Information Extraction Model to Improve Learning Game Metadata Indexing

Abstract

Received: Accepted: The use of Learning Games (LGs) in schools is a success factor for students. The benefits they bring to the learning process should be widely disseminated at all levels of education. Currently, there are thousands of LGs that cover a large variety of educations fields. Despite this large choice of LGs, very few are used by teachers, due to the difficulty of finding and selecting suitable LGs. The aim of this paper is to propose an extraction model that will automatically collect the information about LGs directly from their web pages, in order to index them in a catalogue. The proposed ADEM (Automatic Description Extraction Model), browses the web pages describing LGs and does a first cleaning to remove any unnecessary information. Then a detection of description blocks, based on a certain number of criteria, identifies the regions containing the LG description text. Finally, an indexing on specific fields is performed. ADEM made it possible to automatically process 785 web pages to extract LG metadata indexing information. The results of this extraction process were validated by 20 teachers. This model therefore offers a promising starting point for better LG indexing and the creation of a complete catalogue.