Friday, May 30, 2008

Visualising networks of learning resources

I'm looking at the first dataset of bookmarks from MELT portal. Here you can see some of the first descriptions created by using Many Eyes. Click on the interact button in the pic and it loads. This is a treemap visualisation of the bookmarks that users so far have found.

What do you see here? You first see boxes in different colours. They are "boxed" by the user IDs. The bigger one is, the more learning resources this person has bookmarked. If you hoover your mouse over the boxes, you can see the ID of resources. These, of course, do not mean nothing to you now, but imagine if they were links to resources?

Next you can explore the data a bit further. Drag the mother tongue box on the top of the graph to the first place. Now, the boxes are displayed by the languages spoken by users. You'll see that Hungarian speakers have been busy on the portal, they have the most bookmarks.

Third, you can explore further by dragging the obj_lang to the first place. This shows the languages in which the bookmarked resources are. Interestingly, it turns out, most of these resources are in English. However, the diversity is there to be observed: users have found resources in many different languages useful.

Let's go further. The next one is a network diagram. If you click on "click to interact" you can also zoom into the visualisation.

What do you see here? It's a network that consist of: user mother tongue and the learning resource that those users bookmarked on the portal. You see 4 quite big vertices, which are the mother tongues of the users. consists of a set of objects called vertices connected by edges. The visualization of the network is optimized to keep strongly related items in close proximity to each other. In this way, the overall arrangement of vertices in the network is very telling of the structure of the connections between vertices (vertices that are far away are weakly related to each other).In this visualization, the size of a vertex is proportional to the number of edges emanating from it.
Take the Hungarian speakers, for example. They are the ones who user the portal most, and have actually bookmarked a fair amount of resource. At the end of each edge you can see an ID number. Those are the ID of learning resources that these teachers have bookmarked. The same goes for Finnish speakers, Dutch speakers, etc.

Interestingly, we can see from this visualisation that not many resources are shared among the users from different language groups. A few are, though: take, for example, the LeMill resource that is visualised in orange in the image here. It has edges linking it to Finnish, German and Hungarian speakers. I counted 14 resources in this small dataset that were shared by users from different countries, that's about 13% of resources.

This type of resources are what we call "travel well" resources, as they can cross borders. In this case those borders are lingual. The resource also acts as a bridge between these different language communities. If you look at the resource in question, you'll find that it is to teach English (as
foreign language) and it is in English. Thus, it is not that surprising that it is well accepted in many language communities.

Finally, I also visualised the languages of learning resources instead of the resource ID. You can find it here. As you see from the image on the right, I have highlighted the languages of resources from Dutch speaking users. They have been pretty busy finding resources in all kinds of languages!

Tuesday, May 20, 2008


Good news, we are ready to roll out the call for contributions for our 2nd workshop! This time we are planning more time for discussions and brainstroming type of exercises that participants can lead! This was the feedback from last year, so you see that we are taking it seriously :)

Check out the format for contributions; Research papers and System Demos are the more conventional stuff that we welcome, whereas Hands-On proposals are there to let us all loose and to think how could we use ideas from some exiting, existing systems to enhance and support learning and teaching. Oh then, there are of course the Pecha Kucha talks. That makes me really curious: someone said that they would not really work with computer science. I hope we are able to prove that wrong ;)


in the 3rd European Conference on Technology Enhanced Learning (EC-TEL08), Maastricht, The Netherlands


  • Contribution Submission: June 29, 2008
  • Results Notification: August 3, 2008
  • Camera Ready Submission: August 31, 2008
  • Workshop date: September 17, 2008
  • Main conference dates: September 18-19, 2008


After the successful first SIRTEL workshop last year, we are delighted to welcome
exciting new contributions for the 2nd Social Information Retrieval for Technology Enhanced Learning (SIRTEL) workshop:

  • Research papers
  • System Demos
  • Hands-On proposals
  • "Pecha Kucha" talks*


Learning and teaching resources are available on the Web - both in terms of digital learning
content and people resources (e.g. other learners, experts, tutors). They can be used to
facilitate teaching and learning tasks. Developing, deploying and
evaluating Social information retrieval (SIR) methods, techniques and systems that provide
learners and teachers with guidance in potentially overwhelming variety of choices remains to be tackled.

The aim of the SIRTEL’08 workshop is to look onward beyond recent achievements to discuss
specific topics, emerging research issues, new trends and endeavors in SIR for Technology Enhanced Learning (TEL). The
workshop will bring together researchers and practitioners to present, and more importantly,
to discuss the current status of research in SIR and TEL and its implications for science
and teaching.

TOPICS OF INTEREST (but not limited to):

Technology Enhanced Learning (TEL) and Social Information Retrieval (SIR) techniques such as:

  • Recommender systems
  • Social collaborative searching, browsing and sharing of queries
  • Social network analysis
  • Game-theoretic approaches to select learning materials and learning partners in the long tail
  • Social bookmarking and tagging, folksonomies
  • Annotations, ratings and evaluations

Concepts for Social Information Retrieval (SIR)

  • Defining the scope, purpose and objects of social information retrieval in TEL
  • Defining user requirements for the deployment of SIR systems in a learning setting
  • Current and new trends in SIR methods for TEL
  • Approaches to TEL metadata that reflect social ties and collaborative experiences in the field of education
  • Analytical modelling of strategic intentions in TEL communities
  • Interoperability of SIR systems for TEL

Implementation of SIR in TEL

  • Methods and models of SIR in the area of learning and teaching
  • Social processes and metaphors in learning communities and social networks for searching, acquiring and sharing information
  • Pedagogical aspects of SIR in TEL; how to scaffold students, activity patterns, etc.
  • Integrating SIR services in existing learning platforms
  • Visualisation techniques to support SIR in TEL
  • Successful scaffolding techniques for SIR implementation

Evaluation of SIR in TEL

  • Ideas on how can we get more empirical on evaluation
  • Best practices
  • Evaluation of the success and acceptance of SIR systems in the context of teaching,learning and/or TEL community building
  • Challenges and enablers
  • Evaluating the performance and measuring the effectiveness of SIR systems in learning applications;
  • Evaluation the user satisfaction with SIR system in supporting learning and teaching, etc.


