Is eTwinning socially contagious?

Last weekend I joined more than 500 eTwinners (www.etwinning.net) in the 5th annual conference that took place in Sevilla. Quite a fiesta! I co-ran 3 workshops which all had something to do with social networks, more or less. I jokingly tell teachers that I'd like them all get an eTwinning virus and spread it around when they go home. This "virus" is, of course, a good one (e.g. innovative use of web in ed.context) and it spreads through the social network that teachers have created by being part of eTwinning. From where my question: is, or can, eTwinning be socially contagious?

Often times nowadays when people talk about social networks, they actually talk about social media tools or web 2.0 stuff, where it is made easy to express your social ties and make them visible to others (can I friend you?). Underneath all that "stuff" lies the structure of the social network which is of interest to me. I consider a network

as a conduit for the propagation of information or the exertion of influence, and an individual's place in the overall pattern of relations determines what information that person has access to or, correspondingly, whom he or she is in a position to influence. A person's social role therefore depends not only on the groups to which he or she belongs but also on his or her position within those groups. (Watts, 2003, p. 48)

Moreover, Watts goes on to explain yet a different way to view the network, namely through weak ties, "which can be thought of as a link between individual- and group-level analysis in that they are created by individuals, but their presence affects the status and performance not just of the individual who "own" them but of the entire group to which they belong." - and this all leads to the new science of networks.

Duncan Watts's book Six Degrees (2003) is one of my favourite science-tainment (like edutainment) book. I always enjoy picking it up and re-reading it, I seem to understand some of the passages in a new light. Today I re-read the stuff about differences of spreading a virus and "social contagion", like a fab that spreads or cascades throughout the whole social network.

There are some similarities, like the fact that each individual has a different threshold (some get the virus easier than others) and that you have people around to spread it to ("to whom she or he pays attention to"). But "social contagion", unlike biological one, does not take place if the network is too well connected!

So when everyone is paying attention to many others, no single innovator, acting alone, can activate any one of them. ... In social contagion, remember, it is the relative number of "infected" versus "uninfected" - active versus inactive - neighbors that matter. (Watts, 2003, p. 240)

Studying fabs or innovations (e.g. the use of web in ed.context), the question is about the moment when the fab stops being a niche thing among early adapters and when it leaps to the larger general population. I too often have a feeling that eTwinning "preaches to the converted". So, I'm keen on understanding how eTwinning can step out of being a nice of early adapters and get the others "contaminated". Someone aired a good comment in the conference, we should not take eTwinners as a representative sample of educational community in general.

Step one: who is infected?


I ran a few analysis to get a better picture. It's hard to find the number of schools or teachers in all eTwinning countries. After some digging I found OECD's Stat extracts which has lots of good data, and was able to find the number of teaching staff for 23 out of 32 countries (close to OECD's definition for EU19). I think that's a pretty good proxy to go by, however, not sure how accurately our data aligns with them. I used the date as described in the image for 2007.

The data says there was 6 210 411.57 teachers (I love the 0.57 teacher!) working in those countries, out of which 76 367 have registered in eTwinning by this date. On average, each country has 1.83% of their teachers infected by the "eTwinning virus", median was 1.42%. The countries above median in descending order are: Estonia, Iceland, Slovak Republic, Czech Republic, Slovenia, Finland, Greece, Poland (still above average, too!), Spain, Luxembourg, Portugal and Sweden. Are you surprised? I am a bit...

The countries below median were: the United Kingdom, Turkey, Norway, Netherlands, Italy, Ireland, Hungary, Germany, France, Belgium and Austria.

Well, my question is not about the success of eTwinning initiative itself, but it's more about understanding what are the conditions (globally and locally), what are the individual thresholds and when can we expect the cascading effect to take place (it's all about one or more vulnerable neighbors who have one or more vulnerable neighbors who have...).

These are typical questions that people interested in the new science of networks ask, and I want to know more how it happens within educational context. I think eTwinning is a good virus to study that!

We've just stated the TeLLNet project with some top-notch partners, so I'm looking forward to dwell into this problematic later again!

This is it! Resources that cross boundaries

Ok, I think this graph is the coolest kid in the blog!!




What you can see here are the communities of users by mother tongue (nodes) and the edges are the resources that these users have added to their collections.

This is a great visualisation of communities of practice. What you can see here at a glimpse is that the learning resources that these users have added to their collections, are very much community oriented, in this divided by languages.

I sometimes frame my research question as the following:

Does a multi-lingual and multi-cultural learning resources portal rather act as one system divided into different language or country groups, or is it more like one monolingual system with its own sub-groups and communities of practice (think of a system like delicious) that cross the language and cultural borders?

This visualisation seems to point more to the first one (this REALLY needs to be further investigated!!), it seems that users are divided into groups by mother tongue. Why I say so is that you cannot see many resources that are shared among the groups.

To play around with this by yourself, make sure that you click on the arrow head down at the menu bar. This allows you to see in which directions the links go. They often time just go to one direction.

There are some resource that indicate communities of interests between countries. For example, in this image, we can see that there are some resources that are shared by both Estonian and Lithuanians. One of them is highlighted in orange.

