US Common core state standards and OER that travels well

I've followed the US Common core state standards (CCSS) initiative (http://www.corestandards.org) with some interest. It's not so much from the point of view of harmonising eduction or setting the same standards across the nation, but I'm more interested in it from the Open Educational Resources (OER) point of view.

One of the main problems for educators who want to use OER as part of their lessons is to find good resources that match their curriculum needs at the moment. Interestingly, the US has the same problem here as we have in Europe: each state/country has their own standards, curriculum topics and descriptions. Additionally, in each state/country topics are taught at different times, in somewhat varying order, etc. (and I'm not touching the issue of different styles of instruction/pedagogy here). This makes it almost impossible to find resources related to a certain curriculum topic from the different state/country. Why? Because the metadata descriptions on which many of the search mechanisms rely upon (at least in repositories and referatories) and their meaning differ from state/country to another. In other words, there is little semantic interoperability.

Let's take an example of mathematics and a targeted competency that is described in the national curriculum. It could be formulated as "Pupils know the symbol rules for whole and rational numbers". In Spain, this is only taught at the first year secondary school when students are 13 years old. But in Finland, this is taught in primary school when students are 12 years old. (NOTE, this is only an illustrative example, I'm totally making the specs up). So the Spanish learning resource which teaches "symbol rules for whole and rational numbers" is described in metadata using "secondary education" and 13-14 years, whereas the Finnish one is described in metadata using "primary education" and 11-12 years. So when the Spanish teacher uses her search criteria relevant to her national curriculum, she will totally miss the Finnish piece OER on the same topic because of lack of semantic interoperability.

From the metadata and search point of view, the CCSS initiative has  put the finger on things (i.e. curriculum topics) that are common across the states and also helps increase semantic interoperability across the stats. I came across an example of this at OER Commons. They use CCSS in their metadata to tag resources that originate from different states but that comply to certain given CCSS topics. See the example here: http://www.oercommons.org/browse/collection/common-core-reference-collection.

Another interesting commonality that I see with CCSS and OER in Europe is related to my previous work on finding OER that "travel well" (see for example here). Basically, in Europe, not all the OER are interesting for sharing across the borders, but only the ones that are useful to others (e.g. curriculum match). But how do we sort out the useful ones from the less useful ones? The CCSS work has figured that out in an interesting way. 

Now, what could we do in Europe? If you have ideas on that, I'll be interested in hearing :)

 

Network visualisations of LLP project organisations

This visualisation allows you to see how a number of European organisations (about one thousand) are connected to each other through the involvement in the LLP programmes between 2006-2009.

Below you can explore the data. The first image is a static image and it shows how the institutions are clustered. We can see that there is lots of small clusters and one major one.

 

In the live image below, you should be able to zoom into the image and explore the clusters (note, you need a mouse for that!).

Once the report becomes public, I will share more about it.  In case it does not work, here is the link to ManyEyes: http://www-958.ibm.com/software/data/cognos/manyeyes/visualizations/llp-projects-and-networks-connecti-2.

Reading habits (paper based and web) by Finnish youth

The PISA results have since 2000 highlighted how Finnish 15-year olds excel in math, science, reading, etc. This report gives an interesting look into Finnish youth's reading habits, it includes both digital and paper-based media. Interestingly, we again find out that computers and ICTs are not often used for learning purposes, at least not in a formal learning setting (i.e. school, homework).   

Some outcomes (hastily) translated :

Since 2000, Finnish young people are reading the most variety of literature among 26 participating countries. The "reading index" that tracks the variety of reading, e.g. magazines, comics, literature (fiction and non-fiction) and newspapers is statistically decreasing in all countries. Apart from fiction and non-fiction book, Finnish youth reads less printed material. It is likely that the printed material has been replaced by, among other things, online reading and communication. 

In general, the use of web-based material among Finnish youth is at about average among PISA countries. In schools, however, computers are not used as actively as at home. In Finland, students seek less information on the Internet to help with homework and they rarely discuss their homework assignments online. Instead, the use of computer at home is mainly for pleasure purposes in Finland.

