Boyan Bontchev

Software Construction of an Authoring Tool for Adaptive E-learning Platforms

In last decade, more and more platforms for e-learning content delivery provide adaptability towards learners goals, styles and performance. Usually, such platforms rely on own authoring tool or use external one in order to create learning materials. Usually, these tools follow modern e-learning standards but are rather complicated to be used and miss interoperability features. In this paper, we present software construction of an authoring tool, which is a part of a platform for building edutainment (education plus entertainment) services – ADOPTA (ADaptive technOlogy-enhanced Platform for eduTAinment). This authoring tool is designed by using Java Enterprise Edition platform and provides inheritance mechanisms for learning object metadata descriptions, metadata for semantic ontology graphs, and good integration with instructor tool for creation of adaptive courseware.

Architectural design of a software engine for adaptation control in the ADOPTA e-learning platform

In recent years, the field of adaptive web-based systems becomes one of the key research e-learning areas. Efforts are focused on tools for creating and managing content that on the one hand can be adapted to various goals, preferences, knowledge and learning style of a learner and, on the other hand, can be reused by various authors and exchanged between different systems. Our article discusses a proposal for software engine construction to adaptable content delivery and to adaptive process management. This adaptation control engine is a part of the ADOPTA - adaptive technology-enhanced platform for edutainment. Our approach is based on web services that enables, besides reusing and exchanging of learning content, also reusing of application functionality.

Courseware Authoring for Adaptive E-learning

In recent years, the number of adaptive applications for e-learning content delivery increased immensely. Usually, such applications have own authoring tool or use external one in order to create learning materials. However, very few authors of educational content use such systems. The reason is that the tools are rather complicated and follow modern elearning standards, which leads to a need for content authors to fill multiple metadata for theirs learning materials, that requires much time. In this paper we present an authoring tool, which is a part of a platform for building edutainment (education plus entertainment) services – ADOPTA (ADaptive technOlogy-enhanced eduTainment platform). This authoring tool provides inheritance mechanisms for learning object metadata descriptions, metadata for semantic ontologies, and good integration with instructor tool for creation of adaptive courseware.

Courseware Adaptation to Learning Styles and Knowledge Level

According to initial design of adaptive e-learning, content of an adaptive course should be suitable for students with different profiles (Brusilovsky, 1996). These profiles may contain information about goals, preferences, knowledge level, learning style, rendering psychological profile, and more. Typically, the learning content is developed for some groups of students that have similar values of one or several parameters of the student’s profile. For more groups of students an adaptive course is designed, the more personalized it is.

Affect-based adaptation of an applied video game for educational purposes

Purpose This paper aims to clarify how affect-based adaptation can improve implicit recognition of playing style of individuals during game sessions. This study presents the “Rush for Gold” game using dynamic difficulty adjustment of tasks based on both player performance and affectation inferred through electrodermal activity and facial expressions of the player. The game applies linear regression for calculating playing styles to be applied for achieving a style-based adaptation in other educational video games. Design/methodology/approach The experimental procedure included subject selection, demonstration, informed consent procedure, two game sessions in random order – one without and another with affective adaptation control – and post-game self-report. The experiment was conducted with participation of 30 master students and university lecturers in informatics. Findings This study presents experimental results concerning the impact of affective adaptation over playing style recognition, game session time, task’s effectiveness, efficiency and difficulty and, as well, player’s assessment of affectively adaptive gameplay obtained by an adaptation control panel embedded into the game and by post-game self-report. Research limitations/implications The proposed adaptive game limits recognised styles to such based on the Kolb’s Learning Style Inventory model. Another limitation of the study is the relatively small number of participants constrained by the extended experimental procedure and the desktop game version. Originality/value The paper presents an original research on the effect of affect-based adaptation on a novel approach for implicit recognition of playing styles.

Playing style recognition through an adaptive video game

Abstract Playing style recognition is crucially important for style-based adaptation of digital games. Unlike traditional ways for measuring of styles by means of self-reports, automatic style estimation incorporated into a video game appears to be a more efficient and ecologically valid method. The article presents a model for in-game recognition of four playing styles (Competitor, Dreamer, Logician, and Strategist) based on the Kolbs experiential learning theory. The model applies multiple linear regression over task performance metrics as explanatory variables and coefficients found first by a heuristic approach relaying on experience and observation knowledge of domain experts and, next, estimated by the least squares method. Experiments with the model implemented within an affectively adaptive video game demonstrated the benefits of emotion-based dynamic difficulty adjustment over playing outcomes and proved its validity as an accurate instrument for automatic estimation of both the four playing styles and the learning styles of Honey and Mumford.

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.

DYNAMIC GAME ADAPTATION BASED ON DETECTION OF BEHAVIORAL PATTERNS IN THE PLAYER LEARNING CURVE

In educational serious games, the learning curve of a player represents his/her progress in acquiring cognitive abilities and new knowledge necessary for solving the game challenges. Hence, it is very important for an adaptive serious game to have a mechanism for detection of patterns of player´s learning curve in playing time. The paper presents the application of a game adaptation method based on automatic and dynamically detection of specific learning curves at runtime 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 within the scope of the RAGE (Realising and Applied Gaming Ecosystem) H2020 project. The component is incorporated into a 3D car driving video game in order to enable a dynamic detection of different patterns of player performance. It allows the video game scenario to be adapted to each player by providing appropriately for him/her challenges and game features. We carried out a practical experiment with students from Sofia University, Bulgaria, where we found that adaptation of game difficulty by applying the RAGE software component resulted to improved game playability.

Playing styles based on experiential learning theory

Abstract In recent years, researchers have reported positive outcomes and effects from applying computer games to the educational process. The preconditions for an effective game-based learning process include the presence of high learning interest and the desire to study hard. Therefore, educational video game design has to tailor gameplay to the style of the playing learner, i.e. to the psycho-cognitive abilities, attitudes, and skills of the individual player, in order to foster the players motivation and creativity. To achieve this goal, it is necessary to draw a parallel between learning styles and styles of playing video games, and to investigate the correlations between these types of constructs. The article presents a new family of playing styles based on Kolbs experiential learning theory that is appropriate to be used for educational video games. This family is composed of four playing styles: Competitor, Dreamer, Logician, and Strategist, and corresponds to Honey and Mumfords learning styles based also on the theory of experiential learning, namely Activist, Reflector, Theorist, and Pragmatist. 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.

The RAGE Advanced Game Technologies Repository for Supporting Applied Game Development

This paper describes the structural architecture of the RAGE repository, which is a unique and dedicated infrastructure that provides access to a wide variety of advanced technologies (RAGE software assets) for applied game development. These software assets are reusable across a wide diversity of game engines, game platforms and programming languages. The RAGE repository allows applied game developers and studios to search for software assets for inclusion in applied games. The repository is designed as an asset life-cycle management system for defining, publishing, updating, searching and packaging for distribution of these assets. The RAGE repository provides storage space for assets and their artefacts. It will be embedded in a social platform for networking among asset developers and other users. A dedicated Asset Repository Manager provides the main functionality of the repository and its integration with other systems. Tools supporting the Asset Manager are presented and discussed. When the RAGE repository is in full operation, applied game developers will be able to easily enhance the quality of their games by including advanced game technology assets.