Today I keynoted the Dutch Moodlemoot (mootnl12). I talked about how current times force us to let go of curricula, why it is more important than anything else to teach students how to learn, what it means to work in a knowledge society (work becomes synonymous with learning) and what this might mean for a virtual learning environment like Moodle. Unfortunately this talk was in Dutch and so will be the accompanying blogpost.
De Wikipedia pagina over het Cynefin framework legt goed uit wat het is. Harold Jarche heeft een ijzersterke blogpost geschreven waarin hij dat framework toepast op leren en daar vergaande conclusies voor organisaties uit trekt. Lees ook zijn drie principes voor “net work”.
Scott Jenson had jaren zijn eigen design consultancy and werkt nu als Lead UI Designer for Mobile bij Google. Hij weet dus waar hij het over heeft. Zijn boek The Simplicity Shift staat integraal als PDF online.
Drupal kent al een tijdje het concept van distributions. Moodle heeft misschien met de Flavours plugin al een beetje hetzelfde in huis.
Two weeks ago I visited Learning Technologies 2011 in London (blog post forthcoming). This meant I had less time to write down some thoughts on Lak11. I did manage to read most of the reading materials from the syllabus and did some experimenting with the different tools that are out there. Here are my reflections on week 3 and 4 (and a little bit of 5) of the course.
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.
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:
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:
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:
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:
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):
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:
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.
I have been involved in organizing a workshop on capability building in organizations hosted on my employer‘s premises (to be held on October 20th). We have tried to get together an interesting group of professionals who will think about the future state of capability building and how to get there. All participants have done a little bit of pre-work by using a single page to answer the following question:
What/who inspires you in your vision/ideas for the future state of capability building in organizations?
We don’t understand ourselves well enough. If we did, the world would not be populated with bad design (and everything might look like Disney World). The principles that we use for designing our learning interventions are not derived from a deep understanding of the humand mind and its behavioural tendencies, instead it is often based on simplistic and unscientific methodologies. How can we change this? First, everybody should read Christopher Alexander’s A Pattern Language. Next, we can look at Hans Monderman (accessible through the book Traffic) to understand the influence of our surroundings on our behaviour. Then we have to try and understand ourselves better by reading Medina’s Brain Rules (or check out the excellent site) and books on evolutionary psychology (maybe start with Pinker’s How the Mind Works). Finally we must never underestimate what we are capable of. Mitra’s Hole in the Wall experiment is a great reminder of this fact.
The mental model that 99% of the people in this world have for how people learn is still informed by an implied behaviourist learning theory. I like contrasting this with George Siemens’ connectivism and Papert’s constructionism (I love this definition). These theories are actually put into practice (the proof of the pudding is in the eating): Siemens and Stephen Downes (prime sense-maker and a must-read in the educational technology world) have been running multiple massive online distributed courses with fascinating results, whereas Papert’s thinking has inspired the work on Sugarlabs (a spinoff of the One Laptop per Child project).
Working smarter Jay Cross knows how to adapt his personal business models on the basis of what technology can deliver. I love his concept of the unbook and think the way that the Internet Time Alliance is set up should enable him to have a sustainable portfolio lifestyle (see The Age of Unreason by the visionary Charles Handy). The people in the Internet Time Alliance keep amplifying each other and keep on tightening their thinking on Informal Learning, now mainly through their work on The Working Smarter Fieldbook.
Games for learning
We are starting to use games to change our lives. “Game mechanics” are showing up in Silicon Valley startups and will enter mainstream soon too. World Without Oil made me understand that playing a game can truly be a transformational experience and Metal Gear Solid showed me that you can be more engaged with a game than with any other medium. If you are interested to know more I would start by reading Jesse Schell’s wonderful The Art of Game Design, I would keep following Nintendo to be amazed by their creative take on the world and I would follow the work that Jane McConigal is doing.
Another appstore! Rafael Sidi from Elsevier kicked of the second day with a talk titled “Bring in ‘da Developers, Bring in ‘da Apps – Developing Search and Discovery Solutions Using Scientific Content APIs” (the slightly ludicrous title was fashioned after this).
He opened his talk with this Steve Ballmer video which, if I was the CIO of any company, would seriously make me reconsider my customer relationship with Microsoft:
(If you enjoyed that video, make sure you watch this one too, first watch it with the sound turned off and only then with the sound on).
