Using Information Visualization for Accessing Learning Object Repositories


Joris Klerkx, Erik Duval and Michael Meire
Computer Science Department, K.U.Leuven
Celestijnenlaan 200 A, B-3001 Leuven, Belgium
{Joris.Klerkx, Erik.Duval, Michael.Meire}@cs.kuleuven.ac.be


Abstract

Learning objects are entities that may be used for
learning, education or training. Nowadays they are
often stored in Learning Object Repositories (LORs),
such as the Ariadne Knowledge Pool System (KPS)
[23], Merlot and EdNa. Typically users can search for
learning objects in those LORs by filling out an
electronic form that enables them to compose Boolean
combinations of search criteria. More research is
needed on novel access paradigms to enable more
effective and flexible access to these repositories. In this
paper, we investigate how we can use information
visualization techniques for this purpose. We discuss the
use of three existing information visualization
techniques that we applied to the ARIADNE Knowledge
Pool System (KPS).

Keywords--- learning objects, learning object
repositories, information visualization.

1. Introduction
According to the Learning Object Metadata (LOM)
standard, a learning object is ’any entity, digital or non-
digital, that may be used for learning, education or
training’ [1]. This definition allows for an extremely
wide variety of granularities. This means that a learning
object could be a picture of the Mona Lisa, a document
on the Mona Lisa (that includes the picture), a course
module on da Vinci, a complete course on art history, or
even a 4 year master curriculum on western culture [2].
Learning objects are stored in Learning Object
Repositories (LORs) together with extensive metadata
(e.g. LOM) that describes them. A few examples of
such LORs are the Ariadne Knowledge Pool System
(KPS) [19] [23], Merlot [20], EdNA [21],
Universal/educaNext [22], etc. Students and teachers are
able to search for learning objects in those LORs, based
on the metadata-elements that describe the learning
objects.
In most of those systems, there are two ways for
users to search. The first one is to formulate a simple or
an advanced query by filling out an electronic form that
enables end users to compose boolean combinations of
search criteria. The second one is to perform a kind of
directory or category search by browsing the learning
objects by subject. The resulting set of learning objects
are always presented in a traditional results list.
It is clear that there is a need for more research on
novel access paradigms that enable an end user to zoom
in on relevant learning objects without requiring him or
her to go through a lengthy process to formulate
complex search criteria, to evaluate some of the results,
refine the search criteria, etc. Information Visualization
can improve this kind of access to LORs by enabling
end users to manipulate controls over the metadata,
zoom in on potentially more relevant learning objects
and continuously keep an overview of how additional
search criteria restrict the remaining number of learning
objects [2].
We will start this paper in section 2 with a discussion
of the strategies users can follow to access LORs. In
section 3, we will compare three information
visualization techniques that we have applied to the
Ariadne KPS [19] [23]. In section 4 we will discuss a
few techniques for manipulating controls over the
metadata.
2. Access strategies
There are a number of ways of accessing a LOR.
Imagine an end user, like a teacher, who wants to reuse
a learning object in his own course. The first and
traditional way is to start from an empty set of results
and formulate a simple or an advanced query to get a
number of results. The teacher can then evaluate some
of the results and reformulate the query to filter out
some results or include some others. This process of
formulating queries and evaluating the results could be a
lengthy process and is therefore rather time-consuming
and user-unfriendly [2].