Beginkenmerken en Pedagogisch-Didactische Kern Structuur®: Handleiding voor scholen en leerkrachten in het Primair Onderwijs

Kinderen worden in basisscholen veelal in leeftijdsgroepen ingedeeld. Er zijn dan steeds leerlingen die ten opzichte van leeftijdsgenoten heel goed of juist slecht presteren. Dit leidt bij deze 'relatief afwijkende leerlingen' tot demotivatie, gerichtheid op andere activiteiten, en 'onderpresteren'. In de hier voorgestane pedagogiek en didactiek wordt elk kind, per ontwikkelings- of competentiegebied, doorlopend en adequaat gesteund. Dit is ook conform de vigerende onderwijswetgeving. De praktische consequentie is dat het onderwijsaanbod in elke groep of klas steeds dient te zijn afgestemd op de werkelijke (begin)kenmerken van de aanwezige leerlingen. Vervolgens dient elk kind in school steeds iets boven het eigen niveau te kunnen spelen en leren, deels volgens eigen voorkeuren en eigen regulatie. Dit vereist voorbereide onderwijsleersituaties en een doorlopend helder inzicht in relevante diagnostische ontwikkelings- en leerniveaus alsmede in de individuele leerprocessen en -effecten. Praktische realisatie van deze eisen vraagt ten eerste om vaststelling van de beginkenmerken van leerlingen. Ten tweede dient het feitelijke speel- en leeraanbod te zijn georganiseerd in aansluiting op deze individuele beginkenmerken, in een meer flexibele vormgeving dan gebruikelijk. Deze twee onderwijsaspecten worden hier concreet uitgewerkt en ingevuld in het kader van een handleiding voor ontwikkelings- of design-based onderzoek dat samen met leerkrachten en management wordt uitgevoerd.

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.

D6.1 – Ecosystem Software Platform with Underlying Software Repository, Digital Library and Media Archive

The objective of D6.1 is to make the Ecosystem software platform with underlying Software Repository, Digital Library and Media Archive available to the degree, that the RAGE project can start collecting content in the form of software assets, and documents of various media types. This paper describes the current state of the Ecosystem as of month 12 of the project, and documents the structure of the Ecosystem, individual components, integration strategies, and overall approach. The deliverable itself is the deployment of the described components, which is now available to collect and curate content. Whilst this version is not yet feature complete, full realization is expected within the next few months. Following this development, WP6 will continue to add features driven by the business models to be defined by WP7 later on in the project.

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.