Sidi is responsible for Elservier’s SciVerse platform. He has seen that data platforms are increasingly important, that there is an explosion of applications and that people work in communities of innovation. He used Data.gov as an example: it went from 47 sources to 220,000+ sources within a year’s time and has led to initiatives like Apps for America. We need to have an “Apps for science” too. Our current scientific platforms make us spend too much time gathering instead of analysing information and none of them really understand the user’s intent.
The key trends that he sees on the web are:
Openness and interoperability (“give me your data, my way”). Access to APIs helps to create an ecosystem.
Personalization (“know what I want and deliver results on my interest”). Well known examples are: Amazon, Netflix and Last.fm
Collaboration & trusted views (“the right contacts at the right time”). Filtering content through people you trust. “Show me the articles I’ve read and show me what my friends have right differently from me”. This is not done a lot. Sidi didn’t mention this but I think things like Facebook’s open API are starting to deliver this.
So Elsevier has decided to turn SciVerse, the portal to their content, into a platform by creating an API with which developers can create applications. Very similar to Apple’s appstore this will include a revenue sharing model. They will also nurture a developers community (bootstrapping it with a couple of challenges).
He then demonstrated how applications would be able to augment SciVerse search results, either by doing smart things with the data in a sidebar (based on aggregated information about the search result) or by modifying a single search result itself. I thought it looked quite impressive and thought it was a very smart move: scientific publishers seem to be under a lot of pressure from things like Open Access and have been struggling to demonstrate their added value in this Internet world. This could be one way to add value. The reaction from the audience was quite tough (something Sidi already preempted by showing an “I hate Elsevier”-tweet in his slides). One audience member: “Elsevier already knows how to exploit the labour of scientists and now wants to exploit the labour of developers too”. I am no big fan of large publisher houses, but thought this was a bit harsh.
Knowledge Visualization Wolfgang Kienreich demoed some of the knowledge visualization products that the Know-Center has developed over the years. The 3D knowledge space is not available through the web (it is licensed to a German encyclopedia publisher), but showed what is possible if you think hard about how a user should be able to navigate through large knowledge collections. Their work for the Austrian Press Agency is available online in a “labs” evironment. It demonstrates a way of using faceted search in combination simple but insightful visualizations. The following example is a screenshot showing which Austrian politicians have said something about pensions.
I have only learned through writing this blog post that Wolfgang is interested in the Prisoner’s Dilemma. I would have loved to have talked to him about Goffman’s Expression games and what they could mean for the ways decisions get made in large corporations. I will keep that for a next meeting.
This track was supposed to have four talks, but one speaker did not make it to the conference, so there were three talks left.
The first one was provocatively titled “Does knowledge worker productivity really matter?” by Rainer Erne. It was Drucker who said that is used to be the job of management to increase the productivity of manual labour and that is now the job of management to make knowledge workers more productive. In one sense Drucker was definitely right: the demand for knowledge work is increasing all the time, whereas the demand for routine activities are always going down.
Erne’s study focuses on one particular part of knowledge workers: expert work which is judgement oriented, highly reliant on individual expertise and experience and dependent on star performance. He looked at five business segments (hardware development, software development, consulting, medical work and university work) and consistently found the same five key performance indicators:
quality of interaction
organisation of work
quality of results
This leads Erne to belief that we need to redefine productivity for knowledge workers. There shouldn’t just be a focus on quantity of the output, but more on the quality of the output. So what can managers do knowing this? They can help their experts by being a filter, or by concentrating their work for them.
This talk left me with some questions. I am not sure whether it is possible to make this distinction between quantitative and qualitative output, especially not in commercial settings. The talk also did not address what I consider to be the main challenge for management in this information age: the fact that a very good manual worker can only be 2 or maybe 3 times as productive as an average manual worker, whereas a good knowledge worker can be hundreds if not thousands times more productive than the average worker.
Robert Woitsch talk was titled “Industrialisation of Knowledge Work, Business and Knowledge Alignment” and I have to admit that I found it very hard to contextualize what he was saying into something that had any meaning to me. I did think it was interesting that he really went in another direction compared to Erne as Woitsch does consider knowledge work to be a production process: people have to do things in efficient ways. I guess it is important to better define what it is we actually mean when we talk about knowledge work. His sites are here: http://promote.boc-eu.com and http://www.openmodels.at.
Finally Olaf Grebner from SAP research talked about “Optimization of Knowledge Work in the Public Sector by Means of Digital Metaphors”. SAP has a case management system that is used by organisations as a replacement for their paper based system. The main difference between current iterations of digital systems and traditional paper based systems is that the latter allows links between the formal case and the informal aspects around the case (e.g. a post-it note on a case-file). Digital case management systems don’t allow informal information to be stored.
