Measures for cross-border actions with Tags and Resources

If I break down the triple of {user, tag(s), resource} I can study the three things separately

• User - resource
• User - tags
  


• Resource - users
• Resource - tags
  


• Tag - resource
• Tag - users
  

And as I am especially interested in the cross-border actions, I would study the cases where:

User _country ≠ Resource _country

In this case I am interested in studying users collections of bookmarked resources, especially establishing the facts based on which country the resources are originated from. Using the cross-border metrics I can take a snapshot of the resources and calculate a cross-border resources value for the use.

• E.g. User _Finland has bookmarked Resource1 _Poland , Resource2 _Spain and Resource3_Finland
  
  
• This would make a User _Finland to have a resource profile Poland 33%, Spain 33% and Finland 33%
  
  
• In this case, as the user is from Finland, the cross-border profile would be 66% which would most likely have a value of .66, if we imagine that the cross-border value is between 0 and 1.

So what, you say. It makes a difference, I say.

• This allows me to categorise this user into cross-border user of resources. I assume
  that users have differences in their inclination of using resources that come from different countries, some use them a lot others do not want to bother with them.
  
  
• So this metric allows me to study who does what and thus better understand our user-base.
  
  
• On the long run this of course will make it easier to recommend resources to users, as we
  already know that in their profile it shows that they are inclined to use cross-border resources.
  

Resource _country≠ User _country

This allows to me to look at the thing from a different point of view. Here, I am interested in establishing a profile for a resource. It appears that some resources are used a lot by people from different countries, whereas others are used predominantly by users from the same country than the resource itself is from.

• E.g. Resource _Finland has been bookmarked by User1 _Poland , User2 _Spain and User3Finland

• This makes the Resource_Finland to have a profile Poland 33%, Spain 33% and Finland 33%.
  
  
• In this case, as the resources is from Finland, the cross-border profile would be 66% of users, which would most likely have a value of .66, if we imagine that the cross-border value is between 0 and 1.

So what, you ask again. I think that it's cool, because then I can quickly and in an automated way calculate which of my resources have a high potent to cross borders easily. First of all, this will help me study whether there are some characteristics that make these resources to cross-borders.

Second, we can use this information to make filter out the resources that we think cross borders easily. This could be cool for example on our portal, we could flag out these resources for users, and furthermore, we could give these resources a priority when other repositories are harvesting or searching us in a federated manner.

Resource _country Tag_language
It'll also be interesting to create profiles for resources based on tags in different languages. For tag, we do not trace the country of origin, rather just the language. So in this case I'm interested in looking at resource profile on tags.

• E.g. Resource _Finland has been added a Tag1 _Polish, Tag2 _Spanish and Tag3 _Finnish
  

• This makes the Resource_Finland to have a tag profile Polish 33%, Spanish 33% and Finnish 33%.
  
  
• In this case, as the resources is from Finland, the cross-border tag profile would be 66% of users, which would most likely have a value of .66, as above.

This is also an indication that the resource has a potent to cross borders. Tags in different language might yield some interesting information on how this learning resource could be used in a new context. In this example the resource was created in Finland, so one could assume that it has some underlying ingredients that make it suitable for Finnish curriculum. On the other hand, the fact that users have added tags in Polish and Spanish too might indicate that this resource is also useful for teachers in those countries.

Here an interesting case seem to emerge for topics like Language learning, say, English as Second Language (ESL). Language learning and teaching resources seem to be easily reusable in another language context. Interestingly, though, we've seen that in these cases teachers tend to tag them in the language in question.
E.g. User _Finland has added a Tag _English for ESL Resource _Poland

Tag _language Resource _country

We can also look at the things from tags perspective.

• E.g. Tag _Finnish has been added to Resource1_Poland, Resource2 _Spain and Resource3 _Finland
  

• This makes the Tag_Finnish to have a resource profile Polish 33%, Spanish 33% and Finnish 33%.
  
  
• In this case, as the resources is from Finland, the cross-border tag profile would be 66% of users, which would most likely have a value of .66, as above

This allows us to observe cases where a tag is related to learning resources that most likely share some thematic resemblance. It could be for example Science resources from different countries that Finnish teachers have collected. In this case we also can find evidence that these resources were adaptable to Finnish curriculum despite the fact that they come from other countries.

Tag _language ≠ User _country

On the other hand, we also find tags that have been used by users from different countries. These are the tags that we have previously identified as "travel well" tags. They have some interesting properties that make them easily understandable without translations, e.g. names (people, country, place), acronyms, common terms (web2.0).

By looking at the connection between Tag _language and User _country we can possibly identify such tags. The other common case for this seems to be that these people have tagged the resource in English. In any case, if many people have done that, we can identify these terms and manually analyse them. The hypothesis is that they either are "travel well" tags or then they are some super popular tags that could also count high on tag non-obviousness metric by Farooq et l (2007).

User _country - Tag _language
Lastly, just to enumerate the cases, we also have the relation User _country and Tag _language. This can be used to study user's personal tagging behaviour. In the previous study in Calibrate we found that on average users tag in their mother tongue and in English (75% to 25%). It seems though that things look different in MELT, where teachers are tagging more in English.

We are not sure whether these are personal preferences or the influence of social awareness, as in MELT tags are made readily available to others through a tag cloud, whereas in Calibrate they were only used for personal knowledge management reasons.

In any case, this relation allows us to measure individual differences between users and thus understand our user-base and possible user scenarios better.


_{What next? I will make a case study to apply these measures to MELT tags that we've got in the system so far}

Dataset:

• Learning resources: 199
  
• Users: 40 (From Fi, Hu, Et, Be, At, It)
  
• Tags:
  
• 572 distinct,
  
• 969 applied tags
• 75% of tags were used only once
  
• 25% of tags were used more than once
•