D3.3 – First Social Gamification Assets

This deliverable is software, as such this document is abridged to be as succinct as possible, the extended descriptions and detailed documentation for the software are online. The document consists of two parts, part one describes the first bundle of social gamification assets developed in WP3, part two presents mock-ups of the RAGE ecosystem gamification. In addition to the software outline, included in part one is a short market analysis of existing gamification solutions, outline rationale for combining the three social gamification assets into one unified asset, and the branding exercise to make the assets more developer friendly.Online links to the source code, binaries, demo and documentation for the assets are provided. The combined assets offer game developers as well as a wide range of software developers the opportunity to readily enhance existing games or digital platforms with multiplayer gamification functionalities, catering for both competitive and cooperative game dynamics. The solution consist of a flexible client-server solution which can run either as a cloud-based service, serving many games or have specific instances for individual games as necessary.

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.

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.

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.

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.

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.

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.

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.

Newcomer Integration in Online Knowledge Communities: Exploring the Role of Dialogic Textual Complexity

Using online knowledge communities (OKCs) as informal learning environments poses the question how likely these will integrate newcomers as peripheral participants. Previous research has identified surface characteristics of the OKC dialog as integrativity predictors. Yet, little is known about the role of dialogic textual complexity. This contribution proposes a comprehensive approach based on previously validated textual complexity indexes and applies it to predict OKC integrativity. The dialog analysis of N = 14 blogger communities with a total of 1937 participants identified three main components of textual complexity: dialog participation, structure and cohesion. From these, dialog cohesion was higher in integrative OKCs, thus significantly predicting OKC integrativity. This result adds to previous OKC research by uncovering the depth of OKC discourse. For educational practice, the study suggests a way of empowering learners by automatically assessing the integrativity of OKCs in which they may attempt to participate and access community knowledge.