So Grebner set out to design an add-on to the digital system that would link informal with formal information and would do this by using digital metaphors. He implemented digital post-it notes, cabinets and ways of search and his initial results are quite positive.
Personally I am bit sceptical about this approach. Digital metaphors have served us well in the past, but are also the cause for the fact that I have to store my files in folders and that each file can only be stored in one folder. Don’t you lose the ability to truly re-invent what a digital case-management system can do for a company if you focus on translating the paper world into digital form? People didn’t like the new digital system (that is why Grebner was commissioned to do make his prototype I imagine). I believe that is because it didn’t allow the same affordances as the paper based world. Why not focus on that first?
Knowledge Management and Learning
This track had three learning related sessions.
Martin Wolpers from the Fraunhofer Institute for Applied Information Technology (FIT) talked about the “Early Experiences with Responsive Open Learning Environments”. He first defined each of the terms in Responsive Open Learning Environments:
Responsive: responsiveness to learners’ activities in respect to learning goals
Open: openness for new configurations, new contents and new users
Learning Environment: the conglomerate of tools that bring together people and content artifacts in learning activities to support them in constructing and processing information and knowledge.
The current generation of Virtual Learning Environments and Learning Management Systems have a couple of problems:
Lack of information about the user across learning systems and learning contexts (i.e. what happens to the learning history of a person when they switch to a different company?)
Learners cannot choose their own learning services
Lack of support for open and flexible personalized contextualized learning approach
Fraunhofer is making an intelligent infrastructure that incorporates widgets and existing VLE/LMS functionality to truly personalize learning. They want to bridge what people use at home with what they use in the corporate environment by “intelligent user driven aggregation”. This includes a technology infrastructure, but also requires a big change in understanding how people actually learn.
They used Shindig as the widget engine and Opensocial as the widget technology. They used this to create an environment with the following characteristics:
A widget based environment to enable students to create their own learning environment
Development of new widgets should be independent from specific learning platforms
Real-time communication between learners, remote inter-widget communication, interoperable data exchange, event broadcasting, etc.
He used a student population in China as the first people to try the system. It didn’t have the uptake that he expected. They soon realised that this was because the students had come to the conclusion that use or non-use of the system did not directly affect their grades. The students also lacked an understanding of the (Western?) concept of a Personal Learning Environment. After this first trial he came to a couple of conclusions. Some where obvious like that you should respect the cultural background of your students or that responsive open learning environments create challenges on the technology and the psycho-pedagogical side. Other were less obvious like that using an organic development process allowed for flexibility and for openly addressing emerging needs and requirements and that it makes sense to enforce your own development to become the standard.
For me this talk highlighted the still significant gap that seems to exist between computer scientists on the one side and social scientists on the other side. Trying out Personal Learning Environments in China is like sending CliniClowns to Africa: not a good idea. Somebody could have told them this in advance, right?
Next up was a talk titled “Utilizing Semantic Web Tools and Technologies for Competency Management” by Valentina Janev from the Serbian Mihajlo Pupin Institute. She does research to help improve the transferability and comparability of competences, skills and qualifications and to make it easier to express core competencies and talents in a standardized machine accessible way. This was another talk that was hard for me to follow because it was completely focused on what needs to happen on the (semantic) technical side without first giving a clear idea of what kind of processes these technological solutions will eventually improve. A couple of snippets that I picked up are that they are replacing data warehouse technologies with semantic web technologies, that they use OntoWiki a semantic wiki application, that RDF is the key word for people in this field and that there is thing called DOAC which has the ambition to make job profiles (and the matching CVs) machine readable.
The final talk in this track was from Joachim Griesbaum who works at the Institute of Information Science and Language Technology. The title of his talk must have been the longest in the conference: “Facilitating collaborative knowledge management and self-directed learning in higher education with the help of social software, Concept and implementation of CollabUni – a social information and communication infrastructure”, but as he said: at least it gives you an idea what it is about (slides of this talk are available here, Griesbaum was one of the few presenters that made it clear where I could find the slides afterwards).
A lot of social software in higher education is used in formal learning. Griesbaum wants to focus on a Knowledge Management approach that primarily supports informal learning. To that end he and his students designed a low cost (there was no budget) system from the bottom up. It is called CollabUni and based on the open source e-portfolio solution (and smart little sister of Moodle) Mahara.