Parviainen (2012) Suomalaisnuorten lukemisen ja verkon käytön monipuolisuus http://www.oph.fi/julkaisut/2012/suomalaisnuorten_lukemisen_ja_verkon_kayton_monipuolisuus

FP7 priorities for Creativity and Learning

Over the summer holidays, EU's new FP7 priorities for 2013 funding were communicated. Challenge 8. is related to learning, namely called "ICT for Creativity and Learning"

...Europe must also support national efforts to help students to learn better, teachers to teach better, and school systems to become more effective. This goal can be greatly advanced by learning systems that can adapt to effective use in a wide variety of diverse contexts.

Under 8.2 (technology enhanced  learning), they also mention Learning Analytics:

b) Learning analytics, educational data mining: tools and processes for collecting, storing, exploring and reasoning on large-scale educational data to better understand learners' knowledge, assess their progress and evaluate environments in which they learn. These tools and processes should aim at improving learning and teaching (including 21st century skills) for students and instructors.  

Learning analytics has now become such a hot topic. So, like all the others, I'm also studying the topic. Here is a link to my recent paper on learning analytics. I presented it this summer at “Open and Social Technologies for Networked Learning” in Tallinn.

Country monographs on synergies between teachers’ professional development and eTwinning

The eTwinning report Teachers’ professional development – An overview of current practice was published by European Schoolnet in December 2010, it aims at understanding how eTwinning and national and local teachers’ professional development schemes interact. The report focused on three case studies, namely on Estonia, Poland and Spain.

The report, called "Teachers’ Professional Development: an overview of current practice" can be downloaded in different languages as a pdf file here:  DE, EN, ES, FR, IT

Here is the reference in APA style: 
Vuorikari, R. (2010). eTwinning Report 2010: Teachers’ professional development: an overview of current practice. European Schoolnet. 

Contributing to the Polish Presidency on Mobility

The Polish Presidency of the Council of the European Union organised a conference on "Mobility as a tool to acquire and develop competences from childhood to seniority” in October 2011. I participated  in two workshops as an expert on virtual mobility for teachers. The proceedings of the conference include my short paper called Virtual mobility and teachers’ collaboration networks.

Different types of "Blended learning"

The term "blended learning" has become a lot used word in educational sciences, and in general in the filed of e-learning and technology enhanced learning (TEL). It's almost a "passe-partout", a term used to mean a little bit of everything.

I came across a paper that had identified 6 models or categories of blended learning. I'll copy them here and I look forward to reading the whole paper!  

Choose Your Blend*
In 2010, more than 4 million K-12 students participated in some sort of online learning (up from 45,000 in 2000). An Innosight Institute survey of schools that have adopted blended learning was able to identify six basic models.

1. Face-to-Face: Most material is taught in the traditional manner. The teacher uses online learning as a supplement or remediation.
2. Flex: This is online learning but in a school setting. Teachers provide support as needed, generally through tutoring, either one-on-one or in small groups.
3. Self-Blend: Students supplement traditional school by taking online classes from home. This model is used mostly to take AP and foreign language courses.
4. Rotation: In this model, used in Rocketship schools, students spend a scheduled portion of their day learning online. Essentially, they rotate from classroom to online computer lab and back.
5. Online Lab: Courses are conducted entirely online, including interaction with the teacher, but students do coursework in school (rather than at home). 
6. Online Driver: Schooling is online: Students take classes and work with teachers remotely.

*Source: Heather Staker et al., The Rise of K-12 Blended Learning, Innosight Institute, innosightinstitute.org

Study on contexts in tracking usage and attention metadata in multilingual Technology Enhanced Learning

Just submitted the final version of the paper to a workshop on Exploitation of Usage and Attention Metadata (EUAM 09). Here is a one-pager about it and the link to the paper.

Study on contexts in tracking usage and attention metadata in multilingual Technology Enhanced Learning

“Context” is widely accepted to be important for correctly interpreting user input and for improving predictive and possibly also diagnostic models. But what is context, and how can it be measured? By measuring we mean to operationalise the construct and data gathering to provide values for the desired variables.

In this study, we consider the intersection of the areas of digital learning resource repositories, digital libraries and social tagging systems where users from a variety of countries use technology enhanced learning (TEL) offerings in a variety of languages. We consider usage and attention metadata as an example of the wider notion of context adapting the definition of context as “any information that can be used to characterise the situation of entities” [Dey01]. We give an overview of dimensions of context that are relevant in TEL, specifically arguing that context comprises the usage situation and environment as well as persistent and transient properties of the user. Therefore, distinguishing between the macro-context and the micro-context of TEL is useful.

