unusable stuff

We all have had the experience of trying to turn on one of the burners on a stove and randomly trying out the knobs to see which one works. Donald Norman explains in The Psychology of Everyday Things the cause of this problem: the burners are arranged two by two and the knobs are in a single row of four. There is no natural mapping between the two. Why not? Because even though we all know the problem, there has never been a designer who has cared enough to think about a solution and implement it (i.e. if the knobs were arranged two by two then we would never make the mistake). Often aesthetic reasons get first priority. I keep a Twitter account, @unusablestuff, dedicated to documenting these design follies.

Paying attention to the title bar

Like what appears to be all of the technology world, I too am fascinated enough by Apple’s disruption of multiple markets to have devoured the biography of Steve Jobs as soon as it came out. One passage that really struck me was the following:

Jobs lavished [..] attention on the title bars atop windows and documents. He had Atkinson and Kare do them over and over again as he agonized over their look. [..] “We must have gone through twenty different title bar designs before he was happy,” Atkinson recalled. At one point Kare and Atkinson complained that he was making them spend too much time on tiny little tweaks to the title bar when they had bigger things to do. Jobs erupted. “Can you imagine looking at that every day?” he shouted. “It’s not just a little thing, it’s something we have to do right.”

This shows that he was able to take the tacit view of the user of his products. A view that the user might not even be able to verbalise themselves.

What does this mean for learning design?

These three stories are all about ways of looking at the world that are sorely missing from a lot of elearning design nowadays. So ask yourself the following questions about the next piece of elearning that you design:

• Do you see yourself as an author in the sense that you are fully responsible for the experience that the learner has? Did you look at the end results with the eyes of the learner? Do you realise that the thing you create might be seen by thousands of pairs of eyes?
• Did you make a conscious design decision about every single part of your elearning module and does everything that is included have a clear purpose? Or did you just use things that were turned on by default or put in things because that is the way it is always done?
• Have people around you been talking about the Pareto principle (the 80/20 rule) and are you therefore delivering something that is mediocre? Do you like interacting with things that are mediocre?

To summarise: Details matter, so please act like they do.

---

P.S. I have just started reading On Writing Well. I intend to use the lessons in that book on this piece of writing. I am curious to see how much it can be improved!

The problem

She used [Shakespeak to ask us a set of questions about whether we sometimes feel overwhelmed by information coming our way and whether we are sometimes distracted. Most people in the room answered these questions positively. She then asked how this made us feel: most people seemed to feel confused, stressed or oppressed. Why is this?

• There is too much information
• Too much replication of information (Joyce Seitzlinger pointed out that is actually also a signal for its importance)
• Difficulty in separating the relevant from the irrelevant
• Lack of time

The first solution: train people

Julie has done a few informal learning projects, setting up portals, microblogging (Yammer) and discussion forums. Initially this took off like a rocket. But suddenly it stopped working: people were starting to say that they liked it, but that they . She started solving the crisis by using Shirky's adagio "It isn't information overload, it is filter failure". She started to train people in how they should work with information through aggregators, filters and all kinds of other tools. This actually made the problem worse: people only got more information coming their way. Shiffman wrote in Wired in 2008:

Now that the first burst of enthusiasm for social networking has died, people are realizing that web 2.0 is actually a huge time sink. Facebook, Twitter, Flickr and Plaxo may have helped foster community and communication, but they’ve also added immensely to the flow of often-interruptive messages that their users receive, leading to information overload and possibly a nasty internet addiction.

In The Shallows , there are two types of information overload:

1. Situational overload: searching for a needle in a haystack (of information)
2. Ambient overload: a haystack-sized pile of needles (information)

The second is the problem her clients had. There really was too much good information.

The second solution: curation and a curation framework

Berners-Lee described three principle functions of the Internet:

• Allow anyone to access any type of document
• Allow everyone to disseminate their own documents
• Allow every to organize the entire collection of documentations

The last element is now actually happening on the web in an organic way. We are curating the content organically through our Tweets, likes, shares, etc. We should curate to link the content to business/learning initiatives, identify what is relevant in a particular context, see what the right signposts are.

They implemented this very explicitly through "listening centers". Small teams would listen to all the information sources and tried to match things to themes that relate to business goals and then assign "theme" curators.They then created a curation framework. For each piece of information they decided in what theme it would fit, for who it would be relevant, how much time it would take to review and when the data would expire.

Julie then gave us some practice exercises: we had to curate three pieces of content. Her advice is to really make things really time sensitive, really add value to the piece of content that you curate and it should actually showcase learning. It is also important to find subject matter experts, work with the communication department and external organizations.

Some tools to help with information overload

Julie recommends a few tools that might help with information overload. Readitlater or Instapaper can help you get more reading done. Another tool that is interesting is Symbaloo which allows you to create a visual and shareable set of favorite links around a topic. Her “mix” for content curation is available here. She uses Scoop.it! a lot. Learnfizz is in beta and similar to Scoop.it!, but will eventually work inside organisations.

My thoughts on this

This was an excellently prepared session: properly designed with a good mix of activities and information. I have to admit though that I don’t believe that her curation framework solves the problem of information overload for the true knowledge worker (i.e. for somebody like me) because it is just an extra information source. It is an interesting extra layer on top of internal social networking tools though: basically a slower and more focused source. Three things worry me:

• Scalability. What happens if the internal information become so big that it can’t be manually curated effectively anymore? Would it be possible to automate this? Could we use something that is similar to Summify.
• Quality. How we know that the curator is doing a good job and the most important thing isn’t missed?
• Specificity (i.e. how personal is it). We all know that everything is miscellaneous and to me a “recommendation” should be to an individual not to a group.