They did a first evaluation of the system in late 2009. There was little self-initiated knowledge activity by the 79 first year students. Roughly one-third of the students see an added value in CollabUni and declare themselves ready for active participation. Even though the knowledge processes that they aimed for don’t seem to be self-initiating and self-supporting, CollabUni still shows and stands for a possible low-cost and bottom-up approach towards developing social software. During the next steps of their roll out they will pay attention to the following:
Social design is decisively important
Administrative and organizational support components and incentive schemes are needed
Appealing content (for example an initial repository of term papers or theses)
Identify attractive use cases and applications
Call me a cynic, but if you have to try this hard: why bother? To me this really had the feeling of a technology trying to find a problem, rather than a technology being the solution to the problem. I wonder what the uptake of Facebook is with his students? I did ask him the question and he said that there has not been a lot of research into the use of Facebook in education. I guess that is true, but I am quite convinced there is a lot use of Facebook in education. I believe that if he had really wanted to leverage social software for the informal part of learning, he should have started with what his students are actually using and try to leverage that by designing technology in that context, instead of using another separate system.
Collaborative Innovation Networks (COINs)
The closing keynote of the conference was by Peter A. Gloor who currently works for the MIT Center for Collective Intelligence. Gloor has written a couple of books on how innovation happens in this networked world. Though his story was certainly entertaining I also found it a bit messy: he had an endless list of fascinating examples that in the end supported a message that he could have given in a single slide.
His main point is that large groups of people behave apparently randomly, but that there are patterns that can be analysed at the collective level. These patterns can give you insight into the direction people are moving. One way of reading the collective mind is by doing social network analysis. By combining the wisdom of the crowd with the wisdom of groups of experts (swarms) it is possible to do accurate predictions. One example he gave was how they had used reviews on the Internet Movie Database (the crowd) and on Rotten Tomatoes (the swarm) to predict on the day before a movie opens in the theatres how much the movie will bring in in total.
The process to do these kinds of predictions is as follows:
This kind of analysis can be done at a global level (like the movie example), but also in for example organizations by analysing email-archives or equipping people with so called social badges (which I first read about in Honest Signals) which measure who people have contact with and what kind of interaction they are having.
He then went on to talk about what he calls “Collaborative Innovation Networks” (COINs) which you can find around most innovative ideas. People who lead innovation (think Thomas Edison or Tim Berners-Lee) have the following characteristics:
There are well connected (they have many “friends”)
They have a high degree of interactivity (very responsive)
They share to a very high degree
All of these characteristics are easy to measure electronically and thus automatically, so to find COINs you find the people who score high on these points. According to Gloor high-performing organizations work as collaborative innovation networks. Ideas progress from Collaborative Innovation Network (COIN) to Collaborative Learning Network (CLN) to Collaborative Interest Network (CIN).
Twitter is proving to be a very useful tool for this kind of analysis. Doing predictions for movies is relatively easy because people are honest in their feedback. It is much harder for things like stock, because people game the system with their analysis. Twitter can be used (e.g. by searching for “hope”, “fear” and “worry” as indicators for sentiment) as people are honest in their feedback there.
Finally he made a refence in his talk to the Allen curve (the high correlation between physical distance and communication, with a critical distance of 50 meters for technical communication). I am sure this curve is used by many office planners, but Gloor also found an Allen curve for technical companies around his university: it was about 3 miles.
Outside of the sessions I spoke to many interesting people at the conference. Here are a couple (for my own future reference).
It had been a couple of years since I had last seen Peter Sereinigg from act2win. He has stopped being a Moodle partner and now focuses on projects in which he helps global virtual teams in how they communicate with each other. There was one thing that he and I could fully agree on: you first have to build some rapport before you can effectively work together. It seems like such an obvious thing, but for some reason it still doesn’t happen on many occasions.
He is still actively publishing in peer reviewed journals and speaking at conferences, without being affiliated with a highly acclaimed research institute. He has written an interesting blog post about the pros and cons of working this way.
I had never heard of this young field of community informatics and it is something I would like to explore further.
I also spent some time with Barend Jan de Jong who works at Wolters Noordhoff. We had some broad-ranging discussions mainly about the publishing field: the book production process and the information technology required to support this, what value a publisher can still add, e-books compared to normal books (he said how a bookcase says something about somebody’s identity, I agreed but said that a digital book related profile is way more accessible than the bookcase in my living room, note to self: start creating parody GoodReads accounts for Dutch politicians), the unclear if not unsustainable business model of the wonderful Guardian news empire and how we both think that O’Reilly is a publisher that seem to have their stuff fully in order.