These are the interesting resources as they cross between boundaries. The more I think of it, the more I'm convinced that you cannot call these call boundary objects (see my previous post). If I got the boundary object right, they are the objects that help these two groups to talk to one another, because they do not share the same language or jargon. But in this case, I think it's the contrary, these people share so much the same, that they can even share resources in Russian (of course being ex-Soviet countries, Russian is a common knowledge).

Anyway, even if the rather disappointing news were that users on an international portal seem to stick to one another based on their mother tongue rather than common educational interests, the good news is that I believe that through making more social cues and traces available to them, they would actually start exploring the resources in other languages and other areas.

And besides, who says that my data here really actually displays this community correctly!? This is based only on the common resources that users have put to their collections. Actually, LeMill is more of an authoring environment, so maybe a better way to study this community would be through collaborative authoring of learning resources? Or something else, like common search terms or tags that are used.

So, take this exploratory description of this data set with a little bit of skepticism!

In what languages are the resources that end-up in collections?

Well then, I guess that will be a no-brainer...

In this visualisation, you can see the languages of resources (e.g. English) as nodes and the languages of users as edges (e.g. en, de..).


If you click, for example, on English, lot of edges are highlighted. Those are the mother tongues of users who have bookmarked these resources. After little bit of playing, you'll find that English resources, and the ones with no languages, seem to be most popular with users.

However, it is cool to see that resources in other languages also end up in users' collections. Here, for example, you can see that Czech (sorry for misspelling) are used also by users with Polish and Lithuanian as mother tongue.

More analyses are needed to give you any numbers, but this already is an interesting insight.

Resources country of origin and user mother tongue

This visualisation shows the links between the country, where the resources in the collections were created in, and the mother tongue of the users who had added them in their collections. You can explore the diagram by yourself.

This image here shows how, for example, resources created in Finland (the orange node in the network) have ended up in collections of users who speak Hungarian, Estonian, Lithuanian, etc. as their mother tongue.

Note that this graph does not make any assumption of the language in which these resources are in! If I'm right in my guess, most of these resources were in English, not in Finnish..

But anyhow, I find that as a demonstration that these resources can cross borders of some kind. In this case, a Finn has created the resource. It can be just a very little hint available in the design of the resource that it was a Finn, but still some of the underlying pedagogical assumptions or some hints of Finnish curriculum might be embedded in these resources. Nevertheless, or thanks to that, the resources created in Finland seem like a hit (they are in 8 different language groups).

Ok, to me more truthfully, I think this is because LeMill was create in Finland that many of the Finnish resources are shared.

About networks of resources and users

This visualisation is to explore the networks of users that form between resources that are shared in collections. I think this is one of the most interesting visualisations of the dataset, and the one that inspires me the most.

Same as before, click to interact within the image, or if you click on the title on top of the image, you can get the network in a bigger window.


What's there? It's a network diagram where the nodes represent users (user id number) and the edges are the names of learning resources that these users have saved in their collections.


You can zoom into the diagram and explore it. Same as with the previous post, we can see that lots of the resources that users have put in their collections are not shared with other users. These are the singletons that are not part of the common network here.

Then, there are some star like structures that can be found. Like this one. Here the resource highlighted is something that both users (user 59 and 155) had added into their collection.

What I think, I would almost bet on, is that if these users were made aware that they share this resource in their collections, they would be interested in looking at what other resources are in the other person's collection. In this case the user 59 could be interested in looking at the collection of the user 155 has put in her collection.

This basically would be the idea of making underlying social networks visible in a repository to allow social navigation of like-minded users collections. Or, if you wish, a recommender could take advantage of these underlying connections as well. For the recommender, though, the data is very sparse, as can be seen from the visualisation. For that reason, I think we first should explore social navigation possibilities, and then launch for recommenders, when we get more data.

These resources that connect users, or in some cases (hopefully one day) even communities together, are valuable stuff. I have previously referred to this as one way to identify learning resources that cross borders easily. In this case, the two communities could be speaking different languages or be from different countries.

Some suggested that these objects could be also boundary objects. I cannot get my hands on the original article now (frustration of working from home!), so I am referencing some others that reference it:

Star (1989) and Star and Griesemer (1989), on the other hand, are concerned with the distribution of artefacts across communities. Boundary objects are artefacts used by communities: they cross the boundaries between communities and retain their structure, but are interpreted differently by them. The notion of boundary objects was developed by Star (1989) and Star and Griesemer (1989) as a way to explain co-ordination work between communities.

In a larger sense, maybe some of them could be boundary objects. I will need to think about this more..

Anyway, here is another little visulaisation that is actually an overview of the resources that users have saved in their collections. You can visualise it in many ways, you the ordering function on the top.




Star, S. L. 1989. The structure of ill-structured solutions: boundary objects and heterogeneous distributed problem solving. In Distributed Artificial intelligence (Vol. 2), M. Huhns, Ed. Morgan Kaufmann Publishers, San Francisco, CA, 37-54.

Learning resources as part of collections - what about the network?

I'm just exploring a new dataset that I got from LeMill, it contains information about learning resources that users have put in their "collections". Collections is a tool for users to create their own sub-sets of resources and give them a common title, e.g. I find 5 resources on pyramids, I add them to my collection, and I call it "Pyramids for 5th graders", as I am going to use it during my History lesson that I teach with 5th graders.

I think that collections-tool is an excellent tool, also for me as a researcher ;) What I am interested in knowing is whether we could make the links between these collections visible. The link would, of course, be the resources that are shared with collections.

Let's just explore the early visualisation of LOs connecting the collections. Click on "click to interact", and you get the life image. Alternatively, you can click on the title in the image, and you'll have the whole visualisation in a bigger interface. So what's there?





What you first see is a top-level overview of users' collections using a network diagram. It first looks like a grid; the ones on the top left hand corner are small one, they only contain a few resources. The other ones towards the right bottom corner look more clunky and visibly bigger, they include many more resources and are actually overlapped one with another.

You can start zooming in with your mouse. You see that some names will start appearing. Those are the name of the collection and the resources within. With a right click on your mouse, you see a hand appearing. This allows you to move within the visualisation. What you see here is a huge amount of what is called “singletons” in the network jargon. These singletons are collections, but they do not have any connections through shared resources to other collections.

Now, try to locate yourself in the area where that big cluster is, at the bottom right hand corner.

Now, instead of looking at separate little singletons, we are hoovering over a “giant component”. This is clearly the largest group of nodes within this network and some of them seem interconnected. With interconnection I mean that the same resource is in more than one connection.

You can visualise this nicely, if you click on some of the big nodes. It will be highlighted in orange. This way you can see what are the resources related to this collection (the collection name is the node). Interestingly, you'll see some of the resources act as a connection between different collections.

What we can already quickly see is that something called “middle regions” are entirely missing from this network. They represents rather isolated groups that interact amongst themselves. In our case they would be a few resources that are in a few collections by a few users. There do not seem to be any such "isolated stars" in this network of collections. The cool thing about these isolated stars is that over some period of time, they might merge with the giant component. This would happen through a resource that is shared in both the giant component and the smaller entity.

Ok, visualisation is just a visualisation, a snapshot of a moment. More work is needed to properly analyse what is going on, and most importantly, does this have anything to do with how we can make a repository of learning resources a better place?

Well, I of course am on my SNA trip and think that it can help anything and everything, but more about that later..

Reference

eTwinning/bookmarks and social networks

Excellent write up here about SNA on social networking sites. This makes me think of eTwinning, or my social bookmarks, and the SNA analysis there to better support users.

Kumar, Novak and Tomkins (200&) saw that network activity is of three types:

• “Singletons,” who have no connections and are least central

• The “giant component,” which is the largest group of nodes tightly connected to the central nodes and to each other
• The “middle region,” which represents isolated groups which interact amongst themselves but not with the rest of the network, forming isolated stars. These groups grow one user at a time. Over time they merge with the giant component.
  

The node analysis of these networks showed that more than half of a social network is outside the giant component where the greatest centrality lies. They used the “control” definition of centrality to determine this. The research also highlighted a prevalence of “stars” in the middle region which are mini social networks, typically driven by one dynamic member who serves as the point of centrality with others serving as satellite nodes – connected to the dynamic member but not to each other. In Kumar, Novak and Tomkins’ analysis the middle region represented one-third of users on Flickr and about ten percent of users on Yahoo! 360.

Also keep in mind that the most growth happens in the middle region where dynamic members influence others to join their network. These sub-networks can gradually join the giant component over time. Once they do, the importance of the dynamic member diminishes. Even if that dynamic member were to leave the network, the others would stay in the network.

So, what is needed is to support the "stars" in their growth so that they become independent of that one dynamic member and are able to continue even without that person.

Open social and education

I wonder who is going to come up with the first OpenSocial app or widget for educational use? We certainly are talking about it, for example for our eTwinning platform. It could be cool to be able to use information about teachers collaborative networks to allow, say, better retrieval of learning resources relevant for the project, purpose or task that teachers are undertaking; link with some other sources that teachers are working on through cool widgets, etc.

I never thought that Facebook, which has lately become really popular among my friends (not early adapters), would be the seul app that would "take it all". I was glad to read this:

"The market has already decided that there's going to be a long tail of social networks, and that people are going to belong to more than one. As soon as you belong to more than one, this kind of interoperability is critical," Dash says. "Open standards win every time." wired

Hurray for open standards!

SIRTEL'07: la raison d'etre

"We use people to find content. We use content to find people."*

On Sept 18 our SIRTEL workshop takes place. It's gonna be "Serious Fun"! Let me just outline why:

SIRTEL'07: Raison d'etre

Recommender systems, as well as social navigation, have been around since the popularisation of WWW, that's some 15-20 years now. The idea is to help people choose the right stuff from a potentially overwhelming set of choices. To facilitate that users could be helped with information from other users, the choices made before (by themselves or similar users), the ratings or reviews other people had done, etc. (Rescnik et al., 1997)

The field of learning technologies has seen recommenders of some sort being discussed and prototyped since the late nineteens. In the review of the field in Manouselis et al (2008) we identified about 10 recommenders, and even more conceptual papers of them, but very little has matierialised so far.

