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Server-Side Interaction Storage and Analytics

Ready-to-deploy server-side implementation of a data collection and storage service. Handles authentication and supports current standards for exchange of interaction data.

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Step based competence assessment

Produces an on-the-fly stealth assessment of a particular player skill, based on player interactions. Allows parsing of scenarios generated by the RAGE Communication Scenario Editor. Provides information about the possible following steps at each step in the series of interactions in the scenario

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ReaderBench – Sentiment Analysis on Texts

Sentiment analysis and opinion mining analysis tool. Analyses texts in multiple languages. Focused on the identification of six major sentiment valences (excited, sad, scared, angry, tender and happy)

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Authentication & Authorization

Provides a central location where clients can authenticate and locate server-side assets, including analytics. Servers can also register to locate other servers and be locatable by clients, restricting access using configurable roles.

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Game Storage, Server-Side

Allows games (or authenticated clients or servers) to store small amounts of data on either locally or on the server, associated to arbitrary keys, and later retrieve, modify or erase this data.

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Server-side Dashboard and Analysis

Provides the user interface to access the analytics functionalities of the system, depending on the role with which stakeholders log in. Provides interfaces for pluggable custom analyses and visualizations as well a reference set of standard visualizations. 

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Client Tracker

Service to collect game interaction data and to send this data to the RAGE server-side components

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Domain Model

Models the knowledge domain addressed by the game and representing the subject matter to be learned. In the game development process, the competences conveyed by a game are identified

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Competence Assessment

Allows uncovering the competence state of a player while playing a game

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Motivation Assessment

Allows assessing the player’s motivation to learn while playing a game

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Real-Time Arousal Detection Using Galvanic Skin Response

Detects in real time human arousal based on measuring electro-dermal Activity (EDA).

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Client-Side Real-Time Emotion Recognition

integrates multiple emotion recognition modalities and combines them into a single classification

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Game Storage, Client-Side

Client for the Game Storage Server. Allows saving and querying information either locally or on this server.

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Emotional Appraisal

Manages the emotional state of a game character in response to the events that occur during the game.

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Emotional Decision Making

manages the decisions of a game character that has an internal emotional state. The emotional state requirement is easily achieved by using this asset in conjunction with the Emotion Appraisal Module.

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Social Importance Dynamics

Facilitates the creation of intelligent characters that behave appropriately according to a configurable set of established socio-cultural rules of behaviour

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Virtual Human Controller

Generates expressive behaviour for characters including lip-synchronized speech animation, gestures, facial expressions and gaze

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Role-Play Character

Provides the game developer a simplified abstraction of the internal processes of a character

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Integrated Authoring Tool

Package of tools to create interactive storytelling scenarios. It offers a user-friendly interface to manage the general aspects of a scenario and the behaviour profiles of the non-player characters in it. It also provides a dialogue editor

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ReaderBench – Semantic Models and Topic Mining  

Analyses a text and extracts keywords and topics from it, together with corresponding relevance scores and semantic links between them

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ReaderBench – Automated Essay Grading

Provides a multi-dimensional analysis of textual complexity, covering classic readability formulas, surface metrics derived from automatic essay grading techniques, syntax indices, as well as semantics and discourse.

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ReaderBench – Automated Assessment of Participation and Collaboration in CSCL Conversations

Offers two computational models for assessing participation and collaboration in Computer Supported Collaborative Learning (CSCL).

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ReaderBench – Automated Identification of Reading Strategies

Determines specific strategies used by learners as reliable predictors of comprehension

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Communication Scenario Editor

Tool for authoring dialogues in a user-friendly manner and allows for defining learning goals

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Speech I/O

Set of components for NLP, including speech synthesis and speech act recognition

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Adaptation and Assessment (TwoA)

Enables a real-time adaption of game difficulty according to the player’s expertise

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Competence-based Adaptation

Selects game situations according to the competencies of the player

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Motivation-based Adaptation

Displays encouraging messages based on the player’s motivational state

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Player Profiling

Gives the player pre-game questionnaires to analyse personality traits that can be used to change the contents of the game automatically by the game developer

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Cognitive Intervention

Provides informative feedback at key moments to promote reflection so learners do not get stuck or get off the desired learning path.

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Player-Centric Rule-and-Pattern-Based Adaptation

Uses real-time metrics of player’s performance and emotional state to dynamically adapt features of the game.

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Social Gamification Assets (SUGAR)

Delivers features related to gamification, matchmaking and team formation. It consists 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

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