Learning object repositories are a basic piece of virtual learning environments used for content management. Nevertheless, learning objects have special characteristics that make traditional solutions for content management ine ective. In particular, browsing and searching for learning objects cannot be based on the typical authoritative metadata used for describing content, such as author, title or publication date, among others. We propose to build a social layer on top of a learning object repository, providing nal users with additional services for describing, rating and curating learning objects from a teaching perspective. All these interactions among users, services and resources can be captured and further analyzed, so both browsing and searching can be personalized according to user pro le and the educational context, helping users to nd the most valuable resources for their learning process. In this paper we propose to use reputation schemes and collaborative ltering techniques for improving the user interface of a DSpace based learning object repository.
Since the introduction of Information and Communication Technologies in the
eld of education, almost every educational institution has adopted an e-learning
solution, although with di erent approaches. Virtual learning environments are
one of the most common tools used to implement an e-learning platform,
typically including a module for content management. Usually, content is understood
as complete courses, but the reality is that learning resources can be very di
erent according to their type, format and granularity [
In order to encourage its usage, the most important issue for a learning
object repository integrated in a virtual learning environment is being able to
build a true social learning network around it, promoting the creation, sharing
and reuse of learning resources among the members of the learning community,
mainly both learners and teachers. This can be only done if the learning object
repository, regardless of its technology, provides its users with a virtual learning
environment and a true learning experience. These learning experiences can be
used in order to get knowledge about the needs of learners, their preferences
in the use of learning objects and to identify learning objects in the repository
that are less attractive to them. All this information can be used in order to
improve the repository by updating the descriptions of learning objects
according to what we learnt (making the appropriate learning object easier to nd
through additional metadata [
This paper is organized as follows: Section 2 describes the use of learning object repositories as an important tool for supporting both learners and teachers. Section 3 describes the functionalities of an ideal learning object repository which uses a social layer on top of it to improve searching and browsing capabilities. Finally, Section 4 outlines the main advantages of the proposed system, the implementation issues and the current and future research topics of this project. 2
Learning objects are stored in learning object repositories, which can be
considered a speci c kind of content management system for educational resources but
much more versatile [
Obviously, digital repositories are a way to organize learning objects (and
their parts that can be processed separately) in collections, although there are
several speci c issues that must be rstly addressed. For example, an exercise
(which is basically a text de ning a problem and, optionally, its solution, another
text which may include references to the use of software or tables with data, for
instance) is a typical learning object. But, di erently to classical items in a
collection of a digital repository, exercises may have neither a title nor even an author,
the two main elds used for nding a book. Other typical learning objects can be
data sets, mathematical proofs, equations, simulations, and so. Usually, learners
search through these kinds of resources not by title or author, but by keyword or,
even better, using a hierarchical taxonomy specially designed. Therefore, it
becomes necessary to rethink the traditional way of describing learning resources,
using criteria related to the learning process but maintaining a minimum
description for archiving purposes. Learning object repositories should be designed
for nal users, that is, learners and teachers [
DSpace1 is an open source platform developed by MIT and Hewlett-Packard
in 2002 for creating digital repositories, as initially outlined in [
DSpace organizes resources in a hierarchical structure based on communities (and, recursively, subcommunities) and, nally, collections. These contain the items (i.e. the resources) which are described using a metadata pro le, usually non quali ed Dublin Core. With the default DSpace user interface, users can search and browse by author, title, publication date and keywords, as well as through the hierarchical structure of communities and collections. But, according to their nature, some learning objects may have or not title, author, creation date, etc., so they cannot be accessed by classical retrieval mechanisms used in digital libraries or repositories. In fact, DSpace has to be customized to change the basic elds used for searching and browsing, as well as all the work ows related to the process of adding new resources to the repository. From a teaching perspective, learners should retrieve resources not from a list of search results but within a speci c educational context. DSpace (as any other large collection of resources) su ers from the "Google e ect", that is, a search based on a simple keyword such as "Statistics" may return thousands of resources, which is not a good result from a teaching perspective2. It is well known that most users click on the rst three results (up to 62:53%, see 3), so it is very important to determine a proper ranking of learning resources, providing learners with the most appropriate content according to their pro le and context. 1 http://www.dspace.org/ 2 See http://dspace-dev.dsi.uminho.pt as an example of recommender system. 3 http://www.webuildpages.com/jim/click-rate-for-top-10-search-results/
As described in [
For each resource, we know the following: the number of times it has been accessed, the number of times it has been downloaded, the number of comments placed on it, the number and average of ratings, the number of times it has been favorited, the number of times it has been shared and the number of users subscribed to it.