Since the last few years recommenders have made a second arrival into the discussion topics of technology, or network, enhanced learning. Undoubtedly, this has been influenced by the arrival "Web 2.0" with all its ideas:

- Collaborative tagging, for example, has changed lots of ideas of how metadata should be produced and how static a metadata record should be: it's not anymore one metadata record produced by a librarian, but lots of annotational and attentional metadata by lots of users.

- Other annotations by users that express their subjective judgements have seen a huge growth too, we don't only talk about ratings or reviews in their traditional sense, but also tumbs-up or down, giving pokes to people or objects, etc.

- Social bookmarking, which allows users to create easy references to their own collections of digital resources (photos, books, links, music,..), has given a new dimension to the concept of social navigations. The link between resource-user(-tag) allows users to navigate other people's collections and thus find novel resources. Also, the same resource-user-tag link gives researchers an itch to use this information to group similar users for recommendation purposes, as well as to study the emerging networks.

- Expressing social ties between people has also brought new possibilities along. We are not only seeing networks of friends, but there are new possibilities where people can express different networks, ones for professional use, others for personal, recreational, etc purposes. Also, portability of these networks has become an issue discussed for better designs (social-network-portability group, PeopleWeb ,..).

- Something else is also happening behind the scenes. Clicksteam and user behaviour on the Web is not anymore a property of the commercial portal on which users are, but users are starting to take seriously how their "attention" is being used, who owns it, etc. Attentional metadata is a huge source of information that educationalists are also starting to take more seriously and thinking how it could be used for better serving learners and teachers (Contextual Attention Matadata, Attention Profiling Mark-up Language, Attention Trust,..). Attentional metadata can also become crucial when it comes to better understanding the intentions of a user, why are they, for example, looking for some information and for what task at hand!

- Finally, content for educational use, or rather its production, is also seeing a change. Users generate more and more of the content on the Web in general, a trend which is also seen in the e-learning. Of course, traditionally teachers have always produced lots of their own material, but now its re-use also has been facilitated (e.g. repositories/referatories). Also, the collaboration aspect is facilitated by the Web, it has become easier for people to work together on things (e.g. wikis, collaborative platforms,..). Additionally, learners produce plenty of material which also should be seen and used as educational content.

To sum-up: two main topics evolve around social context and social content. Social context is how we express the who, where and with whom, and social content are the objects or digital artefacts that are in the center of the communication, exchange and networks.

All the above has hopefully also changed how we will see the future of social information retrieval for technology enhanced learning. This workshop will all be about that! Serious Fun!

-------

N. Manouselis, R. Vuorikari, F. Van Assche, “Collaborative Filtering of Learning Objects for Online Communities: An Experimental Investigation”, accepted for publication in Computers in Human Behavior, Special Issue on ‘Advances of Knowledge Management and Semantic Web for Social Networks’, 2008.

P.Morville, 2004

Resnick P. & Varian H.R., “Recommender Systems”, Communications of the ACM, 40(3),1997

Workshop on Social Information Retrieval in Technology-Enhanced Learning (SIRTEL07)

Good news! The workshop proposal for EC-TEL 07 was accepted, so I will be co-organising my first workshop on social information retrieval techniques in support of learning and teaching later this September.

The tag line will be "We use people to find content, we use content to find people" by Morville. On the other hand, maybe it should be "We use digital traces to find people, and we leave digital traces to be found"..

Two main focuses: Recommender systems and Social navigation

The list of topics will be LONG, but I put it in here as an appetiser:

• Defining the scope, purpose and objects of social information retrieval in TEL
• Recommender systems and collaborative filtering in educational settings
• Novel ways of generating input information for recommenders in the area of learning and teaching
• Ranking of search results to support individualised learning needs
• Folksonomies, tagging and other collaboration-based information retrieval systems
• Social navigation processes and metaphors for searching information related to teaching and learning
• Analysing social interactions in learning communities and social networks on the Web to facilitate information sharing and retrieval
• Approaches to TEL metadata that reflect social ties and collaborative experiences in the field of education
• Interoperability of SIR systems for TEL
• Integrating SIR services in existing learning management systems
  
• Visualisation techniques to support social navigation in learning and teaching
• Semantic annotation and tagging for social information retrieval purposes
• Evaluating the performance of SIR systems in educational applications
• Measuring the effectiveness of SIR systems in supporting learning and teaching
• Evaluation the user satisfaction with SIR systems in supporting learning and teaching

The idea is that as this is the first European workshop on the topic, we will try to scout out who are there to work on this topic and set the ground for better future collaboration . Of course we wish to run the workshop again, not as a pre-workshop , but really as a part of the main show.

Voila, more info to come shortly and the website for the call!

What tasks teachers have on a learning resources portal?

The attitude of "if we build it, they will come" has resulted in national and regional learning resources portals where the offer, no matter how many learning objects or assests, does not necessary match the need of teachers. Why is that? What is it that teachers look for?

Curriculum coverage

I first started by looking at 29 different learning resources portals that national and regional educational authorities offer for K-12 teachers in Europe. My task was to find out how many of them offer curriculum related material, i.e. so that a teacher, knowing that tomorrow he has to teach an area that covers a certain goals of the national curriculum, can just go to the portal and pick a resource that actually goes through this particular area.

