Game-based e-learning applications of e-tester

Adaptive E-Learning systems may be a supportive solution for a wide range of learning applications. In order to complement common learning paradigms in computer-based education the digital game-based learning paradigm is an interesting alternative, through which we can increase motivation, intrigue learners for a new or a previously boring subject, and provide another, more relaxing environment for self-assessment and testing. Despite of these advantages, one main shortcoming is that the creation of game stories and curriculum-relevant game content is an enormous workload for teachers and learning content providers. In order to reduce the learning game creation effort, we propose an enhanced digital game-based learning approach by applying a flexible game shell in combination with auto-generated questions based on the course content and automatic assessment of natural language answers. On the basis of requirements within the AdeLE research project and application scenarios, our development approach as well as the first prototype implementation are highlighted. First experiences and tests based on the prototype implementation are promising

Rapid prototyping and fast user trial of multimedia broadcast and cellular services

This paper presents the results of fast user trial of multimedia services that are enabled when a mobile terminal has access to converged services over digital broadcast and mobile telecommunications networks. It first describes the motivations behind developing this system and describes the service scenarios that benefit most from it. It then provides an overview of the service components of the test case scenario. Finally, it presents the results of fast user trials on end users of the services that were developed. This work was conducted as part of the EU-funded CISMUNDUS project

Enhancing automatic level generation for platform videogames

This dissertation addresses the challenge of improving automatic level generation processes for plat-form videogames. As Procedural Content Generation (PCG) techniques evolved from the creation of simple elements to the construction of complete levels and scenarios, the principles behind the generation algorithms became more ambitious and complex, representing features that beforehand were only possible with human design. PCG goes beyond the search for valid geometries that can be used as levels, where multiple challenges are represented in an adequate way. It is also a search for user-centred design content and the creativity sparks of humanly created content. In order to improve the creativity capabilities of such generation algorithms, we conducted part of our research directed to the creation of new techniques using more ambitious design patterns. For this purpose, we have implemented two overall structure generation algorithms and created an addi-tional adaptation algorithm. The later can transform simple branched paths into more compelling game challenges by adding items and other elements in specific places, such as gates and levers for their activation. Such approach is suitable to avoid excessive level linearity and to represent certain design patterns with additional content richness. Moreover, content adaptation was transposed from general design domain to user-centred principles. In this particular case, we analysed success and failure patterns in action videogames and proposed a set of metrics to estimate difficulty, taking into account that each user has a different perception of that concept. This type of information serves the generation algorithms to make them more directed to the creation of personalised experiences. Furthermore, the conducted research also aimed to the integration of different techniques into a common ground. For this purpose, we have developed a general framework to represent content of platform videogames, compatible with several titles within the genre. Our algorithms run over this framework, whereby they are generic and game independent. We defined a modular architecture for the generation process, using this framework to normalise the content that is shared by multiple modules. A level editor tool was also created, which allows human level design and the testing of automatic generation algorithms. An adapted version of the editor was implemented for the semi-automatic creation of levels, in which the designer may simply define the type of content that he/she desires, in the form of quests and missions, and the system creates a corresponding level structure. This materialises our idea of bridging human high-level design patterns with lower level automated generation algorithms. Finally, we integrated the different contributions into a game prototype. This implementation allowed testing the different proposed approaches altogether, reinforcing the validity of the proposed archi-tecture and framework. It also allowed performing a more complete gameplay data retrieval in order to strengthen and validate the proposed metrics regarding difficulty perceptions

Heuristic usability evaluation on games: a modular approach

Heuristic evaluation is the preferred method to assess usability in games when experts conduct this evaluation. Many heuristics guidelines have been proposed attending to specificities of games but they only focus on specific subsets of games or platforms. In fact, to date the most used guideline to evaluate games usability is still Nielsen’s proposal, which is focused on generic software. As a result, most evaluations do not cover important aspects in games such as mobility, multiplayer interactions, enjoyability and playability, etc. To promote the usage of new heuristics adapted to different game and platform aspects we propose a modular approach based on the classification of existing game heuristics using metadata and a tool, MUSE (Meta-heUristics uSability Evaluation tool) for games, which allows a rebuild of heuristic guidelines based on metadata selection in order to obtain a customized list for every real evaluation case. The usage of these new rebuilt heuristic guidelines allows an explicit attendance to a wide range of usability aspects in games and a better detection of usability issues. We preliminarily evaluate MUSE with an analysis of two different games, using both the Nielsen’s heuristics and the customized heuristic lists generated by our tool.Unión Europea PI055-15/E0

Choosers: The design and evaluation of a visual algorithmic music composition language for non-programmers

Algorithmic music composition involves specifying music in such a way that it is non-deterministic on playback, leading to music which has the potential to be different each time it is played. Current systems for algorithmic music composition typically require the user to have considerable programming skill and may require formal knowledge of music. However, much of the potential user population are music producers and musicians (some professional, but many amateur) with little or no programming experience and few formal musical skills. To investigate how this gap between tools and potential users might be better bridged we designed Choosers, a prototype algorithmic programming system centred around a new abstraction (of the same name) designed to allow non-programmers access to algorithmic music composition methods. Choosers provides a graphical notation that allows structural elements of key importance in algorithmic composition (such as sequencing, choice, multi-choice, weighting, looping and nesting) to be foregrounded in the notation in a way that is accessible to non-programmers. In order to test design assumptions a Wizard of Oz study was conducted in which seven pairs of undergraduate Music Technology students used Choosers to carry out a range of rudimentary algorithmic composition tasks. Feedback was gathered using the Programming Walkthrough method. All users were familiar with Digital Audio Workstations, and as a result they came with some relevant understanding, but also with some expectations that were not appropriate for algorithmic music work. Users were able to successfully make use of the mechanisms for choice, multi-choice, looping, and weighting after a brief training period. The ‘stop’ behaviour was not so easily understood and required additional input before users fully grasped it. Some users wanted an easier way to override algorithmic choices. These findings have been used to further refine the design of Choosers