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Serious games often employ pre-scripted dialogues and interactions with a player; in contrast to free user input that enables deeper immersion. In this paper we explore possibilities for interactive natural language dialogue in a serious game by combining Natural Language Processing (NLP) techniques with dialogue management. Our game learning environment has a communication scenario editor in which a domain expert develops a structured, scripted scenario as a sequence of potential interactions. A communication scenario is context-specific and often follows a protocol - for instance, delivering bad news to a patient. Currently, a player navigates through a simulation and converses with a virtual character by choosing a statement option from one of the prescripted player statements, at each step in the simulation. We develop a scenario-specific corpus method (SSCM) to process open responses (i.e. natural language inputs) in our learning environment. We conduct an experiment to collect data for comparing SSCM against multiple NLP methods, and another experiment to investigate if framing can improve processing open-text input using SSCM in a communication simulation.

International audience; Computer Supported Collaborative Learning (CSCL) environments have become a viable learning alternative from which valuable data can be extracted and used for advanced analyses centered on evaluating participants' involvement and their interactions. Such automated assessments are implemented within our ReaderBench framework, a Natural Language Processing platform that contains multiple advanced text analysis functionalities. The ReaderBench framework is based on Cohesion Network Analysis from which different sociograms, relying on semantic similarity, are generated in order to reflect interactions between participants. In this paper, we briefly describe the enforced mechanisms used to analyze three scenarios: individual chat conversations, virtual communities of practice and MOOCs.

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.

National audience; Nous décrivons un prototype de Manuel ouvert, online et massif (MOOT) auquel on peut intégrer diverses fonctionnalités utilisant des tech- niques de Traitement automatique de la langue, afin de promouvoir l’apprentissage auto-régulé. Nous discutons également de l’intérêt de disposer de ressources largement ouvertes, accessibles et flexibles dans l’enseignement supérieur.