=Paper=
{{Paper
|id=None
|storemode=property
|title=Improving Searching and Browsing Capabilities of Learning Object Repositories
|pdfUrl=https://ceur-ws.org/Vol-732/paper8.pdf
|volume=Vol-732
}}
==Improving Searching and Browsing Capabilities of Learning Object Repositories==
https://ceur-ws.org/Vol-732/paper8.pdf
Improving Searching and Browsing Capabilities
of Learning Object Repositories
Julià Minguillón, M. Elena Rodrı́guez, and Jordi Conesa
Estudis d’Informàtica, Multimèdia i Telecomunicació,
Universitat Oberta de Catalunya,
Rambla Poblenou 156, Barcelona, Spain
{jminguillona,mrodriguezgo,jconesac}@uoc.edu
http://www.uoc.edu
Abstract. Learning object repositories are a basic piece of virtual learn-
ing environments used for content management. Nevertheless, learning
objects have special characteristics that make traditional solutions for
content management ineffective. In particular, browsing and searching
for learning objects cannot be based on the typical authoritative meta-
data used for describing content, such as author, title or publication
date, among others. We propose to build a social layer on top of a learn-
ing object repository, providing final users with additional services for
describing, rating and curating learning objects from a teaching perspec-
tive. 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 profile and the educational context, help-
ing users to find the most valuable resources for their learning process.
In this paper we propose to use reputation schemes and collaborative
filtering techniques for improving the user interface of a DSpace based
learning object repository.
Keywords: Learning Object Repositories, Browsing, Searching, Rec-
ommendation Systems, Collaborative Filtering, DSpace, Metadata, Pa-
radata
1 Introduction
Since the introduction of Information and Communication Technologies in the
field of education, almost every educational institution has adopted an e-learning
solution, although with different approaches. Virtual learning environments are
one of the most common tools used to implement an e-learning platform, typi-
cally including a module for content management. Usually, content is understood
as complete courses, but the reality is that learning resources can be very differ-
ent according to their type, format and granularity [2].
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 accord-
ing to what we learnt (making the appropriate learning object easier to find
through additional metadata [3]), improving the services of the repository itself
(making it easier to use) and improving the quality of the institutional reposi-
tory by deleting or improving the irrelevant resources and promoting the more
useful ones. The learning object repository is an important element of the vir-
tual learning environment but it is not the only one and, of course, learners may
search for resources outside the institutional ”walled garden”, mainly through
Google and other search engines. Nevertheless, the main problem for learners is
to filter among the thousands of results returned by a general purpose search
engine. We encourage our learners to use the institutional repository as part of
their learning process.
This paper is organized as follows: Section 2 describes the use of learning
object repositories as an important tool for supporting both learners and teach-
ers. 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 capabili-
ties. 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 Object Repositories
Learning objects are stored in learning object repositories, which can be consid-
ered a specific kind of content management system for educational resources but
much more versatile [7]. Although, as stated before, traditional CMS tools can be
used to store, describe and share learning objects (such as Drupal or OpenCMS,
among many other open source software tools), these tools are usually oriented
towards web content. According to [5], repositories are differentiated from other
digital collections because the content is deposited in the repository together
with its metadata; and such content is accessible through a basic set of services
(i.e. put, get, search, etc.). Depending on the specific needs of the community
using the repository, this will provide additional tailored services, but all repos-
itories should at least provide two basic ones: content preservation and content
reusing [1].
Obviously, digital repositories are a way to organize learning objects (and
their parts that can be processed separately) in collections, although there are
several specific issues that must be firstly addressed. For example, an exercise
(which is basically a text defining 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, differently to classical items in a collec-
tion of a digital repository, exercises may have neither a title nor even an author,
the two main fields used for finding 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 be-
comes necessary to rethink the traditional way of describing learning resources,
using criteria related to the learning process but maintaining a minimum de-
scription for archiving purposes. Learning object repositories should be designed
for final users, that is, learners and teachers [4], promoting content reutiliza-
tion rather than preservation. Both concepts (preservation and reutilization)
are somehow contradictory (institutional, top-down vs social, bottom-up) but a
tradeoff can be achieved by combining digital repositories with web 2.0 services.
2.1 DSpace as a learning object repository
DSpace1 is an open source platform developed by MIT and Hewlett-Packard
in 2002 for creating digital repositories, as initially outlined in [8]. DSpace pre-
serves and enables easy and open access to all types of digital content including
text, images, moving images, mpegs and data sets. It is used by more than one
thousand institutions and it has a large community of developers, becoming a
de facto standard for building open repositories.
DSpace organizes resources in a hierarchical structure based on communities
(and, recursively, subcommunities) and, finally, collections. These contain the
items (i.e. the resources) which are described using a metadata profile, usually
non qualified 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 fields used for searching and browsing, as well as all the workflows
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 specific educational context. DSpace (as any other large collection
of resources) suffers from the ”Google effect”, 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 first 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 profile 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/
3 Improving browsing and searching
As described in [6], our goal is to provide a layer of web 2.0 services on top
of each learning object stored in the repository. These services include adding
comments to a learning object, rating it, starring it as a favorite resource, tagging
it, sharing it through different social networks and, finally, subscribing to it in
order to be aware of all the activity generated around such learning object. All
the information generated during the interaction between users, services and
resources is stored in each learning object and/or user profile, if available. All
this information is known as paradata4 , and it can be used for our adaptation
purposes in two different ways: supporting users when browsing and searching
(i.e. filtering before finding) and sorting results (i.e. filtering after finding). In
our case, paradata is composed of 5-tuples {U, S, R, X, T } meaning that user U
used service S on resource R with result X in moment T . By means of data
mining techniques, these data can be analyzed and reintroduced into the system
to enhance browsing and searching capabilities.
3.1 Possible uses of paradata
For each resource, we know the following: the number of times it has been ac-
cessed, 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 avail-
able) 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 specifications like IMS Learner Information Profile, 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 findable.
– The most active users: analogously, users can be ranked according to their
level of activity. In a virtual learning environment, where peer-to-peer learn-
ing 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 specific 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 filtering can be
used to detect which resources can be potentially more interesting accord-
ing to the implicit navigational behavior of users with similar profiles. This
information will be used to determine the most adequate resources related
to a given one.
3.2 Improving the user experience
When a user reaches the DSpace repository, there is usually a list of the most re-
cently added resources. We propose to replace the items in this list with the most
adequate ones according to her profile (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 different aspects. Firstly, only a few results
are shown (i.e. five to ten), the most important ones according to the underly-
ing reputation scheme which uses both user profile and the available paradata.
Secondly, the resources most related to these search results are also shown, pro-
moting 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 Discussion
Learning object repositories are a very important piece of any e-learning plat-
form, although they are currently being underused by final users, specially learn-
ers. 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 search-
ing and browsing can be adapted to user’s profile, 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. Cur-
rently 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 first version of the repos-
itory 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 avail-
able from social networks (where students are) is also an interesting possibility.
Acknowledgments. This work has been partially supported by Spanish Gov-
ernment funded project MAVSEL ref. TIN2010-21715-C02-02 and by the IN3.
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