ReaderBench: An Integrated Cohesion-Centered Framework

Dascalu, M., Stavarache, L.L., Dessus, P., Trausan-Matu, S., McNamara, D.S., & Bianco, M. (2015). ReaderBench: An Integrated Cohesion-Centered Framework. In G. Conole, T. Klobucar, C. Rensing, J. Konert & É. Lavoué (Eds.), 10th European Conf. on Technology Enhanced Learning (pp. 505–508). Toledo, Spain: Springer.

D8.1 - RAGE Evaluation Framework and Guidelines

This document presents the RAGE evaluation methodology. It provides the framework and accompanying guidelines for the evaluation and validation of the quality and effectiveness of the project outputs. Formative and summative evaluations of the different RAGE technologies and their underlying methodologies – the assets, the Ecosystem, and the applied games – will be carried out on the basis of this common framework.

D3.5 – First Storytelling Framework

This document presents the first release of the project’s storytelling framework, which is composed by two assets. The purpose of this framework is to facilitate the use of interactive storytelling for the development of applied games. More precisely, the framework is meant to aid developers in the creation of game scenarios where both players and autonomous characters are playing an active role in a narrative that unfolds according to their actions. The document describes the current state for the assets that are part of this framework, also providing links to the source code of the assets as well as associated demonstrations and documentation. The primary audience for the contents of this deliverable are the game developers that will use the proposed framework in their development process. The information about the specific RAGE use cases that are using the framework is written in Deliverable 4.2.

ReaderBench goes Online: A Comprehension-Centered Framework for Educational Purposes

In this paper we introduce the online version of our ReaderBench framework, which includes multi-lingual comprehension-centered web services designed to address a wide range of individual and collaborative learning scenarios, as follows. First, students can be engaged in reading a course material, then eliciting their understanding of it; the reading strategies component provides an in-depth perspective of comprehension processes. Second, students can write an essay or a summary; the automated essay grading component provides them access to more than 200 textual complexity indices covering lexical, syntax, semantics and discourse structure measurements. Third, students can start discussing in a chat or a forum; the Computer Supported Collaborative Learning (CSCL) component provides indepth conversation analysis in terms of evaluating each member’s involvement in the CSCL environments. Eventually, the sentiment analysis, as well as the semantic models and topic mining components enable a clearer perspective in terms of learner’s points of view and of underlying interests.