Identifying and classifying learning entities for designing location-based serious games

Abstract

Publisher: IEEE © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Copyright © and Moral Rights are retained by the author(s) and/ or other copyright owners. A copy can be downloaded for personal non-commercial research or study, without prior permission or charge. This item cannot be reproduced or quoted extensively from without first obtaining permission in writing from the copyright holder(s). The content must not be changed in any way or sold commercially in any format or medium without the formal permission of the copyright holders. This document is the author's post-print version, incorporating any revisions agreed during the peer-review process. Some differences between the published version and this version may remain and you are advised to consult the published version if you wish to cite from it.Identifying and classifying learning entities for designing location-based serious games Despina Anastasiadou, Petros LamerasThe Serious Games Institute, Coventry University Coventry, UK DAnastasiadou@cad.coventry.ac.uk, PLameras@cad.coventry.ac.ukAbstract-This paper investigates the development of a classification of features inherent in the design and development of Location Based Experiences (LBEs) with a special focus on games for teaching and learning. The paper aims to identify and associate learning features, such as feedback, activities, outcomes and assessment with location-driven mechanics, such as location-based activities, entities, conditions and actions that constitute the overarching elements of a proprietary location-based games authoring tool. We anticipate that this will pave the way for developing a model taxonomy that may be utilised to support and optimise future end-user profiles for serious game creation, games design for informal learning paths in science museums, science centres and field trips, learning methodologies development and metadata creation. The classification draws on the findings of a tailored approach applied to design and develop an authoring environment, the MAGELLAN platform, for creating locationbased games and mobile location-driven scenarios directly influenced by end-user requirements and evaluation of trainee's feedback. Ultimately, the classification is conceived as part of a broader framework that defines and enables the creation of location-driven games by associating them with learning elements, through visualised design for expert and non-expert users as potential game authors. In an iterative process, the MAGELLAN Authoring Tool and subsequent user training and piloting process is featured as a test-bed, where the proposed taxonomy will be applied and evaluated.