Martin Moehrle

Martin Moehrle, the ex Chief Learning Officer of Deutsche Bank AG titled his talk “The Learning Function as a Performance Improvement Business”. He started by rehashing the traditional way the the learning function proved its value to the business. The old arguments work pretty well in the “industrial” age. In these times of crisis, we have to again discuss the causal chain from learning to performance and we might need some new arguments.

Three things need to change:

1. The learning function as we it today is a product of the industrial age, however as we move into the knowledge age the performance logic is changing. The modern enterprise is a mix between industrial and knowledge-based contexts. In the industrial context you can manage workforce mastery through prescribed work procedures. In the knowledge-based context you manage via connectivity, commitment and inspiration. The workflow is not predetermined, it is at the discretion of the individual. The ownership of the means of production moves from the organisation to the individual. The traditional way that learning works is much less relevant on this knowledge-based side. The learning function currently has multiple roles like: helping to create a learning culture that is trustful and based on shared perspective, an enterprise change agent, business development and innovation, governace of the enterprise learning space. This is all very much formal learning with high control from the learning function. The informal part of learning where there is a low control is a space that the learning function doesn’t like to go. Moehrle thinks that the learning function does need to take some responsibility there, else it will become irrelevant.
2. The learning function needs to have both a macro-view on performance along the value chain as well as a micro-view that focuses on the individual and the team. If you do not have the macro-view you will not be able to get the most out of the performance improvement potential.
3. The performance management process needs to center much more on performance improvement than on assessment of past performance. We need different metrics for the learning function to be able to do this. We cannot focus on the throughput measures and the happy sheets.

Monika Weber-Fahr

Monika Weber-Fahr from the International Finance Corporation (IFC) gave a talk titled: “Where the Rubber Hits the Road: Building Performance Cultures for Delivery”. She is an economist and strategist working for a finance organisation. Her organisation operates in around 90 different countries, mostly in emerging markets. They do private development sector finance with a mission to provide opportunities for people to escape poverty and improve their lives. They not only invest, but also advise and consult. Her talk was quite far removed from the standard scope of the learning function and was therefore sometimes hard to contextualise for me.

She shared three stories from three different companies in these emerging markets. One characteristic is that these markets grow very fast and so are these companies. In these markets there are big disparities in energy access, education and technology (she India as an example: normal Internet access is still not very dispersed, but mobile subscription is now very high). For education these emerging markts are now about 20 years behind where the leading markets are.

Some things are working for emerging markets: cost advantages, well managed quality and profitable in their own right. But certain things are still hard: access to finance, still unclear and non-transparent managed. The IFC’s focus is moving away from a single company to disaggregated global networks of companies.

They have identified a couple of success factors in this space:

• Balance standardization and customization. One interesting example was the SME toolkit that they developed. This is highly standardised content (developed together with IBM) but at the same time highly localisable.
• Connect operations and training
• Build partnerships for reach

Fabrizio Cardinali

Fabrizio Cardinali is the chair of the European Learning Industry Group (ELIG). His talked was about what he now named “The Sputnik Effect” and titled “The Learning Industry Sputnik Challenge: How Can We Get Europe’s Learning Industry (First) to the Moon and Back in the Next Decade?”. He started his talk by showing how scared people in the US were in the late fifties during the cold war, followed by Kennedy talking about putting a man on the moon. Kennedy said: “Do it right and do it first before the decade is over.”.

To Fabrizio this can also be a wake-up call for our industry. We have a couple of big issues facing us and we need to reduce our “missile gap”. According to him we need to understand creativity and genius. He showed us a book written by Frans Johansson, The Medici Effect and Gelb’s book How To Think Like Leonardo da Vinci. Two of terms from that second book that are very relevant in our current situation are “Sfumato” which is managing ambiguity and change and “Connessione” which is systems thinking.

He talks about a renaisssance 2.0 where there are dichotomies like local diversity versus global normalisation, intersectional creativity versus monosectorial innovation, public leadership versus public debate, new Entrepreneurship trust & risk versus old bank bailouts and open co-opetition versus blind competition. His main point seems to be that you need to be fully adaptable to change to be able to survive global competition.

Talal Abu-Ghazaleh

The opening plenary was opened by Talal Abu-Ghazaleh, the President of the Talal Abu-Ghazaleh School of Business in Jordan. He made the same joke as last year (a Churchill quote: “one of the greatest lessons that I’ve ever learned is that idiots are sometimes right” ). He came up with a couple of provoking points: he considers “developed” and “developing” offensive words which we should stop using. He think our talk about a “global crisis” is incorrect, there is a “Western crisis”. The word “spring” in “Arab spring” was sprung upon the Arabic world and is a concept that isn’t used in their literature, he says we should consider it a renaissance instead.

Neelie Kroes

Next up was Neelie Kroes, Vice President of the European Commission and European Digital Agenda Commissioner. I’ve complained before that I am fed up with seeing her face on some big screen delivering a recorded message. To my great surprise she was actually present at the conference.

She discussed how she is on a learning curve herself, because when she became commissioner, she inherited the digital agenda, which was relatively new for her. She has now understood that sharing is the best way to compete: that was counterintuitive to what she learned before. According to her, we should realise how incredibly fast we are changing: the digital economy is only about 20 years old, the iPad only two. People now expect access to information anywhere, anyplace and anytime. At the same time she is disappointed in how the digital revolution is being used in education. We have not changed education enough. Her goal is “Every European Digital”, that includes teaching and learning (also as part of lifelong learning). We should not be constrained by how things were done in the past, rather we should be creative.