There were also some things at I-KNOW 2010 that were really from a different world. The keynote on the morning of the 3rd day was perplexing to me. Márta Nagy-Rothengass titled the talk “European ICT Research and Development Supporting the Expansion of Semantic Technologies and Shared Knowledge Management” and opened with a video message of Neelie Kroes talking in very general terms about Europe’s digital agenda. After that Nagy-Rothengass told us that the European Commission will be nearly doubling its investment into ICT to 11 billion Euros, after which she started talking about the “Call 5” of “FP7” (apparently that stands for the Seventh Framework Programme), the dates before which people should put their proposals in, the number of proposals received, etc., etc., etc. I am pro-EU, but I am starting to understand why people can make a living advising other people how best to apply for EU grants.
Another puzzling thing was the fact that people like me (with a corporate background) thought that the conference was quite theoretical and academic, whereas the researchers thought everything was very applied (maybe not enough research even!). I guess this shows that there is quite a schism between universities furthering the knowledge in this field and corporations who could benefit from picking the fruits of this knowledge. I hope my attendance at this great conference did its tiny part in bridging this gap.
Arjen Vrielink and I write a monthly series titled: Parallax. We both agree on a title for the post and on some other arbitrary restrictions to induce our creative process. This time we decided to write about what makes Goodreads a great website. First we sat together for an hour and used Gobby to collaboratively write a rough draft of the text. Each of us then edited the draft and published the post separately. You can read Arjen’s post with the same title here.
What is Goodreads? Goodreads is Facebook and Wikipedia for readers: a social network of people that love to read books, full of features that readers might like. It allows you to keep many “shelves” with books that can be shared with other people on the site.
Here are some of the features (in no particular order) that make Goodreads work so well:
The site is not only useful when you are a member. Even if you are not logged in it still is a pleasant site to read and browse for book lovers.
It allows you to keep track of your own, yout friends and “the crowds” books. If you see an interesting book you can put it on your to-read shelf, if a friend reads an interesting book than he or she can recommend it to you.
Statistics can suggest recommendations based on my shelves, reviews and friends.
There is a distinction between friends (a symmetric relationship) and followers (an assymetric relationship).
There is a book comparison feature: it finds the books you have both read and compares the scores you have given to those books.
It is very easy to invite your friends into the site. You can put in their email address, or you can give Goodreads access to your webmail contacts (sometimes this is a questionable thing, but Goodreads isn’t to pushy (it doesn’t send out Tweets without you knowing it for example)).
They have a great “universal” search box where you can search books on author, title or isbn from the same field.
It makes use of Ajax in the right locations, allowing you to update small things (“liking” a review, noting what page you’ve reached, handing out stars to a book) without having to reload the page.
The user profile page is related to the contents of the webservice: for example, it allows you to say who your favourite authors are.
The site supports many different ways of viewing and sorting your shelves. You can look at covers or at titles and sort by author, by score, by last update and more.
Before building a great iPhone app, Goodreads made sure their website had a great mobile version of their website. When you access the website with a mobile browser it automatically redirects to a mobile version of the website, so even if you are accessing the site with your Windows Mobile device you have a great experience.
Not only is it very easy to put data into the Goodreads ecosystem, it is also very easy to get your data out again. You can download a CSV file with all your books (including the data you added like reviews, date read, your rating and the metadata about the book that Goodreads has added like the ISBN or the average rating). The smart import feature looks at an HTML page (e.g. an Amazon wishlist page) and imports all the ISBNs it can find in the source code of the page. Like any good webservice it imports files that are exported from their competition (Shelfari, Librarything and Delicious library).
There seems to be an evolving business model. Initially there were only (onubtrusive) adds, but now they are starting to sell e-books, integrating this into the social network.
Often when you read a book there are sentences or passages which really impress or inspire. Most of the times you then forgot these. Goodreads allows you to favourite and rank (and thus collect) quotes easily by author or by book. You can add and export quotes as well.
Sharing your Goodreads activity to other important webservices is built in. There are integrations with Facebook, Twitter, WordPress Blogs and MySpace. Goodreads also provides embeddable widgets that you can put on another website (e.g. a box with the most recent books you have read). A simple integration allows you to instantly find a book that you are looking at in Goodreads in your favourite online bookstore. And of course there is the ubiquitous RSS.