This type of standards-based curriculum material seem to have been on the offer in the US for some time. Two examples could be the DLESE http://www.dlese.org, focusing on Earth Science and IDEAS, http://ideas.wisconsin.edu a repository held by Wisconsin educators. In both teachers can search for a curriculum coverage.

I found that in about 10 out of 29 examples in Europe teachers can explicitly look for curriculum coverage on learning resources, however, it was not always very clearly indicated which goals or skills a resource aims to attain.

On rest of the portals teachers were able to find resources that were categorised by the school level and subject, thus, by using teachers internal knowledge of curriculum, they would, with little poking around, find the matching material. But the problem with this case is that there is nothing that makes the teachers' tacit knowledge externalised for other users, no one else can take advantage of the fact that this teacher knows how well this piece of learning material covers certain areas and goals of the local curriculum.

In MELT, we are trying to get to the core of this problem by encouraging teachers to add tags to resources that they find in the repository. We'll be eager to see whether tags can externalise any of that tacit knowledge that teachers have, and that could help other teachers in finding and using the material.

In Calibrate, another project that I have a minor involvement, we have an opposite approach to the problem. A few topic areas have been selected where 3 EU-countries share a similar curriculum. We try to map the different curriculum through its goals and skills, and match that with the material available. A complex task!

Tasks at hand

Then, I started thinking whether learning material that clearly covers the standard-curriculum is what teachers want? One could think that a busy teacher would really appreciate it, however, there might be other reasons why do they come to a learning portal. So I made a little poll with some tasks that I thought teachers could have, and asked some teachers to vote. I only got 36 votes so far (you can still vote here), but it seems that, at least for these teachers, the curriculum coverage was not what they preferred!









The majority of the respondent teachers (I don't know who they are) seem to be leisurely window-shopping while at the learning resources portal (I browse around to see what is available) or looking for material that could support the lesson that they are planning. Only one teacher is looking for learning resources with an exact curriculum coverage!

This rises two questions in my mind: either teachers have given up to look for curriculum covered material, because a) it never was on the offer, b) it was never successfully on the offer or c) they have better sources for that, like school books, etc.

Secondly, maybe teachers don't really know what to expect from a learning resources portal or a repository. Maybe it was never clear for them what was the intended goal of a learning resources portal and they just come there to see what's up, what's in there and maybe they return if something good is found.

Seriously thinking, do we even know for what tasks and goals the resources repositories are build? If we think of libraries, we know they have a clear goal, or a school book, yes, it has a clear goal. But a LOR (for learning object repository), isn't it something in-between, kind of pretends to be one or the other, without being either of them.

I just looked at the sneak-preview for Yahoo!'s new service for teachers. Pretty neat. They clearly aim it to be to create learning resources, and re-use the ones from other teachers. They also offer neat peer-networking possibilities. It's about using and re-using material that is in the center, not searching for it! A very different focus.

So, I think what should be build in on learning portals is a better support for the tasks that teachers and learners have at hand. So, when they come to a portal,

• if they clearly are just window-shopping, let's provide them with first-grade Champs-Elysees shop-windows. Make nice pre-views available of resources that are there with added value lesson plans and case studies how others have used them in their lessons. Allow browsing other users' collections of learning resources with annotations and comments. These other users can be from any part of the continent, as the goal is to inspire and show how things can be done. If we know anything about the user, let's try to match them with like-minded peers!
  
  
• if they look for material with curriculum coverage, let's lead them to an area where they can either search for material with curriculum coverage, or browse bookmarks and tagged resources for cues from other teachers on attaining certain skills and goals. For the latter, it could be useful to first show "traces", e.g. bookmarks, tags, pedaggical annotations, from teachers who come from the same area, like Yahoo! peer-network allows getting close to teachers from the same area (=same curriculum).
  
  
• etc.

The point that I'm making is to be clear about the task, and the information seeking patterns in general that teachers have, and then match it with the best way providing search, social navigation, recommendations, shop windows, etc., but always thinking what would yield the benefits for the user and task at hand.

That's something to study deeper, and don't worry, I'm on it ;) We don't know yet if the best benefits can stem from using underlying social networks (location) or more implicit ones (profile, tags, similar bookmarks), or from using a search or what?

Where tags and structured vocabularies co-exist, case for tagging learning resources

NOTE: this is a draft and lot of thoughts put together

Context

In the Learning Resources Exchange-portal end-users, i.e. K-12 teachers from all over Europe can access digital learning resources that are made available by European Schoolnet or by its partners from different learning repositories from a number of European country. All of the learning resources have associated metadata to them, all of the partners use Learning Object Metadata, and many of them an application profile specifically geared to European K-12 education. The indexing keywords come from a multilingual Thesaurus that currently exists in 14 languages. Additionally, teachers can add their own keywords to learning resources when saving them to their personal collections, that function like bookmarks. Also, in their personal collections teachers can evaluate their learning resources, rate them and add comments. This paper focuses on vocabularies, both unstructured and structured ones, and proposes a complimentary approaches to their use to enhance learning resources retrieval. We think that tagging has the potential to enhance the retrieval, both through the complementary features that it can offer for controlled vocabularies, as well as through its underlying social structures.