Three key ingredients are necessary to do this:

1. We need to make digital literacy and digital skills central to the public agenda. If not there will be a skills gap. We need to reach out to everyone and especially women (who are currently underrepresented.
2. We must use the full range of funding and support that is available from the EU.
3. If we are serious about tackling the problem, let’s engage all stakeholders and let’s be honest about cultural problems around change. Those who control the money might be reluctant to make investments with a long time to pay off. It is not about gadgets, but it is about empowering teachers. It is not a cost, but an investment in human capital. Technology can tailor an individual learning and teaching experience.

Changing learning through technology might not be an overnight process, but it will be revolutionary. We are moving in the right direction, but we do need to speed up.

Peter Nowak

Peter Nowak, is a technology commentator from Canada. He is the author of Sex, Bombs & Burgers. His talk was titled “Food Technology and the Rise of Entrepreneurial Education”. His focus is on the effect of technology on people and society. According to him the dark side of human nature, gluttony, wrath and lust, and the industries that have sprung up around it have driven much of technological development.

Instinctively we think that food and technology is bad, whereas food and nature is good. This is not true, we are dependent on food technology to feed ourselves. Food technology is one of the biggest drivers of technological change. Half of the G8 nations are on the top eight list of biggest food producers in the world (the Netherlands is number four on that list). He then touched upon the green revolution which enable countries to become food exporters (he did also mention the criticism of that same green revolution: introducing corporate farming for profit and creating dependence on chemical fertilizers).

We are currently in an unprecedented period of poverty reduction. We have reached our millemium goals around poverty. 500 million have escaped abject poverty in the year 2005 to 2010. According to Edward Prescott, Nobel Prize winning economist, “the whole world is going to be rich by the end of this century”. Nowak believes that a massive decrease is poverty, will lead to many new jobs, which will massively increade education demand. Is this educational world ready for this onslaught of demand? One problem is the lack of teachers globally. We will need 8 million new teachers to maintain current teacher-student ratios. Demand will likely outstrip supply. We will need to accept entrepreneurial learning where both teacher and student are responsible for learning.

Nowak then references Mitra’s hole in the wall experiment, after which Mitra said: “I think we have stumbled across a self-organising system with learning as an emergent behaviour.” Learning is in our DNA, we just need to the opportunity and tools to help us. We used to have to learn the tools of the medium to express ourselves, but this is not longer the case. Amateurs can now use technology as quality is no longer necessary for self-expression.

His four points around changing education are:

• Learning is instinctive
• Technology is making it easier
• Old-school attitudes must change
• Entrepreneurialism is the most immportant skills that can be taught

John Bohannon

John Bohannon is a journalist and visiting researcher at Harvard University in the US. His talk is titled: “Without Google and Wikipedia, I Am Stupid”. He really does want to argue this point. The Flynn effect is a well-known trend: IQ has been increasing steadily around the world. What is the reason for this? Are we really getting smarter?

He then talked about the Google effect and the research around it. We all now use the Internet for our “transactive memory”. When we are now asked a question, we immediately think of where can find the answer: Google, Yahoo, etc. Students are now far better at remembering where the information is stored, rather than the information itself!

Your Google footprint is the one-stop-shop from somebody’s identity. Single publications (e.g. a Guardian article) can have a disproportionate effect on somebody’s reputation. He did not offer any quick solutions to this problem, I think he just wanted to raise awareness around these issues.

Jeff Borden,

Next up was Jeff Borden, Senior Director of Teaching and Learning (chief academic) at Pearson, the platinum (closed source) sponsor of the Online Educa. This was a sponsored talk, titled “Always learning: What Educators Want and What Education Needs”. Pearson/Fronter have learned from mistakes in the past when everybody disliked Larsen’s talk (there even was a slide with Moodle and Sakai) and now send true evangelists to come and present. There was so much evangelism for technology in education that I occasionally felt like was in a 15 minute Tell-Sell commercial.

He had some great examples of using learning analytics to find out why student succeed or fail. An average passing student spends two times more time in the first 10 days of a course, then the average student who will fail. This can be used for successful predictions about which students will make it. The next step is to use this type of data for personalising the experience of the learner. We can use the social graphs of courses to then make intervention in this area to create better outcomes for students. Basically a data mining approach to help fix education. This is an obvious reference to the #bigdata scenario in our #lrnscen scenario exercise.

Borden “Technology is a powerful enabler for changing education.”

Some quick commentary

While semi-liveblogging there is little time for reflection. One thing I noticed is that only two of the five speakers used slides to support their talk. I actually believe this is a lost opportunity there. It isn’t strictly necessary to use them, but it does often help. Overall I thought the talks were rather weak. Most of it wasn’t very groundbreaking and even a bit obvious. Exception to this was John Bohannon. The part of his talk about transactive memory was new to me and is, I think, at the core of why formal learning feels so outdated: it does not take this into account at all. His Google searches for his fellow speakers were both painful and funny. He used it to show how messiness of Wikipedia: on the one hand it is incredibly useful, but at the same leaves very much to want for.

1. International Online Edubloggers Dinner

On Thursday December 1, 2011 Wilfred Rubens and I organize the International Online Educa edubloggers dinner.

