Enhancing availability of learning resources on organic agriculture and agroecology

Date02 October 2009
Pages792-813
DOIhttps://doi.org/10.1108/02640470910998524
Published date02 October 2009
AuthorSalvador Sánchez‐Alonso
Subject MatterInformation & knowledge management,Library & information science
Enhancing availability of learning
resources on organic agriculture
and agroecology
Salvador Sa
´nchez-Alonso
Information Engineering Research Unit, University of Alcala
´, Madrid, Spain
Abstract
Purpose The purpose of this paper is to describe the provision of existing learning object
repositories with enhanced forms of describing digital resources on organic agriculture and
agroecology.
Design/methodology/approach The approach followed was to investigate non-invasive
techniques for semantic annotation of learning objects, for which use was made of two tools. The
first tool was a software application for the automated classification of learning resources stored in
public learning object repositories. The second tool was an ontology in OWL derived from the
knowledge in the Agrovoc thesaurus.
Findings – Current digital repositories for educational resources and open access archives provide
scholars with a number of features, such as the ability to search for materials according to given
criteria, or to retrieve the full content of those materials from the repository. Many provide advanced
features as well, such as browsing, assessing and collaboratively peer reviewing learning resources,
but at the cost of using the specific tools and interfaces provided by each repository.
Research limitations/implications This research is part of the EU-funded project
Organic.Edunet, aimed at facilitating access, usage and exploitation of digital educational content
related to organic agriculture and agroecology (OA&AE). Consequently, knowledge representation,
thesauri and the educational resources reported herein are deliberately focused on the OA&AE
domain, even though the concepts and techniques utilized may be easily applied in other contexts.
Originality/value – The paper describes two new approaches aimed at enhancing availability of
learning resources: the potential use of ontologies for the description of learning resources, and the full
use of the classification category in the IEEE LOM metadata standard.
Keywords Digital storage,Organic food, Agriculture, Learningmethods
Paper type Research paper
1. Introduction
Organic farming is a form of agriculture aimed at efficiently producing food while
respecting the environ ment and preserving Ear th’s natural fertility . Organic
agricultural practices and methods emphasize the optimization of the resources
available and avoid synthetic pesticides and fertilizers as a means of attaining its main
goals. Nowadays, organic farming is widespread in most developed countries, but the
promotion of organic practices in farming still requires a considerable effort in terms of
education.
Different institutions and organizations provide quality educational resources on
organic agriculture and agroecology (OA&AE) topics openly on the web. However,
The current issue and full text archive of this journal is available at
www.emeraldinsight.com/0264-0473.htm
The research reported in this paper is part of the activities of the EU-funded project
Organic.Edunet (ECP-2006-EDU-410012), from which it receives partial funding.
EL
27,5
792
Received 1 March 2008
Revised 15 September 2008
Accepted 24 October 2008
The Electronic Library
Vol. 27 No. 5, 2009
pp. 792-813
qEmerald Group Publishing Limited
0264-0473
DOI 10.1108/02640470910998524
locating those resources with conventional search engines is often complicated,
mainly owing to noise in the results of common search input terms. Learning object
repositories provide an alternative which enables more relevant results at the cost of
developing (and packing together with the learning objects) a few metadata records.
The IEEE LOM standard (LTSC, 2001) is the most widely recognised form of
providing metadata to learning resources, thus facilitating their retrieval from
learning repositories.
Learning object repositories are a specific type of software aimed at properly storing
and organizing learning resources. As different types of repositories are often referred
to under the generic designation of learning object repository, it is necessary to classify
them before continuing.
Learning object repositories are online databases of learning contents. They host
digital resources for education, and most also host metadata which describes the
learning object. The degree of conformance of this metadata to IEEE LOM varies: some
repositories rely on their own specific metadata schema, while others implement a
IEEE LOM application profile. The Wisconsin online resource centre
(www.wisc-online.com/) and Organic Eprints (http://orgprints.org) are two examples
of this type of repository.
Learning object metadata repositories. These do not host the digital resources
themselves, but instead metadata records for learning objects stored elsewhere.
Relevant examples are Merlot (www.merlot.org) and Intute (www.intute.ac.uk).
The existence of learning object repositories helps users to find quality materials,
but our experience says that current metadata descriptions in those repositories are,
unfortunately, not rich enough to provide relevant search results to their users. A
non-exhaustive list of shortcomings could be the following:
.Metadata records in existing repositories are often fragmentary and
unstructured (Page
´set al., 2003).
.The level of description in learning objects annotation is often deficient: most
metadata elements are either never or rarely used by annotators.
.Learning resources annotations are extensively dependent on repository-specific
non-standard metadata elements. The IEEE LOM standard itself, often criticized
for the unclear use of some of its categories, may be a key factor in its lack of
widespread adoption (Robson, 2003; Farance, 2003).
Providing better search mechanisms to the users is a challenging task in the light of
the above considerations. However, existing techniques can be successfully applied
to aid in obtaining better search results through the provision of enhanced
classifications to current metadata. It is important to point out that neither the
educational resources themselves nor the repositories where they are hosted will
need to be modified as a result of the application of those techniques. As we will
describe further in this paper, the automated classification of existing metadata and
the introduction of semantic web mechanisms will offer us the necessary tools to
achieve more relevant results to those users searching for learning objects in
OA&AE repositories.
The first of the two techniques suggested, which we will detail in depth in section 2,
is the automated classification of learning resources in a public repository from their
associated metadata. The target of our study will be the digital resources in Organic
Availability of
learning
resources
793

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