TEL and the analysis of the data it generates take place in different types of educational settings which we call the macro-context of TEL. We use the term micro-context to denote the context that is relevant for interpreting a specific user input and for designing adequate system responses and other output. The micro-context is subdivided into user models, material/environment models, interaction models, and background knowledge, showing that usage and attention metadata are of different types and play different roles for learning about context.

We then concentrate on teachers using learning-resource repositories as an important use-case example of TEL and focus on language and country as context variables. We describe different ways in which these variables are operationalised, and we outline ways in which TEL use such context information to improve the use and reuse of repositories by supporting users in a multilingual and multicultural context. A key theme of our article is the central role that social tagging can play in this process: on the one hand, tags describe usage, attention, and other aspects of context, on the other, they can help to exploit context data towards making repositories more useful, and thus enhance the reuse.

Riina Vuorikari 1,2, Bettina Berendt3
1 European Schoolnet, Brussels, Belgium,
2 OUNL, Heerlen, Netherlands,
3 KU Leuven, Belgium

Are tags from Mars and descriptors from Venus?

A study on the ecology of educational resource metadata.

I just finished a paper on the tag evaluations that we did in the MELT project. We had lots of fun with the name of the paper :) the main question being which one, tags or descriptors, should be from Venus...?

Anyway, we were able to show that not all the tags are as far from the Thesaurus descriptors as Mars is from Venus. We had different perspectives for evaluations: end-users, expert indexers and repository owners. For me the most interesting thing that came up was that 11% of end-user generated tags are actually terms that we can find in our multilingual Thesaurus! I assume teachers are "better taggers" than average, usually there is lots of talk about the gap between end-users' language and the one deployed by experts.

Abstract. pdf. In this study, over a period of six months, we gathered empirical data from more than 200 users on a learning resource portal with a social bookmarking and tagging feature. Our aim was to look at the tags from different stakeholders’ points of view; end-users, librarians/expert indexers and repository owners. We first look how users tag resources, and then conduct an evaluation with indexers to understand how they perceive the value of tags as descriptors. We then present a case study from a repository owner’s point of view. Lastly, we study users’ clickstream when searching resources. We find that, even though end-users and expert evaluators apply very different strategies when adding metadata, (end-users have a rather synthetic approach whereas expert indexers an analytical one) there is an overlap in the information in tags and the official descriptors, this overlap is even up to 51%, creating an ecology of metadata.

Keywords: Learning resource metadata, tags, folksonomy, clickstream,
thesaurus, evaluation.

How different is user behaviour on a portal from the ones who log-in to ones how do not?

I've recently done quite a few studies on users of learning resources portals, I've looked for example how do they tag resources in a multilingual context or how much use and reuse is there across the borders. In all cases the studies have concentrated on the small amount of the (minority) users who actually log in and had created Collections of resources: in Calibrate that was about 30% and in LeMill about 10%.

Now in MELT we've revised the logging scheme to collect the click-stream from users who don't log-in. We also have Google analytics, but I don't have those at hand right now. I looked at the data from last 3 months, from Aug 18 to Dec 18, and then only from the last month (Table 1).

What do users do on the portal?

The most popular activity on the portal is search, 64.29% of all actions on the portal are different types of searches. They result in "playing" the resources in 18.31% of all actions on the portal. 13.09% of all actions are contributing actions on the portal, this means adding a tag, bookmarking or rating it. The figure of contributing actions is actually a bit distorted, we count each tag, rating and bookmarking there. As each bookmark has average of 4.3 tags attached to it, it brings up the figure. Actually, the number of actions that contribute to "acting with an individual resource" is around 4.3% of all actions (i.e. add rate and bookmark). Other includes activities like view evaluation, view other users who have bookmarked the resources, etc.

Table 1


The downside here is not having the stats from Google analytics, so I cannot exclude our internal usage, which I know has been quite a lot, since we've been testing the portal internally. So the figures might be somewhat distorted...


What about users who log-in and the ones who don't?

About a month ago I invited some 260 teachers on the portal, so I was intrigued to see what had happened. 2 weeks ago I checked that 11% of these teachers had started their own account. But, it seems like much more have come about and cruised around the portal.

