D9.5 – Exploitation Plan (version 1 of 3)

The RAGE Exploitation Plan is a living document, to be upgraded along the project lifecycle, supporting RAGE partners in defining how the results of the RAGE RIA will be used both in commercial and non-comercial settings. The Exploitation Plan covers the entire process from the definition of the business case for the RAGE Ecosystem to the creation of the sustainability conditions for its real-world operation beyond the H2020 project co-funding period. The Exploitation Plan will be published in three incremental versions, due at months 18, 36 and 42 of the project lifetime. This early stage version 1 of 3 is mainly devoted to: i. Setting-up the structure and the initial building blocks to be populated and completed in the future editions of the Exploitation Plan and to ii. providing additional guidance for market intelligence gathering, business modelling definition and validation, outreach and industry engagement and ultimately providing insights for the development, validation and evaluation of RAGE results across the project´s workplan execution. These tasks will in turn render suitable inputs to enhance the two future editions of the Exploitation Plan.

D1.4 – First version applied gaming asset methodology

This deliverable (D1.4) is an intermediate document, expressly included to inform the first project review about RAGE’s methodology of software asset creation and management. The final version of the methodology description (D1.1) will be delivered in Month 29. The document explains how the RAGE project defines, develops, distributes and maintains a series of applied gaming software assets that it aims to make available. It describes a high-level methodology and infrastructure that are needed to support the work in the project as well as after the project has ended.

Software Components for Serious Game Development

The presentation explains the approach of the RAGE project. It presents three examples of RAGE software components and how these can be easily reused for applied game development.

Amplifying applied gaming

This presentation explains how RAGE develops reusable game technology components and provides examples of their application.

The RAGE Software Asset Model and Metadata Model

Software assets are key output of the RAGE project and they can be used by applied game developers to enhance the pedagogical and educational value of their games. These software assets cover a broad spectrum of functionalities – from player analytics including emotion detection to intelligent adaptation and social gamification. In order to facilitate integration and interoperability, all of these assets adhere to a common model, which describes their properties through a set of metadata. In this paper the RAGE asset model and asset metadata model is presented, capturing the detail of assets and their potential usage within three distinct dimensions – technological, gaming and pedagogical. The paper highlights key issues and challenges in constructing the RAGE asset and asset metadata model and details the process and design of a flexible metadata editor that facilitates both adaptation and improvement of the asset metadata model.

Software Components for Serious Game Development

The large upfront investments required for game development pose a severe barrier for the wider uptake of serious games in education and training. Also, there is a lack of well-established methods and tools that support game developers at preserving and enhancing the games’ pedagogical effectiveness. The RAGE project, which is a Horizon 2020 funded research project on serious games, addresses these issues by making available reusable software components that aim to support the pedagogical qualities of serious games. In order to easily deploy and integrate these game components in a multitude of game engines, platforms and programming languages, RAGE has developed and validated a hybrid component-based software architecture that preserves component portability and interoperability. While a first set of software components is being developed, this paper presents selected examples to explain the overall system’s concept and its practical benefits. First, the Emotion Detection component uses the learners’ webcams for capturing their emotional states from facial expressions. Second, the Performance Statistics component is an add-on for learning analytics data processing, which allows instructors to track and inspect learners’ progress without bothering about the required statistics computations. Third, a set of language processing components accommodate the analysis of textual inputs of learners, facilitating comprehension assessment and prediction. Fourth, the Shared Data Storage component provides a technical solution for data storage - e.g. for player data or game world data - across multiple software components. The presented components are exemplary for the anticipated RAGE library, which will include up to forty reusable software components for serious gaming, addressing diverse pedagogical dimensions.