Structured and Unstructured vocabularies to access resources

The table "axes of organizational vocabularies" depicts the dimensions of structured and unstructured vocabularies. Learning resource repositories, for example, often use an authoritative vocabulary to index the material that is based on their national or regional educational needs such as requirements in curriculum or educational levels. Some of these vocabularies are structured domain taxonomies with hierarchies (LRE thes, EUN, Italy; Mobus thesaurus, France;,..) whereas others might be unstructured glossary type lists of terms (eg....). These vocabularies are used in educational repositories and portal most often to provide access to resources by classification or allowing hierarchical browsing, search functions are equally applied, however, teachers seem to prefer browsing features (based on focus groups and some Calibrate logs).

	Structured	Unstructured
Authoritative	Domain Taxonomy	Glossary
Personal/Opportunistic	Hierarchical Filesystem	Folksonomy

Axes of organizational vocabularies, Iverson (2006), CC some rights reserved.


As opposed to authoritative vocabularies, personal tagging of resources provides an unstructured vocabulary for resources. These end-user generated keywords serve, in the first place, personal knowledge management needs. For example by bookmarking resources one creates a link to the resources to access them later, and the use of personal keywords allows naming things in a meaningful way, grouping them, etc. Depending on the user-base and behaviour, tagging can start providing a community based vocabulary for the given community of users. This is commonly known as folksonomy. It is important to note that not all the actions of tagging and personal vocabularies allow a community folksonomy to emerge, thus the division to broad and narrow folksonomies (Vander Wal, 2005).

Classified categories or domain taxonomies offer a way to access resources, either through keyword based retrieval or browsing through categories. A solid indexing strategy based on controlled vocabularies guarantees that the searcher does not need to face all the uncertainties of natural language (synonymy, polysemy, homonymy), combined with all the uncertainties of a full text search (no relevance control on the retrieved occurrences) (Trigari, 2002). Both searching and browsing, based on metadata and controlled, structured taxonomies, are methods most used to access resources on educational portals.

Browsing and searching serve two fundamentally different information seeking tasks. It is similar to the difference between exploring a problem space to formulate questions, as opposed to actually looking for answers to specifically formulated questions (Mathes, 2005). One could say that searching happens at the stage where user already have a clearly defined task, a well formulated question, and an idea how and by what information sources to fulfill it (reference), whereas browsing many times occurs when the user has not yet a clear question or task at hand, rather just a hunch of what to look for.

Second important advantage of folksonomies is that they can serve the community by promoting a serendipitous access to resources through browsing interlinked related tag sets, also known as "tagclouds". A tagcloud can be a personal one comprised only of the personal keywords, it can be a local one to display the most common tags by a given community of uses (e.g. based on domain interest, language, etc.) or even a global one allowing an access to all resources that are available through the service.

Complicity of relationships between thesauri and folksonomies

An interesting hybrid of vocabularies will emerge within the ELR context, where users add personal keywords to learning resources that are already indexed by an authoritative, structured domain taxonomy such as the LRE Thesaurus. As opposed to most Web-based applications where no top-down vocabulary exist and where users tag content with their personal keywords (del.icio.us, Flickr,..), the situation is different in ELR. Here, a learning resource is indexed using a 1 to 3 thesaurus terms that further allow search and browsing in a multilingual context, added also with multilingual tags by end-users. This will open a number of interesting research possibilities in the twilight zone of structured and unstructured vocabularies.

In the LRE Thesaurus the structure of the descriptors follows the classical semantic relationships such as the following: intra-language equivalence (USE-UF), inter-language equivalence, hierarchical BT/NT relationship and the associative relationship (RT). The research attention will be directed to study the relations between terms in all the areas. For the revision purposes of the thesauri, we will be interested in looking whether folksonomies could provide any new descriptors for the LRE Thesaurus.

Maybe the most potential area of complicity between folksonomies and thesaurus will be in the area of inter-language equivalence (USE-UF) between the thesaurus terms and folksonomies, which could be seen as a good source for non-descriptors. Non-descriptors are part of thesaurus, they provide the intra-language equivalence that facilitates the access to resources that are indexed by using the descriptors (i.e. thesaurus term) that do not translate well to the language that the end-user uses. For example, a user could search for antiquities, but the preferred term in the thesaurus, the one used for indexing, is "ancient history". The use of antiquities as a non-descriptor thus facilitates the access to the resource.

Example of inter-language equivalence (USE-UF)

Antiquities USE ancient history
ancient history UF Antiquities

Moreover, an equally interesting area will be associative relationships (RT) and folksonomies. The associative or related term relationship is expressed in thesauri between terms that are mentally associated to such an extent that it's useful to make the link between them explicit, however, they are not members of equivalence set, neither are subordinated or superordinated to another (Trigari, 2001). We think that folksonomies could help to define and re-define a number of associative relationships in thesauri.

