itec - European Schoolnet

iTEC Project
Title: ITEC-D10_2_V1-1 041102012.Docx
REPORT
1. SCENARIO DEVELOPMENT ENVIRONMENT
This chapter discusses the activities performed during the second year corresponding to tasks
T10.3 and T10.4, whose main aim was to implement the first prototype of the Scenario
Development Environment (SDE). The SDE is a software system based on semantic technologies
that manages its own information models expressed as RDF triples (Beckett, 2004). The
development, alignment and updating of this conceptual framework is performed within WP10’s
tasks T10.1 and T10.2, whose most relevant results during the second year are discussed in
Chapter 2.
The SDE is a software package whose services are transparently integrated with those offered by
the iTEC Composer to extend its functionality by providing recommendations to support the
selection of resources. Therefore, the SDE does not provide an actual user interface, as other
components in the iTEC Cloud will manage the interaction with final users. Each of the services
implemented by the SDE is exposed to the world through the SDE API, whose main features (i.e.
methods and data models) are briefly introduced in Section 1.1 and thoroughly described in
Appendix III. The development of this API has been performed from the use cases in the SDE’s
Reference Architecture, discussed in D10.1 Section 4.3 (Anido, Santos, Caeiro, Míguez, Cañas, &
Fernández, 2011). Functionalities provided by this API can be classified into three categories (cf.
Figure 2):
A) Technical Localization: methods in this category support the identification of Learning
Stories and Learning Activities that may be implemented in a school according to the
technological resources available there.
B) Resource Planning: collects methods targeted to support the location and recommendation
of resources useful to implement a Learning Story / Learning Activity.
C) Validation: it provides methods to support the automated analysis of the degree to which
the selected resources in a LARG meet the requirements specified by a Learning Story.
The recommendation process is undoubtedly one of the more complex activities supported by SDE
methods. It is organized into three distinct steps:
1. Parsing and analysis of the requests received by the SDE API, and incorporation of
relevance-related contextual information.
2. Generation, according to the requests above, of one or more SPARQL semantic queries to
the system’s Knowledge Base. This provides preliminary contextualized information
filtering to retrieve only those resources that are potentially relevant to the active
recommendation process.
3. Estimation of the relevance of each resource according to the heuristics and algorithms.
Section 1.2 analyses the existing alternatives for selecting recommendation algorithms, justifies
the options taken, and discusses the recommendation factors considered by the present system.
The greater the amount of information handled by the KB, the more accurate the recommendation
results that can be obtained. The SDE relies on source data from existing iTEC registries, namely
People & Events Directory, Learning Resource Exchange, Widget Store and Social Data
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