2. Preparing together for the future of corporate learning

Utilization of excess capacity empowered by collaborative platforms

Robin Chase gave the clearest explanation of this theme that many speakers kept referring back to:

This world has large amounts of excess capacity that isn’t used. In the past, the transaction costs of sharing (or renting out) this capacity was too high to make it worthwhile. The Internet has facilitated the creation of collaborative platforms that lower these transaction costs and make trust explicit. Chase’s most simple example is the couch surfing idea and her Zipcar and Buzzcar businesses are examples of this too.

Entangled with the idea of sharing capacity is the idea of access being more important than ownership. This will likely come with a change in the models for consumption: from owning a product to consuming a service. The importance of access shows why it is important to pay attention to the (legal) battles being fought on patents, copyrights, trademarks and licenses.

I had some good discussions with colleagues about this topic. Many facilities, like desks in offices, are underused and it would be good to try and find ways of getting the percentage of utilization up. One problem we saw is how to deal with peak demand. Rick Marriner made the valid suggestion that transparency about the demand (e.g. knowing how many cars are booked in the near future) will actually feed back into the demand and thus flatten the peaks.

A quick question that any (part of an) organization should ask itself is which assets and resources have excess capacity because in the past transaction costs for sharing them across the organization were too high. Would it now be possible to create platforms that allow the use of this extra capacity?

Another question to which I currently do not have an answer is whether we can translate this story to cognitive capacity. Do we have excess cognitive capacity and would there be a way of sharing this? Shirky’s Cognitive Surplus and the Wikipedia project seem to suggest we do. Can organizations capture this value?

Disintermediation

The idea of the Internet getting rid of intermediaries is very much related to the point above. Intermediaries were a big part of the transaction costs and they are disappearing everywhere. Travel agents are the canonical example, but at the conference, Paul Wicks talked about PatientsLikeMe, a site that partially tries to disintermediate doctors out of the patient-medicine relationship.

What candidates for disintermediation exist in learning? Is the Learning Management System the intermediary or the disintermediator? I think the former. What about the learning function itself? In the last years I have seen a shift where the learning function is moving away from designing learning programs into becoming a curator of content and service providers and a manager of logistics. These are exactly the type of activities that are not needed anymore in the networked world. Is this why the learning profession is in crisis? I certainly think so.

The primacy (and urgency) of design

Maybe it was the fact that the conference was full of French designeurs (with the characteristic Philippe Starck-ish eccentricities that I enjoy so much), but it really did put the urgency of design to the forefront once again for me. I would argue that design means you think about the effects that you would like to have in this world. As a creator it is your responsibility to think deeply and holistically. I will not say that you can always know the results of your design (product, service, building, city, organization, etc.), there will be externalities, but it is important that you leave nothing to chance (accident) or to convenience (laziness).

There is a wealth of productivity to be gained here. I am bombarded by bad (non-)design every single day. Large corporations are the worst offenders. The only design parameter that seems to be relevant for processes is whether they reduce risk enough, not whether they are usable for somebody trying to get something done. Most templates focus on completeness and not on aesthetics or ease of use. When last did you receive a PowerPoint deck that wasn’t full of superfluous elements that the author couldn’t be bothered to remove?

Ivo Wenzler reminded me of Checkhov’s gun (no unnecessary elements in a story). What percentage of the learning events that you have attended in the last couple of years adhered to this?

We can’t afford not to design. The company I work for is full of brilliant engineers. Where are the brilliant designers?

Distributed, federated and networked systems

Robin Chase used the image below and explicitly said that we now finally realize that distributed networks are the right model to overcome the problems of centralized and decentralized systems.

I have to admit that the distinction between decentralized and distributed eludes me for now (I guess I should read Baran’s paper), but I did notice at Fosdem earlier this year that the open source world is urgently trying to create alternatives to big centralized services like Twitter and Facebook. Moglen talked about the Freedombox as a small local computer that would do all the tasks that the cloud would normally do, there is StatusNet, unhosted and even talk of distributed redundant file systems and wireless mesh networking.

Can large organizations learn from this? I always see a tension between the need for central governance, standardization and uniformity on the one hand and the local and specific requirements on the other hand. More and more systems are now designed to allow for central governance and the advantages of interoperability and integration, while at the same time providing configurability away from the center. Call it organized customization or maybe even federation. I truly believe you should think deeply about this whenever you are implementing (or designing!) large scale information systems.

Blurring the distinction between the real and the virtual worlds

Lift also had an exhibitors section titled “the lift experience“, mostly a place for multimedia art (imagine a goldfish in a bowl sat atop an electric wheelchair, a camera captured the direction the fish swam in and the wheelchair would then move in the same direction). There were quite a few projects using the Arduino and even more that used “hacked” Kinects to enable new types of interaction languages.

Most projects tried, in some way, to negotiate a new way of working between the virtual and the real (or should I call it the visceral). As soon as those boundaries disappear designers will have an increased ability to shape reality. One of the projects that I engaged with the most was the UrbanMusicalGame: a set of gyroscopes and accelerometers hidden in soft balls. By playing with these balls you could make beautiful music while using an iPhone app to change the settings (unfortunately the algorithms were not yet optimized for my juggling). This type of project is the vanguard of what we will see in the near term.

Discomfort with the dehumanizing aspects of technology

A surprising theme for me was the well articulated discomfort with the dehumanizing aspects of some of the emerging digital technologies. As Benkler says: technology creates feasibility spaces for social practice and not all practices that are becoming feasible now have positive societal impact.

One artist, Emmanuel Germond, seemed to be very much in touch with these feeling. His project, Exposition au Danger Psychologique, made fun of people’s inability to deal with all this information and provided some coy solutions. Alex Peng talked about contemplative computing, Chris de Decker showed examples of low-tech solutions from the past that can help solve our current problems and projects in the Lift Experience showed things like analog wooden interfaces for manipulating digital music.