Table 2 presents the data from the last 4.5 months (Aug-December) where I have divided it in two slots: first months include pilot teachers and lots of testing, in the table it's erroneously called "First 2 months". The second slot covers the time from Nov 18 to Dec 18 when we invited the new teachers (Nov 18/19 in 4 different patches of invites). It is called "the 3rd month" in the tables (again, my mistake). Moreover, the top half of the table has data regarding users who log-in and the bottom with users who did not log in.

I have mostly the same attributes for both, how did they search; advanced, browsing by category and by tag cloud and how many resources they clicked on (play). The table also contains the number of sessions and number of actions. A session is one consecutive event when the user does something, it's logged. If left idel, the user is logged off in some time. An action is anything, a search, a click on a resource, on a tag, etc. Additionally, we have the contribution by logged-in users, these are tags, bookmarks and ratings.

Table 2


As you can see, most of the sessions (above 86%, the second last row) in both slots take place when users are not logged in. Actually, the percentage of sessions stays pretty regular in both slots. Moreover, regarding the actions, we can see that during first months they mostly (70%) came from non-logged in users. However, when we invited the new teachers, we see 10% increase in actions by logged in users (from 30% to 40%). That's positive, as it shows that some of the invited teachers were motivated to contribute.

There is actually quite big differences in what do these two groups of users do when they are on the portal. Where logged-in users spend about 1/3 of their actions in searching, non logged-in users spent about 2/3 of their actions in searching. Chart 1 shows this clearly, however, I must say that most likely the disparity between the number of searches and plays by non-logged in users in the first months are due to our internal testing. If you compare that to non logged-in users in the 3rd month, you see that there is already less searches and more plays.

There has been a difference since the new comers (3rd month): within the logged in users, the number of searches executed has gone down (10%), whereas the number of plays has gone up (from 17% to 23%). Among non-logged in users there is the same 10% drop in searches, but plays have gone up by 10% (from 16% to 27%)! That shows that the new comers were interested in seeing what kind of resources were out there in the portal.

Chart 1 can maybe be used to illustrate

Chart 1


One difference can be observed in how differently these two groups seem to search: with logged-in users the advanced search seems to be the more popular way to search (more than 50% of searches are advanced), whereas with the users who are not logged-in browsing (both by category and tag cloud) is more popular. During first months 54% of searches were browsing, which went slightly up (to 56%) during the 3rd month. The tag cloud was the biggest winner in both groups (logged-in and not) at the cost of advanced search. I assume the difference is due to the fact that people who are not logged in are interested in seeing what is out there and browse around to discover learning material.

In any case, if we look at the figures of non logged-in users within the 3rd month, it's intriguing how equally the searches are distributed across these different ways of searching. We'll keep an eye on this in the future (e.g. when we know that most non-logged clicks come from us testing the portal).

Consumers and contributors

In Table 3, where I again have data for users logged-in and not, and by periods of first months and the 3rd month, we see that when users are logged in, they do things differently. First of all, the logged-in users spare much smaller percentage of their actions in searching (average 33% to 75%), however, bizarrely, they still seem to "play" about the same amount of resources (around 20%).

Table 3


Within the 3rd month we see the percentage of plays growing. We can assume that the logs from the first months period are most likely influenced due to our internal testing of the portal, which often times includes making searches. We see that the percentage of plays go up for the non logged-in users within the last month (from 16% to 27%), which, I assume shows a more normal user-behaviour than what could be observed before.

This still indicates that there is lots of inefficiency when non-logged in users search: on the average during the 3rd month, for those logged-in, one "play" was a result of 1.2 searches, whereas with those not logged-in, one "play" was a result of 2.6 searches - lot of time lost in searching. From Table 2 we can observe that there was more browsing (non logged-in users 1 month), I wonder if that was the reason? Have to keep on eye at that one!

Most interestingly, 40% of actions by logged in users contribute are the ones that contribute something to the portal, they rate, tag and bookmark. Folks who do not log-in are consumers: they only search, click and leave (- which is fine too).

So all in all, if we look at all the actions on the portal, the contributing actions by logged in users amount to about 17%. Too bad that this figure did not go up in the 3rd month like some others did. Anyhow, it seems to follow the power-law of distribution (20-80), where small amount of people contribute a lot so that other people can take advantage of this work, also know as participation inequality by J.Nilsen (2006).



