Towards a Lightweight Approach for Modding Serious Educational Games: Assisting Novice Designers

Serious educational games (SEGs) are a growing segment of the education community’s pedagogical toolbox. Effectively creating such games remains challenging, as teachers and industry trainers are content experts; typically they are not game designers with the theoretical knowledge and practical experience needed to create a quality SEG. Here, a lightweight approach to interactively explore and modify existing SEGs is introduced, a toll that can be broadly adopted by educators for pedagogically sound SEGs. Novice game designers can rapidly explore the educational and traditional elements of a game, with a stress on tracking the SEG learning objectives, as well as allowing for reviewing and altering a variety of graphic and audio game elements

+SPACES: Serious Games for Role-Playing Government Policies

The paper explores how role-play simulations can be used to support policy discussion and refinement in virtual worlds. Although the work described is set primarily within the context of policy formulation for government, the lessons learnt are applicable to online learning and collaboration within virtual environments. The paper describes how the +Spaces project is using both 2D and 3D virtual spaces to engage with citizens to explore issues relevant to new government policies. It also focuses on the most challenging part of the project, which is to provide environments that can simulate some of the complexities of real life. Some examples of different approaches to simulation in virtual spaces are provided and the issues associated with them are further examined. We conclude that the use of role-play simulations seem to offer the most benefits in terms of providing a generalizable framework for citizens to engage with real issues arising from future policy decisions. Role-plays have also been shown to be a useful tool for engaging learners in the complexities of real-world issues, often generating insights which would not be possible using more conventional techniques

Procedural and semantic modeling of virtual environments for serious games development

International audienceVirtual environments are useful tools for visualization, discovery as well as training. In serious or learning games contexts, 3D graphical worlds, interaction, navigation and immersion capabilities are needed to propel narration and emotion. Furthermore, they are key elements to materialize pedagogical content and to support knowledge transfer. Semantic modeling, serious game classification and gameplay component identification allow generating serious game scenarios linked to the 3D world modeling and interaction or animation capabilities

Serious Game Evaluation as a Meta-game

Purpose – This paper aims to briefly outline the seamless evaluation approach and its application during an evaluation of ORIENT, a serious game aimed at young adults. Design/methodology/approach – In this paper, the authors detail a unobtrusive, embedded evaluation approach that occurs within the game context, adding value and entertainment to the player experience whilst accumulating useful data for the development team. Findings – The key result from this study was that during the “seamless evaluation” approach, users were unaware that they had been participating in an evaluation, with instruments enhancing rather than detracting from the in-role game experience. Practical implications – This approach, seamless evaluation, was devised in response to player expectations, perspectives and requirements, recognising that in the evaluation of games the whole process of interaction including its evaluation must be enjoyable and fun for the user. Originality/value – Through using seamless evaluation, the authors created an evaluation completely embedded within the “magic circle” of an in-game experience that added value to the user experience whilst also yielding relevant results for the development team

Using motivation derived from computer gaming in the context of computer based instruction

This paper was originally presented at the IEEE Technically Sponsored SAI Computing Conference 2016, London, 13-15 July 2016. Abstract— this paper explores how to exploit game based motivation as a way to promote engagement in computer-based instruction, and in particular in online learning interaction. The paper explores the human psychology of gaming and how this can be applied to learning, the computer mechanics of media presentation, affordances and possibilities, and the emerging interaction of playing games and how this itself can provide a pedagogical scaffolding to learning. In doing so the paper focuses on four aspects of Game Based Motivation and how it may be used; (i) the game player’s perception; (ii) the game designers’ model of how to motivate; (iii) team aspects and social interaction as a motivating factor; (iv) psychological models of motivation. This includes the increasing social nature of computer interaction. The paper concludes with a manifesto for exploiting game based motivation in learning

An Overview of Self-Adaptive Technologies Within Virtual Reality Training

This overview presents the current state-of-the-art of self-adaptive technologies within virtual reality (VR) training. Virtual reality training and assessment is increasingly used for five key areas: medical, industrial & commercial training, serious games, rehabilitation and remote training such as Massive Open Online Courses (MOOCs). Adaptation can be applied to five core technologies of VR including haptic devices, stereo graphics, adaptive content, assessment and autonomous agents. Automation of VR training can contribute to automation of actual procedures including remote and robotic assisted surgery which reduces injury and improves accuracy of the procedure. Automated haptic interaction can enable tele-presence and virtual artefact tactile interaction from either remote or simulated environments. Automation, machine learning and data driven features play an important role in providing trainee-specific individual adaptive training content. Data from trainee assessment can form an input to autonomous systems for customised training and automated difficulty levels to match individual requirements. Self-adaptive technology has been developed previously within individual technologies of VR training. One of the conclusions of this research is that while it does not exist, an enhanced portable framework is needed and it would be beneficial to combine automation of core technologies, producing a reusable automation framework for VR training

MASELTOV Deliverable Report 7.1.2: Incidental Learning Framework

This document describes progress in the development of an ‘Incidental Learning Framework’, building on the work reported in Deliverable D7.1.1, submitted July 2012. The goal of the Incidental Learning Framework (ILF) is to facilitate the creation of technology rich learning opportunities for immigrants within cities. The framework is a descriptive mechanism that permits analysis, and a generative tool to support software system design, and it facilitates the communication of learning design ideas both visually and textually. The framework focuses on incidental learning i.e. learning that is spontaneous and unplanned, in the knowledge domains of interest to the MASELTOV project including health care, culture, and language and information access. Its use should encourage links and triggers to structured and reflective learning to back up and deepen learning that happens incidentally. This document describes the Incidental Learning Framework developed for the MASELTOV project, presents a examples of its use, and describes some conclusions and recommendations for future work - Introduction - Purpose of the framework - Challenges with ILF from its initial conception - Work carried out developing ILF for use in the project --Literature --Alternative visualisations --Focus workshops in OU --Partner testing --Template for testing --Examples of the partners’ testing their tools against the template -Reporting on incidental learning reflections with language learning and serious games - Conclusions and recommendations It should be noted that this document is a high level review, identifying significant literature and the on-going development of the framework through dialogue with educational experts and MASELTOV partners. This document offers recommendations therefore in general terms. Decisions about the specific implementation of the learner’s journey as framed by an incidental learning approach will be made in coordination with technical partners as the dialogue progresses

Towards a data-driven approach to scenario generation for serious games

Serious games have recently shown great potential to be adopted in many applications, such as training and education. However, one critical challenge in developing serious games is the authoring of a large set of scenarios for different training objectives. In this paper, we propose a data-driven approach to automatically generate scenarios for serious games. Compared with other scenario generation methods, our approach leverages on the simulated player performance data to construct the scenario evaluation function for scenario generation. To collect the player performance data, an artificial intelligence (AI) player model is designed to imitate how a human player behaves when playing scenarios. The AI players are used to replace human players for data collection. The experiment results show that our data-driven approach provides good prediction accuracy on scenario’s training intensities. It also outperforms our previous heuristic-based approach in its capability of generating scenarios that match closer to specified target player performance