This leads me to believe that both physical reality and being disconnected will come at a premium in the near future. People will be willing to pay for having real experiences versus the ubiquitous virtual experiences. Not being connected to the virtual world will become more expensive as it becomes more difficult. Imagine a retreat which markets itself as having no wifi and a giving you a free physical newspaper in the morning (places like this are starting to pop up, see this unplugged conference or this reporter’s unconnected weekend).

There will be consequences for Learning and HR at large. For the last couple of years we have been moving more and more of our learning interventions into the virtual space. Companies have set up virtual universities with virtual classrooms, thousands and thousands of hours of e-learning are produced every year and the virtual worlds that are used in serious games are getting more like reality every month.

Thinking about the premium of reality it is then only logical that allowing your staff to connect with each other in the real world and collaborate in face to face meetings will be a differentiator for acquiring and retaining talent.

Big data for innovation

I’ve done a lot of thinking about big data this year (see for example these learning analytics posts) and this was a tangential topic at the conference. The clearest example came from a carpool site which can use it’s data about future reservation to clearly predict how busy traffic will be on a particular day. PatientsLikeMe is of course another example of a company that uses data as a valuable asset.

Supercrunchers is full of examples of data-driven solutions to business problems. The ease of capturing data, combined with the increase in computing power and data storage has made doing randomized trials and regression analysis feasible where before it was impossible.

This means that the following question is now relevant for any business: How can we use the data that we capture to make our products, services and processes better? Any answers?

The need to overcome the open/closed dichotomy

In my circles, I usually only encounter people who believe that most things should be open. Geoff Mulgan spoke of ways to synthesize the open/closed dichotomy. I am not completely sure how he foresees doing this, but I do know that both sides have a lot to learn from each other.

Disruptive software innovations currently don’t seem to happen int the open source world, but open source does manage to innovate when it comes to their own processes. They manage to scale projects to thousands of participants, have figured out ways of pragmatically dealing with issues of intellectual property (in a way that doesn’t inhibit development) and have created their own tool sets to make them successful at working in dispersed teams (Git being my favorite example).

When we want to change the way we do innovation in a networked world, then we shouldn’t look at the open source world for the content of innovation or the thought leadership, instead we should look at their process.

Your thoughts

A lot of the above is still very immature and incoherent thinking. I would therefore love to have a dialog with anybody who could help me deepen my thoughts on these topics.

Finally, to give a quick flavour of all my other posts about Lift 11, the following word cloud based on those posts:

Mobile Augmented Reality

Jie-Eun is part of a team developing an AR management platform for the web titled Cellophane funded by the culture/tourism ministry. One part of the project is mapping cultural expressions (like movies, drama, pictures, drawings and advertising) onto the city. Imagine being able to watch a movie and seeing a place you are interested in. You would then be able to visit the place either virtually or in real life. It can also work in the other direction: what movies are shot in the area? The tool comes with a nice admin interface allowing you to match the cultural expression to the physical space with a simple point and click interface.

They have the ambition to push beyond the current capabilities of apps like Layar. They overlay some icons and text on the camera view. For some reason it is quite difficult to use and doesn’t have a very good user experience.

Use cases

What invisible elements can we reveal through this medium? What types of data would we like to get (that go beyond the obvious things like gaming and tourism). In small groups we prototyped a couple of ideas using a use-case template.

I worked with Catherine Gall, Director of Workspace Futures at Steelcase. We first thought about the potential for mobile augmented technology to help in never making the same mistake twice. This could be at the level of the individual, the organisation or maybe even larger concepts like cities. How come you make the same mistake on that tax form every year? Why do you go a second time to a restaurant that you don’t like. We reflected on how a sense of space could help you in memorize things. We finally settled on an idea titled Location based well-being analytics. Certain places (in the sense of locations, but also spaces), events and situations affect our well-being in a consistent matter (be it positively or negatively) without us necessarily being aware of that. Many companies our now designing little monitors that measure your body for things like activity/movement, calorie intake, blood pressure, temperature, sugar levels and more. In the future these devices might even measure some form of quantified emotional state. Some mobile technology could combine your (intended) location with the historical data of these devices to predict how the location will affect your well-being and give out recommendations. This could be useful for people with fragile health or people who are rehabilitating. Alternatively it could just help people become more aware of their own well-being and how the environment affects this.

Other groups had ideas like:

• Moody community: in a community you would have a wall where you would be able to see the mood of the community as it is aggregated by individual “mood” statements by the residents of the community. This could actually help build a community. Who would use this data?
• An augmented mirror that you can use to try on clothes in which you can easily change the colour or fit etc.
• Supporting professional teams during crisis with incredibly relevant and targeted information.
• Maintenance: the system would recognise the part you are working on and it would recognize the context of what you are trying to do. The system would then be able to overlay extra information on reality, including maintenance history, particular advice or the gesture that you need to do.

My personal open questions after the session

• All of the solutions assume that you are connected to the net for them to work. Can we afford to make this assumption or should we still explore ways of having the data that augments locally? Might there be other models? Mesh networked? Where the device would get the data from the environment on demand?
• Imagine a future in which everything you do is recorded in many dimensions (solving the problem of needing to capture your learned lessons). Would this help you in not making the same mistake twice? What kind of interfaces and experiences would be necessary to not only learn from your own mistakes, but learn from other people’s mistakes? How would you now a “lesson from a mistake” would exist? Would it need to be pushed to you?
• For current mobile performance support technology we usually think about location, direction, and maybe some RFID technology as “cues” to match the virtual content to reality. What other cues can be used sensibly? Light? Sound? Temperature?
• A recurring question for me in the last couple of years is whether we start lusting for a non-technology mediated experience of reality. Will we put a premium on experiencing something for “real”? Can you see a future where you have “Augmented Reality Retreat Zones”?

