Wednesday, April 02, 2008

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.

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: