D6.3 – Semantic Content Annotation Support

The Semantic Annotation component is a software application that provides support for automated text classification, a process grounded in a cohesion-centered representation of discourse that facilitates topic extraction. The component enables the semantic meta-annotation of text resources, including automated classification, thus facilitating information retrieval within the RAGE ecosystem. It is available in the ReaderBench framework (http://readerbench.com/) which integrates advanced Natural Language Processing (NLP) techniques. The component makes use of Cohesion Network Analysis (CNA) in order to ensure an in-depth representation of discourse, useful for mining keywords and performing automated text categorization. Our component automatically classifies documents into the categories provided by the ACM Computing Classification System (http://dl.acm.org/ccs_flat.cfm), but also into the categories from a high level serious games categorization provisionally developed by RAGE. English and French languages are already covered by the provided web service, whereas the entire framework can be extended in order to support additional languages.

Document Cohesion Flow: Striving towards Coherence

Text cohesion is an important element of discourse processing. This paper presents a new approach to modeling, quantifying, and visualizing text cohesion using automated cohesion flow indices that capture semantic links among paragraphs. Cohesion flow is calculated by applying Cohesion Network Analysis, a combination of semantic distances, Latent Semantic Analysis, and Latent Dirichlet Allocation, as well as Social Network Analysis. Experiments performed on 315 timed essays indicated that cohesion flow indices are significantly correlated with human ratings of text coherence and essay quality. Visualizations of the global cohesion indices are also included to support a more facile understanding of how cohesion flow impacts coherence in terms of semantic dependencies between paragraphs.

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.

Finding the Needle in a Haystack: Who are the Most Central Authors Within a Domain?

The speed at which new scientific papers are published has increased dramatically, while the process of tracking the most recent publications having a high impact has become more and more cumbersome. In order to support learners and researchers in retrieving relevant articles and identifying the most central researchers within a domain, we propose a novel 2-mode multilayered graph derived from Cohesion Network Analysis (CNA). The resulting extended CNA graph integrates both authors and papers, as well as three principal link types: coauthorship, co-citation, and semantic similarity among the contents of the papers. Our rankings do not rely on the number of published documents, but on their global impact based on links between authors, citations, and semantic relatedness to similar articles. As a preliminary validation, we have built a network based on the 2013 LAK dataset in order to reveal the most central authors within the emerging Learning Analytics domain.

Integrating Scientific Publication into an Applied Gaming Ecosystem

Salman, M. et al. (2016). Integrating Scientific Publication into an Applied Gaming Ecosystem. GSTF Journal on Computing (JoC), Volume 5 (Issue 1), pp. 45-51.

Game Learning Analytics: Learning Analytics for Serious Games

Video games have become one of the largest entertainment industries, and their power to capture the attention of players worldwide soon prompted the idea of using games to improve education. However, these educational games, commonly referred to as serious games, face different challenges when brought into the classroom, ranging from pragmatic issues (e.g. a high development cost) to deeper educational issues, including a lack of understanding of how the students interact with the games and how the learning process actually occurs. This chapter explores the potential of data-driven approaches to improve the practical applicability of serious games. Existing work done by the entertainment and learning industries helps to build a conceptual model of the tasks required to analyze player interactions in serious games (gaming learning analytics or GLA). The chapter also describes the main ongoing initiatives to create reference GLA infrastructures and their connection to new emerging specifications from the educational technology field. Finally, it explores how this data-driven GLA will help in the development of a new generation of more effective educational games and new business models that will support their expansion. This results in additional ethical implications, which are discussed at the end of the chapter.

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.

An implementation architecture for scenario-based simulations

The past years have witnessed an increased use of applied games for developing and evaluating communication skills. These skills benefit from in-terpersonal interactions. Providing feedback to students practicing communica-tion skills is difficult in a traditional class setting with one teacher and many students. This logistic challenge may be partly overcome by providing training using a simulation in which a student practices with communication scenarios. A scenario is a description of a series of interactions, where at each step the player is faced with a choice. We have developed a scenario editor that enables teachers to develop scenarios for practicing communication skills. A teacher can develop a scenario without knowledge of the implementation. This paper presents the implementation architecture for such a scenario-based simulation.

D7.3 - Summary Report RAGE Stakeholder Consultation Pocess

This report presents a summary of the RAGE Stakeholder consultation methodology, process, instruments and activities, which inform parallel work packages in the RAGE project; specifically workpackage 8 (responsible for development of the evaluation framework) and workpackage 9 (concerned with exploitation and sustainability).