A site like Goodreads get is value from the data that its users put in. Goodreads allows this at many levels. There are trivial ways of adding information (i.e. saying you like a review by clicking a single link, allowing Goodreads to display useful reviews first), but there are also ways of adding information that take slightly more effort. For example, it is fairly easy to get “librarian” status which shows the site trusts their users. As a librarian you can edit existing book entries. A low entrance level is key to crowd sourcing. Another way to involve people is to allow them to add their own trivia that other users can try and answer in trivia games.
It allows users to flag objectionable content.
Goodreads has its own blog, keeping you up to date about the latest features and their direction.
It has an element of competition, you can see how many books are on your shelf and how many books are on other people’s shelf, but there are more metrics: you can see who has written the most popular reviews, your rank among this week’s reviewers, or who has the most followers
It has a great and open API. This allows other people to build services on top of Goodreads. The potential for this is huge (the very first Goodreads iPhone app was not made by Goodreads itself, but was made by a Goodreads enthousiast) and I don’t think we have seen what will be possible with this yet. A lot of the data that Goodreads collects is accesible through the API in a structured and aggregated form. It should be very easy for other book related sites to incorporate average ratings from Goodreads on their own pages for example.
It is in continual beta and their design process seems to be iterative: it keeps evolving and adding new features at a high frequency like the recently added stats feature.
It is easy to delete your account, deleting all your data in the process. This makes for complete transparancy about data ownership, an issue that other sites (Facebook!) have been struggling with lately.
It has a kind of update stream which let’s you easily keep up to date with your friends, groups and favourite authors status.
The service has ambitious and lofty goals: “Goodreads’ mission is to get people excited about reading. Along the way, we plan to improve the process of reading and learning throughout the world.” (see here). I do believe that this clear mission has led to many features that wouldn’t have been there otherwise. For example, there is a book swap economy built into the site allowing people to say that they own the book and are willing to swap it for other books. Another book lovers feature are the lists. Anybody can start a list and people can then vote to get books on the list. Examples of list are The Movie was better than the Book or Science books you loved. Another feature are the book events. You can find author appearance, book club meetings, book swaps and other events based on how many miles away you want these to be from a certain city or in a certain country. Of course you can add events yourself, next to the ones that Goodreads imports from other sites, and you can say which events you will attend, plus invite friends to these events.
How Goodreads could improve
As said, Goodreads is continuously changing, there are still some things that require some change in the right direction:
Ocassionally the site feels a bit buggy. I have had a lot of grief updating the shelves of books using the mobile site with it not doing the things I wanted it do.
It is not always clear what kind of updates are triggered by an user action. I am not sure what my friends see. Sometimes you find your Facebook Wall flooded with Goodreads updates because your friend found a box of long lost books in the attic which he entered in an update frenzy.
Usability: Some features are hard to find. Like the new stats feature discussed above, you can only find it hidden away on the bottom left of a page in some obscure menu. Other features are hard to use, requiring many more clicks than are actually necessary.
They could improve on localisation and on the translations of books. In your profile settings you can select your country, but you cannot select in which languages you are able to read books.
The graphic design of the site isn’t top notch. When people initially see Shelfari, it might have more appeal just because it looks a tad better.
In-app mailing or messaging systems are always beyond me. Goodreads also has an “inbox” where you can send mail to and receive mail from your Goodreads friends. I would much rather use my regular mail and use Goodreads as a broker so email addresses can be private.
Some thoughts on the process of writing this post Gobby is a multi-platform text editor that allows multiple people to work on the same text file in realtime. It uses colours to denote who has written what.
This was an experiment to see how it would feel to work like this and whether it would be an efficient and effective way of working together. I thought it was quite successful as we produced a lot of material and helped eachother think: building on the point of the other person. It was helpful to do an initial draft, but it does require some significant editing afterwards. I thought it was interesting to see that you feel no compunction to change the other person’s spelling mistake, but that you feel less free to change the contents of what they are writing.
This time we were sitting opposite each other while writing. In the future it would be interesting (firewalls permitting) to try and do this over a longer distance. Then the unused chat-window might become more useful and important.
You can download the original Gobby file here (it requires Gobby to make sense).
Hopefully this post about Goodreads is an inspiration to anybody who tries to build a social network around a certain theme and remember: if I know you I would love nothing more than to be your Goodreads “friend”.