Digital catalogues must be intuitive and easy to use. However, designing their interfaces is a complex task because there is so much available information and such little space. The choice of search filters, their format, their position, including the way to represent the results, are not trivial decisions. This paper presents the User-Driven Interface Design (UDID) method that offers five steps with specific material to help end users produce mock-up interfaces for digital catalogues. This method recommends letting participants compose their interfaces according to their needs. In this article, we present how the UDID method offered several befits for designing the interface of a Learning Game catalogue. 17 participants followed this method to produce five mock-up interfaces that we then analysed and compared to create the final interface.
Learning Games (LGs) have proven to be effective in a large variety of academic fields and for all levels; from kindergarten to professional training. They are therefore very valuable learning resources that should be shared and reused. However, the lack of catalogues that allow teachers to find existing LGs is a significant obstacle to their use in class. It is difficult for catalogues, or any type of search engine, to index LGs because they are poorly referenced. Yet, many researches have proposed elaborate metadata models for LGs. However, all these models are extensions of LOM, a metadata model that is widely used for referencing learning resources, but that contains more than 60 fields, of which more than half are irrelevant to LGs. The gap between these models and the information that game designers are willing to provide is huge. In this paper, we analyze the LG metadata models proposed in previous research to detect the fields that are specific to LGs and the fields that are irrelevant to LGs. We then propose LGMD (Learning Games Metadata Definition), an optimal lightweight metadata model that only contains the important information for LG indexing. LGMD reduces by two thirds the number of fields compared to the previous models. We confronted this model with the information actually provided by LG editors, by analyzing 736 LG page descriptions found online. This study shows that LGMD covers all the information provided by the LG editors.
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.
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