This year we base our call for contributions on last year’s comments, where the participants wanted more time for discussions, for picking each other’s brains and to forecast how SIR could be used in TEL. Apart from more conventional contributions, we also have new formats for you to consider!

  • Research papers (4-8 pages)
    to present exciting new work that is not mature enough for a long conference/journal paper. We especially value papers with focus on evaluating early results and making them available for further discussion among practitioners.

  • Work in progress and System demos (upto 4 pages)
    allow participants to share the basics of their SIR for TEL applications. Papers can be short (upto 4 pages), but also different ways using screencasting or YouTube-type recordings of the demo are welcome. Include also information also needed on how others can access your system and test it.

  • Hands-On proposals (1-pager)
    Got a good idea for a SIRTEL implementation? Toying with ideas for SIRTEL prototypes, either totally new ones or based on some existing application (e.g. Amazon, Flickr, Digg, ..)? Interested in “pimping-up” your current LMS or platform to support social networks?
    Create a little scenario and write it down so that others can follow your thinking. Put in a few screen shots to illustrate your point better. During the session, which you will lead, the participants will have their hands and brains-on your idea. The outcome will help you with requirements of implementations in a TEL setting. Early ideas welcome!

  • Abstract for Pecha Kucha (5 min talk)
    Want to share your discussion ideas on SIRTEL concepts with others? We are listening! To leverage on the face-to-face of the workshop, we invite you to submit an abstract for CP type of presentation-discussion moment which you will lead during the workshop. Your talk can be max. 4 minutes long, the participants will decide how much discussion will follow.

Papers are to be submitted to:
Accepted papers will be published online as EC-TEL workshop proceedings
as part of the CEUR Workshop proceedings series.

The two best papers of the workshop will be published in a special issue of
the International Journal of Technology-Enhanced Learning (IJTEL)

More information at the submission site. All questions and submissions should be sent to: sirtel @


  • Alexander Felfernig, University of Klagenfurt, Germany
  • Barry Smyth, University College Dublin, Ireland
  • Brandon Muramatsu, Utah State University, USA
  • Clemens Cap, University of Rostock, TBC
  • Frans van Assche, European Schoolnet, Belgium
  • Fridolin Wild, Vienna University of Economics and Business Administration, Austria
  • Hendrik Drachsler, Open University of the Netherlands, The Netherlands
  • Jon Dron, Athabasca University, Canada
  • Lisa Petrides, ISKME, USA
  • Marc Spaniol, Max-Planck-Institute for Informatics, Germany
  • Markus Strohmaier, Technical University of Graz, TBC
  • Martin Memmel, DFKI, Germany
  • Wolpers, Fraunhofer, Germany
  • Miguel-Angel Sicilia, University of Alcala, Spain
  • Nikos Manouselis. Greek Research & Technology Network, Greece
  • Oliver Bohl, Accenture GmbH, Germany
  • Rick D. Hangartner, MyStrands, USA
  • Selmin Nurcan, University of Paris 1, France
  • Yiwei Cao, RWTH Aachen University, Germany


  • Riina Vuorikari, Katholieke Universiteit Leuven (K.U.Leuven) & European Schoolnet (EUN), Belgium
  • Barbara Kieslinger, Centre for Social Innovation (ZSI), Austria
  • Ralf Klamma, RWTH Aachen University, Germany
  • Prof. Erik Duval, Katholieke Universiteit Leuven (K.U.Leuven), Belgium & ARIADNE Foundation

Tuesday, May 13, 2008

Mine/d your data

I just participated in a week-long datamining course at the university. It was hard work, but actually a lot of fun. We plowed thorough a lot of things; including association rules, clustering, logistic regression, decision trees, neural networks, and also learned, well, made acquaintance with, some of the dataminging software like SAS Entreprise miner and used MatLab to check out the neural networks. What a strange world.

In one exercise we used the German credit dataset and wanted to come up with a decision tree to sort out the bad customers from the good ones. After lots of clicking and choosing values and setting roles, we came up with a tree that had an error rate of 47%. Wow. As well the banker could just flip a coin to choose which customer to give credit and whom not. Ok, probably a bad example, we did learn after that about the cost of misclassification, so we were able to make something better. But anyway, it just kind of made me laugh.

I was reading this blog and came across this interesting information about datamining methods that "miners" choose to use. Now that I know what all those words mean, this became an interesting piece of information for me :)

• Correspondingly, the most commonly used algorithms are regression (79 percent), decision trees (77 percent) and cluster analysis (72 percent). Again, this reflects what we have seen in our own work. Regression certainly remains the algorithm of choice for large sections of the academic community and within the financial services sector. More and more data miners, however, are using decision trees, and cluster analysis has long been the bedrock of the marketing community.
I personally thought that most useful techniques for me could be mining association rules, clustering analysis and maybe the use of decision trees. To be seen.

What I was actually pretty amazed about was that Datamining is very related to predicting missing values, i.e. the same methods that many recommender systems/studies use to predict the missing values of ratings. Another thing which was totally new was that Datamining and Machine learning are actually very related, well, quasi-overlapping, I guess.