J.Nilsen (2006) Participation inequality: Encouraging More Users to contribute

Learning resources landscape

Learning resources come in all colours and shapes, that is for sure. They also come from all kinds of different places; repositories, portals, the web.... For a recent presentation and paper, I created this diagram to better depict the learning resources landscape. As I later had to remove this part from the paper to save place, I post it here.


Teachers use a plethora of ways to discover educational content online. Harvey et al. (2006) report on search strategies of 4500 US faculty members where Google-like searches are by far the most prominent (81%), second most important being own personal Collections of resources and also “portals” that provide links to disciplinary topics (55%). In our user group comprised of 45 language and science teachers in K-12 education, such diversity of strategies was also discovered: one third use national and regional educational repositories as their primary source of educational content, 28% use search engines, 21% said they create their own content, 7% use content from schoolbook publishers and 12% reported all of the above (Vuorikari, 2008a).

These search strategies also give an indication of the different types of resources that teachers use. Figure 1 illustrates a number of different sources of content that teachers use. First of all, on the horizontal axis we distinguish between platforms that have institutional support and the ones that are rather teachers’ community driven sources. On the vertical axis we distinguish between teacher-generated content and “other sources”. The latter encompasses a large number of providers from educational portals and repositories, schoolbook publishers to educational and non-educational sites created by a number of private and public stakeholders. This “other sources” category is essentially as large as a teacher’s pedagogical imagination is in taking advantage of the resources on the Internet.

This diagram allows us to draw a landscape for educational resources. In the upper left corner of the diagram, there are examples of institutional Learning Object Repositories (LOR), such as the ones managed by Educational Authorities (e.g. Learning Resource Exchange for schools and members of EdReNe) and other repositories that make educational content available. On the lower left corner we place initiatives like MIT OCW which is an institutional repository that makes available teacher-generated content. The lower right corner represents teacher-generated content in a community-driven environment (e.g., LeMill), whereas the upper right hand corner represents content that is found on the Internet from various sources and saved in community-driven environments like delicious.com. None of these boundaries are fixed and there are many in-between-models (e.g., LOR with both user-generated content and institutional ones). Our data sets for this study, which are presented in Table 1, cover a wide area of Figure 1. For learning resources we use Wiley’s (2002) definition of learning object as “any digital resources that can be reused to support learning”, as they vary greatly in granularity and other qualities.

Our evidence finding focuses on teachers in K-12 education in a European multilingual context. In the Europe Union area, where 497 million people (Eurostats) live from diverse ethnic, cultural and linguistic backgrounds, multilinguality has an important role (Council of Europe, 2007). There are 23 EU official languages, 3 alphabets, and some 60 other languages are part of the EU heritage and spoken in specific regions or by specific groups (COM, 2008). Multilinguality can be defined as a situation where several languages are spoken within a certain geographical area, as well as the ability of a person to master multiple languages. 56% of EU citizens say that they are able to hold a conversation in one language apart from their mother tongue, and 28% in at least two languages. English remains the most widely spoken foreign language throughout Europe (38%), second and third place is French (14%) and German (14%), whereas 6% have foreign language expertise in Spanish and Russian respectively. Over two-third say that they language lessons at school was the way they have learned foreign languages (COM, 2006).

..................

Harley, D., Henke, J., Lawrence, S., Miller, I., Perciali, I., and Nasatir, D. (2006). Use and Users of Digital Resources: A Focus on Undergraduate Education in the Humanities and Social Sciences. Available from
http://cshe.berkeley.edu/research/digitalresourcestudy/report/digitalresourcestudy_final_report.pdf

Vuorikari, R. (2008a). A case study on teachers' use of social tagging tools to create collections of resources - and how to consolidate them. In Wild, F., Kalz, M., Palmer, M (Eds) Proceedings of the First International Workshop on Mashup Personal Learning Environments. Available from http://sunsite.informatik.rwth-aachen.de/Publications/CEUR-WS/Vol-388/vuorikari.pdf

Wiley, D. (2002). The Instructional Use of Learning Objects. Online at: http://reusability.org/read

COM(2006). Europeans and their languages. Special Eurobarometer, European Commission.

COM(2008). 566 final. Multilingualism: an asset for Europe and a shared commitment, European Commission.