Introduction

We work in a virtual team. Even though there aren’t many of us, we often have few ideas about what the other people in our team are working on, which people they have met recently and what they are struggling with. The time difference between our main offices make our occasional feelings of being disconnected worse.

This “Narrating Your Work” experiment is an attempt to help overcome these problems.

If you are interested in some background reading, you should probably start with Luis Suarez’ blog post about narrating your work (”it’s all about the easiest way of keeping up with, and nurturing, your working relationships by constantly improving your social capital skills”) and then follow his links to Dave Winer, ambient intimacy and declarative living.

The Semantic Web and Linked Data

This was the main topic of week three of the course. Basically the semantic web has a couple of characteristics. It tries to separate the presentation of the data and the data itself. It does this by structuring the data which then allows linking up all the data. The technical way that this is done is through so-called RDF-triples: a subject, a predicate and an object.

Although he is a better writer than speaker, I still enjoyed this video of Tim Berners-Lee (the inventor of the web) explaining the concept of linked data. His point about the fact that we cannot predict what we are going to make with this technology is well taken: “If we end up only building the things I can imagine, we would have failed“.

The benefits of this are easy to see. In the forums there was a lot of discussion around whether the semantic web is feasible and whether it is actually necessary to put effort into it. People seemed to think that putting in a lot of human effort to make something easier to read for machines is turning the world upside down. I actually don’t think that is strictly true. I don’t believe we need strict ontologies, but I do think we could define more simple machine readable formats and create great interfaces for inputting data into these formats.

Microformats: where are the learning related ones?

These formats actually already exist and they are called microformats. Examples are hCard, hCalendar and hReview. These formats are simple and easy to understand and are created in a transparent and open process. Currently it does require some understanding of how these formats work to be able to use them, but in the near future this functionality will be build into the tools that we use to publish to the web. So just by filling in a little form about yourself you would be able to create an editable piece of text with an embedded hCard microformat.

So where are the learning related formats? I think it would be great to have small microformats that can describe a course or a learning object. I am aware of Dublin Core and IEEE LOM as ways of describing content, but these are a bit too complex (and actually do mix data and presentation is some weird way). Is anybody aware of initiatives to create some more simple formats? Are they built into any existing learning-related products?

Thinking about this has inspired me to add two microformats to my blog. The little text about me now contains machine readable hCard information and the license at the bottom of the sidebar is now machine readable too (using rel=”license”). I will also start to work on building my resume into the hResume format and publish it on my site. Check http://www.hansdezwart.info/qr in a couple of weeks to see how I have been getting on.

Use cases for analytics in corporate learning

Weeks ago Bert De Coutere started creating a set of use cases for analytics in corporate learning. I have been wanting to add some of my own ideas, but wasn’t able to create enough “thinking time” earlier. This week I finally managed to take part in the discussion. Thinking about the problem I noticed that I often found it difficult to make a distinction between learning and improving performance. In the end I decided not to worry about it. I also did not stick to the format: it should be pretty obvious what kind of analytics could deliver these use cases. These are the ideas that I added:

• Portfolio management through monitoring search terms
  You are responsible for the project management portfolio learning portfolio. In the past you mostly worried about “closing skill gaps” through making sure there were enough courses on the topic. In recent years you have switched to making sure the community is healthy and you have switched from developing “just in case” learning intervention towards “just in time” learning interventions. One thing that really helps you in doing your work is the weekly trending questions/topics/problems list you get in your mailbox. It is an ever-changing list of things that have been discussed and searched for recently in the project management space. It wasn’t until you saw this dashboard that you noticed a sharp increase in demand for information about privacy laws in China. Because of it you were able to create a document with some relevant links that you now show as a recommended result when people search for privacy and China.
• Social Contextualization of Content
  Whenever you look at any piece of content in your company (e.g. a video on the internal YouTube, an office document from a SharePoint site or news article on the intranet), you will not only see the content itself, but you will also see which other people in the company have seen that content, what tags they gave it, which passages they highlighted or annotated and what rating they gave the piece of content. There are easy ways for you to manage which “social context” you want to see. You can limit it to the people in your direct team, in your personal network or to the experts (either as defined by you or by an algorithm). You love the “aggregated highlights view” where you can see a heat map overlay of the important passages of a document. Another great feature is how you can play back chronologically who looked at each URL (seeing how it spread through the organization).
• Data enabled meetings
  Just before you go into a meeting you open the invite. Below the title of the meeting and the location you see the list of participants of the meeting. Next to each participant you see which other people in your network they have met with before and which people in your network they have emailed with and how recent those engagements have been. This gives you more context for the meeting. You don’t have to ask the vendor anymore whether your company is already using their product in some other part of the business. The list also jogs your memory: often you vaguely remember speaking to somebody but cannot seem to remember when you spoke and what you spoke about. This tools also gives you easy access to notes on and recordings of past conversations.
• Automatic “getting-to-know-yous”
  About once a week you get an invite created by “The Connector”. It invites you to get to know a person that you haven’t met before and always picks a convenient time to do it. Each time you and the other invitee accept one of these invites you are both surprised that you have never met before as you operate with similar stakeholders, work in similar topics or have similar challenges. In your settings you have given your preference for face to face meetings, so “The Connector” does not bother you with those video-conferencing sessions that other people seem to like so much.
• “Train me now!”
  You are in the lobby of the head office waiting for your appointment to arrive. She has just texted you that she will be 10 minutes late as she has been delayed by the traffic. You open the “Train me now!” app and tell it you have 8 minutes to spare. The app looks at the required training that is coming up for you, at the expiration dates of your certificates and at your current projects and interests. It also looks at the most popular pieces of learning content in the company and checks to see if any of your peers have recommended something to you (actually it also sees if they have recommended it to somebody else, because the algorithm has learned that this is a useful signal too), it eliminates anything that is longer than 8 minutes, anything that you have looked at before (and haven’t marked as something that could be shown again to you) and anything from a content provider that is on your blacklist. This all happens in a fraction of a second after which it presents you with a shortlist of videos for you to watch. The fact that you chose the second pick instead of the first is of course something that will get fed back into the system to make an even better recommendation next time.
• Using micro formats for CVs
  The way that a simple structured data format has been used to capture all CVs in the central HR management system in combination with the API that was put on top of it has allowed a wealth of applications for this structured data.

