RAGE Architecture for Reusable Serious Gaming Technology Components

For seizing the potential of serious games, the RAGE project - funded by the Horizon-2020 Programme of the European Commission - will make available an interoperable set of advanced technology components (software assets) that support game studios at serious game development. This paper describes the overall software architecture and design conditions that are needed for the easy integration and reuse of such software assets in existing game platforms. Based on the component-based software engineering paradigm the RAGE architecture takes into account the portability of assets to different operating systems, different programming languages and different game engines. It avoids dependencies on external software frameworks and minimizes code that may hinder integration with game engine code. Furthermore it relies on a limited set of standard software patterns and well-established coding practices. The RAGE architecture has been successfully validated by implementing and testing basic software assets in four major programming languages (C#, C++, Java and Typescript/JavaScript, respectively). A demonstrator implementation of asset integration with an existing game engine was created and validated. The presented RAGE architecture paves the way for large scale development and application of cross-engine reusable software assets for enhancing the quality and diversity of serious gaming.

Making sense of game based user data: learning analytics in applied games

Digital learning games are useful educational tools with high motivational potential. With the application of games for instruction there comes the need of acknowledging learning game experiences also in the context of educational assessment. Learning analytics provides new opportunities for supporting assessment in and of educational games. We give an overview of current learning analytics methods in this field and reflect on existing challenges. An approach of providing reusable software assets for interaction assessment and evaluation in games is presented. This is part of a broader initiative of making available advanced methodologies and tools for supporting applied game development.

Performance assessment in serious games: Compensating for the effects of randomness

This paper is about performance assessment in serious games. We conceive serious gaming as a process of player-lead decision taking. Starting from combinatorics and item-response theory we provide an analytical model that makes explicit to what extent observed player performances (decisions) are blurred by chance processes (guessing behaviors). We found large effects both theoretically and practically. In two existing serious games random guess scores were found to explain up to 41% of total scores. Monte Carlo simulation of random game play confirmed the substantial impact of randomness on performance. For valid performance assessments, be it in-game or post-game, the effects of randomness should be included to produce re-calibrated scores that can reasonably be interpreted as the players´ achievements.

Applying standards to systematize learning analytics in serious games

Learning Analytics is an emerging field focused on analyzing learners’ interactions with educational content. One of the key open issues in learning analytics is the standardization of the data collected. This is a particularly challenging issue in serious games, which generate a diverse range of data. This paper reviews the current state of learning analytics, data standards and serious games, studying how serious games are tracking the interactions from their players and the metrics that can be distilled from them. Based on this review, we propose an interaction model that establishes a basis for applying Learning Analytics into serious games. This paper then analyzes the current standards and specifications used in the field. Finally, it presents an implementation of the model with one of the most promising specifications: Experience API (xAPI). The Experience API relies on Communities of Practice developing profiles that cover different use cases in specific domains. This paper presents the Serious Games xAPI Profile: a profile developed to align with the most common use cases in the serious games domain. The profile is applied to a case study (a demo game), which explores the technical practicalities of standardizing data acquisition in serious games. In summary, the paper presents a new interaction model to track serious games and their implementation with the xAPI specification.