On the other hand, for each user, we know the following according to its role (manager, teacher, learner or anonymous visitor). For teachers, we are interested in knowing the list of subjects she is in charge of, as her activity on resources related to those subjects will have an important weight. For learners, we know the list of subjects she is/has been enrolled in, the languages she is competent (and her preferred one), previous and current professional experience (if available) as well as all the repository services used in a period of time. Although it is out of the scope of this paper, all this information could be available by means of speci cations like IMS Learner Information Pro le, in order to promote interoperability with other e-learning systems.
Once the layer of services is available to all users and the proposed system has been gathering interaction data during an adequate period of time (i.e. an academic semester), it is possible to use such data for computing the heuristics that will be used by the reputation scheme to rank both users and resources, providing useful information about: { The most popular resource: popularity can be both rank based or activity based, or any combination of both. Not only the most popular resources are interesting, the worst ranked ones need to be analyzed by teachers in order to detect potential problems. On the other hand, it might be useful to identify 4 http://nsdlnetwork.org/stemexchange/paradata unused resources. This may lead to detecting wrong metadata descriptions which may cause a resource to be non ndable. { The most active users: analogously, users can be ranked according to their level of activity. In a virtual learning environment, where peer-to-peer learning is promoted as part of the underlying pedagogical model, learners can build a reputation by creating, sharing and answering other peers' questions, all these actions rated by the other users. On the other hand, teachers can act as peer experts in one or more subjects. { The most common tags used for describing resources: although resources have been already described by both librarians and teachers according to several taxonomies (domain speci c keywords, resource type, etc.), users can add their own tags for describing resources as in delicious. These tags can be analyzed and further incorporated into metadata as new keywords, for example. { Relationships between resources: like Amazon, collaborative ltering can be used to detect which resources can be potentially more interesting according to the implicit navigational behavior of users with similar pro les. This information will be used to determine the most adequate resources related to a given one. 3.2
When a user reaches the DSpace repository, there is usually a list of the most recently added resources. We propose to replace the items in this list with the most adequate ones according to her pro le (i.e. the most relevant ones with respect to the subjects she is enrolled in). A second list with the resources previously used and/or the most popular is also desirable.
On the other hand, when the same user performs a search and obtains a list of results, we propose to modify two di erent aspects. Firstly, only a few results are shown (i.e. ve to ten), the most important ones according to the underlying reputation scheme which uses both user pro le and the available paradata. Secondly, the resources most related to these search results are also shown, promoting a browsing strategy through a local network of resources, updating it according to user's navigational behavior. From a teacher's perspective, this is better than providing learners with hundreds of resources without any contextual support. 4
Learning object repositories are a very important piece of any e-learning platform, although they are currently being underused by nal users, specially learners. In order to promote repository usage, we propose to add a layer of web 2.0 services on top of the repository, bridging resources to the learning process. The interactions between users, services and resources generate a lot of paradata that may be captured and further analyzed for personalization purposes. Both searching and browsing can be adapted to user's pro le, improving her experience when using the repository.
Nevertheless, there are some well known issues that must be faced regarding to the way learning resources must be described. It is not easy to establish a taxonomy for describing all the resources in an institutional repository. Due to the variety of contents, such a taxonomy would have probably too many levels, making it too complex for most users. On the other hand, learning activities that promote the use of the repository must be also designed, in order to encourage learners to adopt a more active role in their learning process.
This work is part of a three year research project (2011-2013) on analyzing usage of repositories and social networks in virtual learning environments. Currently now we are modifying our DSpace institutional repository5 in order to include the layer of services as well as the mechanisms for paradata gathering. We expect that in Fall 2011 we will be able to deploy a rst version of the repository and start capturing users' interactions. Current and future research lines around this topic include the creation of reputation schemes for both users and resources, using explicit and implicit paradata. Making repository services available from social networks (where students are) is also an interesting possibility. Acknowledgments. This work has been partially supported by Spanish Government funded project MAVSEL ref. TIN2010-21715-C02-02 and by the IN3. 5 http://openaccess.uoc.edu/webapps/o2/?locale=en