There are three more titles that I wanted to do, but did not have the chance to do yet.

• Using external information inside the company
• Suggested learning groups to self-organize
• Linking performance data to learning excellence

Book: Head First Data Analytics

I have always been intrigued by O’Reilly’s Head First series of books. I don’t know any other publisher who is that explicit about how their books try to implement research based good practices like an informal style, repetition and the use of visuals. So when I encountered Data Analysis in the series I decided to give it a go. I wrote the following review on Goodreads:

The “Head First” series has a refreshing ambition: to create books that help people learn. They try to do this by following a set of evidence-based learning principles. Things like repetition, visual information and practice are all incorporated into the book. This good introduction to data analysis, in the end only scratches the surface and was a bit too simplistic for my taste. I liked the refreshers around hypothesis testing, solver optimisation in Excel, simple linear regression, cleaning up data and visualisation. The best thing about the book is how it introduced me to the open source multi-platform statistical package “R”.

Learning impact measurement and Knowledge Advisers

The day before Learning Technologies, Bersin and KnowledgeAdvisors organized a seminar about measuring the impact of learning. David Mallon, analyst at Bersin, presented their High-Impact Measurement framework.

The thing that I thought was interesting was how the maturity of your measurement strategy is basically a function of how much your learning organization has moved towards performance consulting. How can you measure business impact if your planning and gap analysis isn’t close to the business?

Jeffrey Berk from KnowledgeAdvisors then tried to show how their Metrics that Matter product allows measurement and then dashboarding around all the parts of the Bersin framework. They basically do this by asking participants to fill in surveys after they have attended any kind of learning event. Their name for these surveys is “smart sheets” (an much improved iteration of the familiar “happy sheets”). KnowledgeAdvisors has a complete software as a service based infrastructure for sending out these digital surveys and collating the results. Because they have all this data they can benchmark your scores against yourself or against their other customers (in aggregate of course). They have done all the sensible statistics for you, so you don’t have to filter out the bias on self-reporting or think about cultural differences in the way people respond to these surveys. Another thing you can do is pull in real business data (think things like sales volumes). By doing some fancy regression analysis it is then possible to see what part of the improvement can be attributed with some level of confidence to the learning intervention, allowing you to calculate return on investment (ROI) for the learning programs.

All in all I was quite impressed with the toolset that they can provide and I do think they will probably serve a genuine need for many businesses.

The best question of the day came from Charles Jennings who pointed out to David Mallon that his talk had referred to the increasing importance of learning on the job and informal learning, but that the learning measurement framework only addresses measurement strategies for top-down and formal learning. Why was that the case? Unfortunately I cannot remember Mallon’s answer (which probably does say something about the quality or relevance of it!)

Experimenting with Needlebase, R, Google charts, Gephi and ManyEyes

The first tool that I tried out this week was Needlebase. This tool allows you to create a data model by defining the nodes in the model and their relations. Then you can train it on a web page of your choice to teach it how to scrape the information from the page. Once you have done that Needlebase will go out to collect all the information and will display it in a way that allows you to sort and graph the information. Watch this video to get a better idea of how this works:

I decided to see if I could use Needlebase to get some insights into resources on Delicious that are tagged with the “lak11″ tag. Once you understands how it works, it only takes about 10 minutes to create the model and start scraping the page.

I wanted to get answers to the following questions:

• Which five users have added the most links and what is the distribution of links over users?
• Which twenty links were added the most with a “lak11″ tag?
• Which twenty links with a “lak11″ tag are the most popular on Delicious?
• Can the tags be put into a tag cloud based on the frequency of their use?
• In which week were the Delicious users the most active when it came to bookmarking “lak11″ resources?
• Imagine that the answers to the questions above would be all somebody were able to see about this Knowledge and Learning Analytics course. Would they get a relatively balanced idea about the key topics, resources and people related to the course? What are some of the key things that would they would miss?

Unfortunately after I had done all the machine learning (and had written the above) I learned that Delicious explicitly blocks Needlebase from accessing the site. I therefore had to switch plans.

The Twapperkeeper service keeps a copy of all the tweets with a particular tag (Twitter itself only gives access to the last two weeks of messages through its search interface). I manage to train Needlebase to scrape all the tweets, the username, URL to user picture and userid of the person adding the tweet, who the tweet was a reply to, the unique ID of the tweet, the longitude and latitude, the client that was used and the date of the tweet.