Example of associative (RT) relationship
NT1 old age
. . . . RT elderly person

As the ELR Thesaurus is also multilingual, we will be interested in investigating what kind of inter-language equivalence folksonomies could offer between languages. As for the area of hierarchical broad terms and narrow terms (BT/NT relationship) between the thesauri terms, we assume that folksonomies have very little to offer.

All in all, additionally to the above, for the revision purpose of the thesaurus, we think that folksonomies can be a great source to verify whether the scope of the current descriptors actually serves the need of its users for indexing and retrieval purposes.


To better understand the nature of folksonomies and tagging

A number of interesting research papers have emerged in the area of tags and folksonomies. Mathes (2005) described the phenomena, whereas the term and its main characteristics such as broad and narrow folksonomies were described by Vander Wal (2005). Broad folksonomies can be defined as social tagging systems where many people tag a few items (e.g. del.icio.us), whereas narrow folksonomies are the ones where content provider, usually the owner of the content, gives tags to items (e.g. flickr). Mainly, folksonomies emerge from broad ones, as there are many users and also niche user groups, who give the item the name that they know it for. Through the recent research we also know more about the evolution of tagging and how users provide them (Sen et al., 2006). Marlow et al., (2006) propose a model and taxonomy of different tagging systems. Moreover, Bogers et al. (2006), Lin et al. (2006) and Tonkin (2006) have provided interesting insights into the issues between classification and tagging.

By studying tagging and folksonomies more closely in this specific hybrid setting of learning resource,s that are indexed by experts and tagged by end-users, might yield some more interesting insights into its nature, both from qualitative and quantitative point of view. More so, we are interested in finding best ways to have social tagging systems to co-exist with more top-down indexing with structured vocabularies, and find out ways that can leverage such a system to better serve its users' needs. Also, research in this type of setting might allow us to better understand the many downfalls of folksonomies, such as ambiguity of tags, their synonym (different word, same meaning) and homonym (same word, different meaning) control (Guy, 2006). Moreover, we are interested in using the folksonomies to tap into the social structures and networks to, first of all, study and analyse the charasteristics and the main behavior of our user-base, and secondly, to use the social networks to enhance the information retrieval and serendipitous access to resources.


References

Bogers, T., Thoonen, W., & van den Bosch, A. (2006). Expertise classification: Collaborative classification vs. automatic extraction. Social Classification: Panacea or Pandora? 17th Annual SIG/CR Classification Research Workshop. Retrieved from http://www.slais.ubc.ca/users/sigcr/sigcr-06bogers.pdf.

Guy, M., & Tonkin, E. (2006). Folksonomies: Tidying up Tags? D-Lib Magazine, 12(1). Retrieved November 10, 2006, from http://www.dlib.org/dlib/january06/guy/01guy.html.

Iverson, L. (2006). Thomas Vander Wal on Folksonomy. Blog posting. Retrieved from http://www.ece.ubc.ca/~leei/weblog/2006/03/thomas_vander_wal_on_folksonom.html.

Lin, X., Joan, B., Yen, B., et al. (2006). Exploring characteristics of social classification. Social Classification: Panacea or Pandora? 17th Annual SIG/CR Classification Research Workshop. Retrieved from http://www.slais.ubc.ca/users/sigcr/sigcr-06lin.pdf.

Long, K. (2005). Leveraging folksonomy - flickr clusters at ExperienceCurve. Blog posting. Retrieved November 10, 2006, from http://blog.experiencecurve.com/archives/leveraging-folksonomy-flickr-clusters.

Marlow, C., Naaman, M., boyd, D., et al. (2006). HT06, Tagging Paper, Taxonomy, Flickr, Academic Article, . Hypertext 06. Retrieved from http://www.danah.org/papers/Hypertext2006.pdf.

Mathes, A. (2004). Folksonomies - Cooperative Classification and Communication Through Shared Metadata. Retrieved November 13, 2006, from http://www.adammathes.com/academic/computer-mediated-communication/folksonomies.html.

Sen, S., Shyong K., L., Cosley, D., et al. (2006). tagging, community, vocabulary, evolution. Proceedings of CSCW 2006. Retrieved from http://www.grouplens.org/papers/pdf/sen-cscw2006.pdf.

Tonkin, E. (2006). Searching the long tail: Hidden structure in social tagging . Social Classification: Panacea or Pandora? 17th Annual SIG/CR Classification Research Workshop. Retrieved from http://www.slais.ubc.ca/users/sigcr/sigcr-06tonkin.pdf.

Trigari, M. (2001). Multilingual thesaurus, why? . European Schoolnet, ETB project.. Retrieved November 13, 2006, from http://etb.eun.org/eun.org2/eun/en/etb/content.cfm?lang=en&ov=3813.

Vander Wal, T. (2005). Explaining and Showing Broad and Narrow Folksonomies :: Personal InfoCloud. Blog posting. Retrieved November 13, 2006, from http://www.personalinfocloud.com/2005/02/explaining_and_.html.