RAGE Reusable Game Software Components and Their Integration into Serious Game Engines

This paper presents and validates a methodology for integrating reusable software components in diverse game engines. While conforming to the RAGE com-ponent-based architecture described elsewhere, the paper explains how the interac-tions and data exchange processes between a reusable software component and a game engine should be implemented for procuring seamless integration. To this end, a RAGE-compliant C# software component providing a difficulty adaptation routine was integrated with an exemplary strategic tile-based game “TileZero”. Implementa-tions in MonoGame, Unity and Xamarin, respectively, have demonstrated successful portability of the adaptation component. Also, portability across various delivery platforms (Windows desktop, iOS, Android, Windows Phone) was established. Thereby this study has established the validity of the RAGE architecture and its un-derlying interaction processes for the cross-platform and cross-game engine reuse of software components. The RAGE architecture thereby accommodates the large scale development and application of reusable software components for serious gaming.

D6.6 – Summary, Analysis, Road-mapping and Production of Training materials

This deliverable summarizes, validates and explains the purpose and concept behind the RAGE knowledge and innovation management platform as a self-sustainable Ecosystem, supporting innovation processes in the Applied Gaming (AG) industry. The Ecosystem portal will be developed with particular consideration of the demand and requirements of small and medium sized game developing companies, education providers and related stakeholders like AG researchers and AG end-users. The innovation potential of the new platform underlies the following factors: a huge, mostly entire collection of community specific knowledge (e.g., content like media objects, software components and best practices), a structured approach of knowledge access, search and browse, collaboration tools as well as social network analysis tools to foster efficient knowledge creation and transformation processes into marketable technology assets. The deliverable provides an overview of the current status and the remaining work to come, preceding the final version in month 48 of the RAGE project.

D2.3 First bundle of client-side components

This document describes the first bundle of core WP2 (user data analytics) client side components, including their specifications, usecases, and working prototypes. Included assets contain a description of their current status, and links to their full designs and downloadable versions. This deliverable only describes operational SW assets (even though beta) that are tested and documented. It should be noted, however, that various additional software assets (2.2d Cognitive Capacity Measurement and 2.3a Realtime Emotion Detection) are near completion for inclusion in games during the first pilot round. Those assets are still scheduled for inclusion in the final bundle deliverable D2.2.

Serious gaming: wondermiddel of luchtkasteel?

Games voor onderwijs en training worden een serieuze zaak. Marktprognoses melden een jaarlijkse groei van 18% tot een wereldwijd volume van meer dan 5 miljard Euro in 2020. Games bieden leerlingen een virtuele praktijk, waarin ze actief kunnen oefenen. Studietaken worden levensechte uitdagingen die tot indringende leerervaringen leiden. Dat schept nieuwe mogelijkheden juist in het MBO, waar praktische ervaring vaak belangrijker is dan theoretische kennis. Deze sessie geeft een snelle update over serious games. Diverse game-mechanismen worden besproken aan de hand van actuele voorbeelden.

D2.1 - First bundle of server-side components

This document describes the first bundle of core WP2 (user data analytics) serverside components, including their specifications, usecases, and working prototypes. Included assets contain a description of their current status, and links to their full designs and downloadable versions.

Modeling Individual Differences among Writers Using ReaderBench

The current study builds upon a previous study, which examined the degree to which the lexical properties of students’ essays could predict their vocabulary scores. We expand on this previous research by incorporating new natural language processing indices related to both the surface- and discourse-levels of students’ essays. Additionally, we investigate the degree to which these NLP indices can be used to account for variance in students’ reading comprehension skills. We calculated linguistic essay features using our framework, ReaderBench, which is an automated text analysis tools that calculates indices related to linguistic and rhetorical features of text. University students (n = 108) produced timed (25 minutes), argumentative essays, which were then analyzed by ReaderBench. Additionally, they completed the Gates-MacGinitie Vocabulary and Reading comprehension tests. The results of this study indicated that two indices were able to account for 32.4% of the variance in vocabulary scores and 31.6% of the variance in reading comprehension scores. Follow-up analyses revealed that these models further improved when only considering essays that contained multiple paragraph (R2 values = .61 and .49, respectively). Overall, the results of the current study suggest that natural language processing techniques can help to inform models of individual differences among student writers.