I had to change my questions too:

• Which ten users have added the most tweets and what is the distribution of tweets over users?
  This was easy to get and graph with Needlebase itself: 
  

  Top 11 Lak11 Twitter Users

  I personally like treemaps for this kind of data, so I tried to create one in IBM’s ManyEyes. Unfortunately they seem to have some persistent issues with their site:

  

  ManyEyes error message

• Which twenty links were added the most with a “lak11″ tag? Another way of asking this would be: which twenty links created the most buzz?
  This was a bit harder because Needlebase did not get the links for me. I had to download all the text into a text file and use some regular expressions to get a list of all the URLs in the tweets. 796 of the 967 tweets had a URL (that is more than 80%), 453 of these were unique. I could then do some manipulations in a spreadsheet (sorting, adding and some appending) to come up with a list. Most of these URLs are shortened, so I had to check them online to get their titles. This is the result: 
  • Elluminate | Session Log-in (23 mentions)
  • Learning Analytics Syllabus – Google Docs (17 mentions)
  • The # LAK11 Daily (15 mentions)
  • Elluminate | Session Log-in (14 mentions)
  • Where do we find good critiques of learning analytics? | Learning and Knowledge Analytics (10 mentions)
  • MOOC newbie voice “a slackers entrance into lak11″» Dave’s Educational Blog (10 mentions)
  • Half an Hour: Why the Semantic Web Will Fail (9 mentions)
  • Artifacts of sensemaking | Learning and Knowledge Analytics (9 mentions)
  • Help Me Understand The Buzz Around Learning Analytics | FunnyMonkey (9 mentions)
  • Social Networks in Action – Learning Networks @ UOW (8 mentions)
  • Reflections on Open Courses: Curation, Ombuds, and Concierges | Learning and Knowledge Analytics (7 mentions)
  • Course: Learning and Knowledge Analytics (7 mentions)
  • YouTube – What is Hadoop? Other big data terms like MapReduce? Cloudera’s CEO talks us through big data trends (6 mentions)
  • @Ignatia Webs: #LAK11 a free and open #elearning course on #statistics starts today, join!Ignatia Webs (6 mentions)
  • Learning & Knowledge Analytics 2011 (6 mentions)
  • for the love of learning: Measurable Outcomes (6 mentions)
  • Free project in the clouds for teachers around the world http://web20ineducation2010.ning.com/: applications, e-safety, education, education , social media, tools, web 2.0 | Glogster EDU – 21st century multimedia tool for educators, teachers and students (6 mentions)
  • Study Group: Learning Analytics | OpenStudy (6 mentions)
  • Course: Learning and Knowledge Analytics (6 mentions)
  • http://paper.li/tag/LAK11 (5 mentions)

One problem I noticed is that two of the twenty results were the same URL with a different shortened URLs (the link to the Moodle course and to the Paper.li paper): URL shorteners make the web the more difficult place in many ways.

• What other hashtags are used next to Lak11?
  Here I used a similar methodology as for the URLs. In the end I had a list of all the tags with their frequencies. I used Wordle and ManyEyes to put them into tag clouds: 
  

  Wordle Lak11 Hashtags

  

  ManyEyes Lak11 Hashtags

  Also compare them to tag clouds of the complete texts of the tweets (cleaned up to remove usernames, “RT”, “Lak11″ URLs and the # in front of the hash tags):

  

  Wordle Lak11 Tweets Texts

  

  ManyEyes Lak11 Tweets Texts

  Which one do you find more insightful? I personally prefer the latter one as it would give somebody who knows nothing about Lak11 a good flavor of the course.

• How are the Tweets distributed over time? Is the traffic increasing with time or decreasing?
  I decided to just get a simple list of days with the number of tweets per day. As an exercise I wanted to graph it in R. These are the results: 
  

  Tweets per day

  I couldn’t learn anything interesting from that one.

• Imagine that the answers to the questions above would be all somebody were able to see about this Knowledge and Learning Analytics course. Would they get a relatively balanced idea about the key topics, resources and people related to the course? What are some of the key things that would they would miss? If you would automate getting answers to all these question (no more manual writing of regex!) would that be useful for learners and facilitators?
  I have to say that I was pleasantly surprised by how fruitful the little exercise with getting the top 20 links was. I really do believe that these links capture much of the best materials of the first couple of weeks of the course. If you would use the Wordle as the single image to give a flavour of the course and then point to the 20 URLs and get the names of the top Twitterers, than you would be off to badly.

Another great resource that I re-encountered in these weeks of the course was the Rosling’s Gapminder project:

Google has acquired some part of that technology and thus allows a similar kind of visualization with their spreadsheet data. What makes the data smart is the way that it shows three variables (x-axis, y-axis and size of the bubble and how they change over time. I thought hard about how I could use the Twitter data in this way, but couldn’t find anything sensible. I still wanted to play with the visualization. So at the World Bank’s Open Data Initiative I could download data about population size, investment in education and unemployment figures for a set of countries per year (they have a nice iPhone app too). When I loaded that data I got the following result:

The last tool I installed and took a look at was Gephi. I first used SNAPP on the forums of week and exported that data into an XML based format. I then loaded that in Gephi and could play around a bit:

My participation in numbers

I will have to add up my participation for the two (to three) weeks, so in week 3 and week 4 of the course I did 6 Moodle posts, tweeted 3 times about Lak11, wrote 1 blogpost and saved 49 bookmarks to Diigo.

The hours that I have played with all the different tools mentioned above are not mentioned in my self-measurement. However, I did really enjoy playing with these tools and learned a lot of new things.