Council of Europe (2007). Un cardre Européen commun de référence pour les langues : apprendre, enseigner, évaluer. Division des Politiques Linguistiques, Strasbourg: France.

Teachers as Netpromotors of digital content

I made a survey with 28 teachers from different European countries on multilingual learning resources. You can find those 28 resources from this list. Our portal has a lot of multilingual resources that come from a variety of Ministries of Education in Europe.

But - we do not know for sure whether teachers find resources useful that come from different countries than they do, and that are in different languages than they speak. Hence my little survey. You can read more details here.

We only considered responses from teachers who came from different countries than the 18 resources did that we had in our survey. Quick round of results:

• 43% of respondents found resources, which came from a different country than they did, of use for preparation purposes.
  
  
• 41% of respondents found resources, which came from a different country than they did, of use for teaching purposes.
  
  
• 65% of respondents said that they would share these resources, or parts of them, with their colleagues and friends.
  
  
• Even 35% of respondents, who said they did not have expertise in the given subject area, thought that they would share the resource with their colleagues
  

These were the results on a scale 1-5 (n=254)







It made me think that:

a) If teachers use multilingual or foreign language resources, they most likely use them both for preparatory purposes and for teaching purposes. We do not know, though, whether they would use the resource in their teaching themselves or let pupils interact with this resource.

b) Teachers are good filters. More teachers said that they would be willing to share resources with their colleagues than actually use them themselves. It might be that this happens with a resource, which they think is interesting, but does not match to their curriculum goals for the year. They might say, "Hey, my colleague would love this, I'll send it to her!" This is the basic mechanism of viral marketing, how can we leverage this on a learning portal?

c) "Would you like to share it with your colleagues" is one of the key questions when studying customer satisfaction and loyalty, topic that we in learning repositories often neglect. If teachers are happy users, or if teachers find good material on the portal, they can become promoters of those resources. This might be very important especially when we deal with resources that are in multiple languages, because sometimes it is hard to discovery those resources.

If we take the teachers in the survey, we could calculate the Net Promoter Score by subtracting the % Detractors (e.g. the ones in my survey who rated this 1 or 2 on the scale 1-5) from the % Promoters (e.g. the ones in my survey who rated this 4-5).

Take the case for sharing: it would be 65% -22% =43%. That is a pretty good net promoter score, most companies have it around 5 to 10%, and it is very unusual to have it above 50%.

This can indicate that teachers are willing to put their credibility on the line by recommending a resource that comes from a different country than they do to a friend!

Now, I just have to think of the best way to do this ;)

A draft idea for a paper: A case study on teachers' use of social tagging tools to create collections of resources - and how to consolidate them?

UPDATE: the submitted paper, comments welcome!

This paper explores how a group of pilot teachers (16) create collections of digital learning resources using tagging tools. We study two different tools: an educational portal (MELT) and del.icio.us. We first look at the characteristics of these collections (number of resources, languages of resources, number of tags used, etc), and then propose a way to display the resources and tags from del.icio.us on the learning portal (MELT) using Attention Profing Markup Language (APML). This allows a higher level of integration between a learning portal and an external social tagging service like del.icio.us, and thus enhances the wider variety of digital learning resources to be discovered.

Method

We selected 16 pilot teachers to be subjects of this study from the MELT project. These teachers have both an account on the MELT portal and on the delicious bookmarking service. These teachers are primary and secondary teachers in science, language learning and ICTs in Finland, Estonia, Hungary and Belgium. 7 of them are females and 10 males. One participant is under 30 years old, 8 are under 40 years, 5 under 50 years, 3 under 60 years old.

They have been part of the MELT project since Summer 2007, when they were first introduced to delicious during a summer school. In March 2008 they were also invited to create a profile on the MELT portal, where they were able to access multilingual learning resources for different topical areas.

From the MELT portal we know the detailed profiles of these teachers: their names, topics they teach, country where they teach and languages they speak. Moreover, we have information regarding the learning resources that they have bookmarked using the portal. This includes the information about the resource itself and the tags applied. We additionally have asked for their delicious username to be part of this small study.

From delicious, using the html service, we were able to download the 100 last bookmarks and tags that these teachers had posted on delicious. We also took all the data regarding the tags and people these users had in their network. Lastly, we recorded the number of posts each teacher had on their account.

