Learning Games Factory: Construction of Learning Games Using a Component-Based Approach
Résumé
The complexity of Learning Games development is currently increasing significantly. The requirements for these learning environments imply building several components aiming at supporting particular activities (games of "snakes and ladders" type, puzzles, animated MCQs). Furthermore, new up-to-date functionalities are often wanted: collaborative aspects, observation features for awareness purpose, and links to tangible user interfaces. In light of this observation, we need a new approach for the construction of Learning Games, in order to reduce the associated time and costs. We propose to reuse the various existing components for a particular purpose, to adapt, to configure and to integrate them in order to obtain a new game. We call our approach Learning Games Factory (LGF) as in a factory where one assembles parts to obtain manufactured objects. In this approach, we need powerful research tools to find out the suitable components meeting particular requirements. In this paper, we show how to ease the exchange of data between data providers and service providers through the Open Archives Initiative's Protocol for Metadata Harvesting (OAI-PMH). We first give the description of a general game-based platform called "Learning Adventure". We then explain how to tag components with meta-data, according to usual standards in the education field. The retrieval of such components according to specific needs is subsequently described through an architecture using OAI-PMH. We illustrate the different steps of this approach through a broad example in the "Learning Adventure" (LA) Platform. This example is taken from the LGF project, in which the different partners have already developed their own components (compliant with the LGF standards). The interesting idea is the integration of these different components into the LA platform, allowing their sequencing and their data exchange in order to obtain effortlessly a new learning game.