On Social Network Analysis and Recommenders

An interesting area where I drifted today is the crossing point of recommender systems and social network analysis (SNA). I read a few papers in a row about it (Rashid et al., 2005; Korfiatis et al. 2006, not published; Carcia-Barriocanal&Sicilia, 2005),

The other day I chatted up with my PhD study-buddy on SNA and it was actually quite enlightening. I was seeking to understand what is the difference between what most (old-school) recommenders do, and what does SNA have to offer to this. SNA are used to better figure out what groups do and how do they form, etc, but I lacked the understanding of how to use this for something that I want to do, i.e. enhance the discovery and re-use of LOs in a repository.

I am an avid believer and lover of social bookmarks. That's it, it's out. I think we could do so many things better just doing that. I, of course, just have to prove that in my PhD, and find a way to prove it, so it helped to talk with my buddy who is researching stuff somewhere between behavioural economics and social network theory. He had the words that I was lacking for social navigation – you get in a social space and you don't have any clues of what is out there. What do people do. They follow others, they need a guide. Say, you see other people going one way and you follow. That is what I see social bookmarks offer you, a guide to go ahead, a direction, a pointer to start. But there is also another aspect to it, bookmarks offer connections, relations between me and things I like, and then again, between things I like and other people who like the same things.

Which brings me to - how can we leverage this for information retrieval (IR). Sicilia and Garcia, 2005 and Korfiatis et al. 2006 (not published) talked about this: to bridge the areas of Social Network Analysis (SNA) and Information Retrieval. In a way, already the famous PageRank is about social networks, who endorses whom in a form of a hyperlink. The only problem being is that we do also link to things that we don't care about...but back to recommenders...

Until somewhat recently recommenders were about ratings and explicit values that people gave to items. The big deal was inferring those values for users who had not explicitly done that or even interacted with the item. Nowadays we are moving into using all other kinds of data as an input for recommenders, like the context-aware attention metadata that my colleagues are looking into.

The idea of Contextual Attention Metadata-framework is that it would log data from different application that a user is using for the e-learning purposes. The fact is, that nowadays we are getting further and further away (at least mentally) from single big Learning Management Systems (LMS) and are more and more looking into using small “comfi” tools (IM, bookmarks, wikis, blogs,..) for learning purposes too. All these tools can generate attention metadata, and a framework like CAM could track that. A step ahead from conventional data-mining from separate and sparse log-files.

So, now are are looking into contextual attention metadata that can arch across application boundaries and tell us stuff like: after watching that educational movie, the learner 3 contacted a tutor by IM and then spent an hour working on a text editor while surfing on the Web using x and y keywords. From that we can try to deduce things (like how the learner actually uses the learning tools and material) that we could use to make more personalised recommendations.

What I find more interesting, though, is the social context, like PeopleRank (Carcia-Barriocanal&Sicilia, 2005; Korfiatis, 2006 n-y-p), that could be used to compliment something like PageRank. PeopleRank would use the social ties, i.e. the links that people have expressed in a FOAF-file to compliment the “conventional” the PageRank algorithm. That's cool, all right, although, just right from the bat I feel like I prefer the Yahoo's MyRank, that also uses a FOAF-description on top of their conventional search algorithm. Moreover, I would be interested in finding some other ways to use the FOAF-file, which I'm trying to think of. Maybe some more interesting things could, in deed like suggested by Carcia-B..&co, come from the use of foaf to express relations between organisation or group (schools, educational projects,.- like we could use it in our EUN-context), instead of individuals.

Well, back to my bookmarks and tags: I'm interested in observing on what happens in a repository of LOs where users can bookmark learning resources, socially navigate them in other people's collections, when tags are used and when people can rate and evaluate LOs that they have in their collections. Furthermore, we like to facilitate the creation of lesson plans, like one would create play lists in iTunes.

Recommending educational material to teachers and learners, automatically sequencing course material or aggregating learning resources and delivering personalised learning has in many research oriented projects relied on pedagogical concepts, on learners learning styles, on assessment of previous knowledge and skills, etc. This is probably very useful and has undoubtedly many potentials. (First we only need kind of standardised testing to assess skills and then plentiful pool of varied learning resources that comply to any different learning style, oh yeah, and which definition of learning styles are we going to use...).

Instead, I'm interested in tapping into the social power of a group of educators and their knowledge about what learning resources to use and in what case. Instead of looking into personalisation-side of things, I want to see what happens if we just look into socialisation-side of things. Do like others have done-kinda idea. If other people cross the street here, maybe I should cross it here too.

Of course we would have to assume that there are some personalisation going on, each case is unique, after all. But still many cases do resemble one another. And maybe looking into social navigation in an educational context can help us to unlock the problematic and labour intensive questions of recommending educational material.

Additionally, bookmarking comes with tagging, user-generated keywords that people can assign for material to find it later. That's the personal knowledge management side of things. Tags can also create communities, people interested in same things eventually end up using similar names/tags and thus a link is formed. Tags also make us understand better the different meanings and ways that people can understand “a thing”, etc..In the LOR-context tags could make explicit some of the teachers' "folk pedagogy" type of knowledge. Folk pedagogy can be an accumulated set of beliefs, conceptions and assumptions that professors personally hold about the practice of teaching (Bruner, 1996). Maybe this can also unlock something that we don't know of, yet.

Well, some thoughts that I've decided to write down to keep track of my thought.