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Detection of learning curves of player metrics is very important for the serious (or so called applied) games, because it provides an indicator representing how players master the game tasks by acquiring cognitive abilities, knowledge, and necessary skills for solving the game challenges. Real time identification of specific patterns in the learning curve of a particular player may be applied for dynamic adjusting of learning task difficulty and audio-visual features of the game. The paper presents a method for automatic and straightforward detection of specific learning curves at run time within a 3D video game of car driving in various weather conditions. The method uses a client-side software component called "Player-centric rule-and-pattern-based adaptation asset" and developed in the scope of the RAGE (Realising and Applied Gaming Ecosystem) H2020 project. This component is integrated within the video game in order to detect dynamically specific player learning curve patterns of moving average of overall player performance. The paper explains how these patterns were identified while players play the game and, next, how are defined formally within the asset for a detection at run time. The presented results show that the patterns are found in consecutive game sessions, although the changes in player performance and playing time. ; This study is part of the RAGE project. The RAGE project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 644187. This publication reflects only the author's view. The European Commission is not responsible for any use that may be made of the information it contains.

In recent years, the interest to the area of computer games for educational purposes increased due to their positive outcomes and effects in technology-enhanced learning. One of their chief merits is retaining the learning motivation and engagement of players during all time of the game. Therefore, it is necessary educational games to be able to adjust their features such as task difficulty, object speed, learning content, etc. according to the current emotional state of the player and, as well, to his/her playing style. In this paper, we present a dynamic mechanism for affective game adaptation based on both emotion and arousal estimation. The mechanism is implemented within an applied video game named "Rush for Gold" designed for implicit recognition of playing or learning styles. The paper outlines, analyzes and discusses results of an experimental study related to player´s engagement in affective applied adaptation. ; This study is part of the RAGE project. The RAGE project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 644187. This publication reflects only the author's view. The European Commission is not responsible for any use that may be made of the information it contains.

In recent years, many researchers have reported positive outcomes and effects from applying computer games to the educational process. The main preconditions for an effective game-based learning process include the presence of high learning interest and the desire to study hard. Therefore, educational game design has to tailor gameplay to the psycho-cognitive abilities, attitudes and skills of an individual player, in order to foster the player's motivation and creativity. To achieve this goal, it is necessary to draw a parallel between learning and playing styles, and to investigate the correlations between these types of constructs. The article presents a new playing style family based on Kolb's experiential learning theory that is appropriate to be used for educational games. This family is composed of four playing styles: Competitor, Dreamer, Logician and Strategist, and corresponds to Honey and Mumford's learning styles. To measure the four playing styles, a 40-item questionnaire was designed. In order to verify the consistency, validity, and reliability of that questionnaire as an accurate tool for recognizing the four suggested player styles, a pilot study was conducted. The article reports the results obtained from the study, along with their analysis and applicability. ; This study is part of the RAGE project. The RAGE project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 644187. This publication reflects only the author's view. The European Commission is not responsible for any use that may be made of the information it contains.

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. ; This study is part of the RAGE project. The RAGE project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 644187. This publication reflects only the author's view. The European Commission is not responsible for any use that may be made of the information it contains.