We collected the following data for our selected 16 users:

















Additionally, the delicious data contained the following information regarding the networks. Two people had chosen to keep their networks private:

• Number of distinct people in the networks: 104
• Number of people in the networks: 270
  

Results
Discussion

References

My PhD dissertation, a new take on defining it

How Social Information Retrieval (SIR) can be used to enhance the discovery of large-scale collections of multilingual digital learning resources

The PhD dissertation deals with the discovery of digital learning resources and flexible access to large-scale collections of multilingual digital educational content. The thesis attempts to prove that we can use information deduced from social bookmarks and tags to better select suitable learning resources to users, who come from a variety of countries, speak different languages and whose educational context vary.

The first step towards proving this thesis statement is to better understand whether there are digital learning resources that afford a good usage also in a context other than the one they were originally intended for. We call this type of educational content “travel well” resources because they cross borders easily; those borders can be national, linguistic, educational or socio-cultural.

Upon better understanding of how users agree on “travel well” resources, we can explore the ways to identify them. Two different sources of information can be used for this purpose: looking at the properties of these resources (e.g. Learning Object Metadata), as well as attentional metadata collected from users interactions with the resources on the portal (Najjar, 2006). Our interest is in attentional metadata that we can gather from users' social bookmarks, from their personal collections of educational resources that they create, and from tags that they add to these resources (Vuorikari and Van Assche, 2007, Vuorikari et Poldoja, submitted).

One major contribution of this thesis is the better understanding of how users (e.g. teachers) tag educational resources in a multilingual environment and whether a multilingual context has any implication on the tagging behaviour (e.g. in what languages do users tag) (Vuorikari, et al., submitted). Secondly, we are interested in the value that a multilingual tagging system provides; on the one hand, we want to know what kind of information multilingual tags can yield about the resources and their possible use in different contexts. On the other hand, we are interested in their value for resource discovery and as a navigational tool to allow cross-language and country exploration of new resources in multiple languages.

Better understanding of tagging behaviour and creation of personal collections of learning resources will help us to create metrics that can be used to calculate “travel well” value of resource. Our hypothesis is that we can define a “travel well” resource when we use information deduced from social bookmarks, users’ personal collections of educational resources, and from tags that they have added to these resources. We will be watching the following variables:

• The resource is from a different country than the user is
• The resource is in a different language than user’s mother tongue,
• The resource has tags in different language(s) than that of the item language

The metrics used to calculate the “travel well” value of digital learning resources would be used to create a TravelRank algorithm that allows identifying learning resources that “travel well”, and which can be used to compliment the LearnRank algorithm (Duval, 2006). Identifying these resources from large collections of digital learning content from different countries and in different languages has a potential to allow a more flexible access to large-scale collections of resources. The final part of the thesis is to validate this claim and to evaluate its usefulness for a large audience of users from different countries.
References:

Najjar J., Wolpers M., and Duval E. Towards Effective Usage-Based Learning Applications: Track and Learn from User Experience(s). IEEE International Conference on Advanced Learning Technologies, (2006) (ICALT '06).

Duval E. LearnRank: Towards a real quality measure for Learning. In U. Ehlers & J.M. Pawlowski (eds.), European Handbook for Quality and Standardization in E-Learning. Springer (2006), 379-384.

other non-published, submitted papers at my site:
http://www.cs.kuleuven.be/~riina/

Hole in the wall-experiment and eTwinning

The eTwinning conference is just kicked-off by the Commissionaire Figel here in Bucarest.

I'm very exited to hear the keynote speaker Dr. Sugata Mitra who will speak in a few hours. He is the one who made the most exiting (OK, that's my idea of it) experiment with kids and computers, namely, made a "hole" in a wall at the slum in Delhi to allow unprivileged kids to access computers and the Internet. He came up with something that he calls "Minimal Invasive Education", which allows kids to learn without formal instruction.

So, more about him later, I already had a chance to have a beer with him last night, but I'm really looking forward to some more question time with him. I saw that Downes talked very highly about one of his previous speeches.

Tomorrow I will have 3 workshops in a row to talk about social bookmarking and social tagging with teachers.

´´´´´´´´´´´´´´´´´´´´´´
Update: I have blogged about the speech at FlossePosse the audio of his excellent (!!) keynote is available there too. Dr. Mitra is so inspiring that you just want to leave everything that you are doing now and start working for him!