________________________________________________________________________In the domain of computer games, research into the interaction between player and game has centred on 'enjoyment', often drawing in particular on optimal experience research and Csikszentmihalyi's 'Flow theory'. Flow is a well-established construct for examining experience in any setting and its application to game-play is intuitive. Nevertheless, it's not immediately obvious how to translate between the flow construct and an operative description of game-play. Previous research has attempted this translation through analogy. In this article we propose a practical, integrated approach for analysis of the mechanics and aesthetics of game-play, which helps develop deeper insights into the capacity for flow within games.The relationship between player and game, characterized by learning and enjoyment, is central to our analysis. We begin by framing that relationship within Cowley's user-system-experience (USE) model, and expand this into an information systems framework, which enables a practical mapping of flow onto game-play. We believe this approach enhances our understanding of a player's interaction with a game and provides useful insights for games' researchers seeking to devise mechanisms to adapt game-play to individual players.

Background The exploitation of synthetic data in health care is at an early stage. Synthetic data could unlock the potential within health care datasets that are too sensitive for release. Several synthetic data generators have been developed to date; however, studies evaluating their efficacy and generalizability are scarce. Objective This work sets out to understand the difference in performance of supervised machine learning models trained on synthetic data compared with those trained on real data. Methods A total of 19 open health datasets were selected for experimental work. Synthetic data were generated using three synthetic data generators that apply classification and regression trees, parametric, and Bayesian network approaches. Real and synthetic data were used (separately) to train five supervised machine learning models: stochastic gradient descent, decision tree, k-nearest neighbors, random forest, and support vector machine. Models were tested only on real data to determine whether a model developed by training on synthetic data can used to accurately classify new, real examples. The impact of statistical disclosure control on model performance was also assessed. Results A total of 92% of models trained on synthetic data have lower accuracy than those trained on real data. Tree-based models trained on synthetic data have deviations in accuracy from models trained on real data of 0.177 (18%) to 0.193 (19%), while other models have lower deviations of 0.058 (6%) to 0.072 (7%). The winning classifier when trained and tested on real data versus models trained on synthetic data and tested on real data is the same in 26% (5/19) of cases for classification and regression tree and parametric synthetic data and in 21% (4/19) of cases for Bayesian network-generated synthetic data. Tree-based models perform best with real data and are the winning classifier in 95% (18/19) of cases. This is not the case for models trained on synthetic data. When tree-based models are not considered, the winning classifier for real and synthetic data is matched in 74% (14/19), 53% (10/19), and 68% (13/19) of cases for classification and regression tree, parametric, and Bayesian network synthetic data, respectively. Statistical disclosure control methods did not have a notable impact on data utility. Conclusions The results of this study are promising with small decreases in accuracy observed in models trained with synthetic data compared with models trained with real data, where both are tested on real data. Such deviations are expected and manageable. Tree-based classifiers have some sensitivity to synthetic data, and the underlying cause requires further investigation. This study highlights the potential of synthetic data and the need for further evaluation of their robustness. Synthetic data must ensure individual privacy and data utility are preserved in order to instill confidence in health care departments when using such data to inform policy decision-making.

The authors of this paper are based at the University of Ulster in Northern Ireland. Darryl Charles specialises in computational intelligence for games and virtual worlds. Michael McNeill is interested in graphics algorithms and interaction within the same context. Therese Charles is currently completing her PhD studies in the area of game based learning, under the supervision of Dave Bustard and Michaela Black, who have an interest in innovative approaches to e-learning and teaching in general. AbstractIt is generally accepted that informative and timely feedback is important to a student's learning experience within higher education. In the study of commercial digital games it has also become increasingly understood that games are particularly good at providing effective feedback of this form to gameplayers. We discuss recent game based learning research that attempts to harness the motivating qualities of digital games to inform the design of educational technology. Results from this research demonstrate student participation and performance can be improved by providing Game-Based Feedback (GBF) to students. The GBF approach awards points to students for the successful completion of tasks throughout a course of study. Points and achievements accumulated over time builds a profile that provides a student with a potentially powerful representation of their educational identity. In this paper, we argue that virtual worlds are particularly suitable for this form of GBF and can further enhance a student's understanding of their educational standing. We outline a Virtual Learning Landscape (VLL) design that is embedded within a multi-user virtual environment, where educational feedback is supplied to students via their avatar and a virtual world's landscape. The core structural principles of the proposed VLL are explained and several examples of the use of the VLL are provided to illustrate the system. IntroductionIn comparison to secondary school, university can be a complex learning experience for students. In particular, students find that they have much more responsibility for their own learning. Degree courses have fewer timetabled classes and class sizes are often significantly larger. Typically, attendance, while encouraged, is not compulsory, and contact time with teachers is much less than they are used to. It may be argued that to be successful on a degree, a student needs to learn how to learn within the university context. They must understand that learning within a university is about understanding the processes and systems that help them build skills and knowledge. It is about forming good learning habits: good attendance, preparing for class, reflection, good communication with peers and teachers, reading around a topic, consistent work ethics, knowing who to go when in trouble, and other implicit institutional expectations. From a university teaching perspective, the focus is naturally placed on the communication of compul-

The European Union (EU) initiative on the Digital Transformation of Health and Care (Digicare) aims to provide the conditions necessary for building a secure, flexible, and decentralized digital health infrastructure. Creating a European Health Research and Innovation Cloud (HRIC) within this environment should enable data sharing and analysis for health research across the EU, in compliance with data protection legislation while preserving the full trust of the participants. Such a HRIC should learn from and build on existing data infrastructures, integrate best practices, and focus on the concrete needs of the community in terms of technologies, governance, management, regulation, and ethics requirements. Here, we describe the vision and expected benefits of digital data sharing in health research activities and present a roadmap that fosters the opportunities while answering the challenges of implementing a HRIC. For this, we put forward five specific recommendations and action points to ensure that a European HRIC: i) is built on established standards and guidelines, providing cloud technologies through an open and decentralized infrastructure; ii) is developed and certified to the highest standards of interoperability and data security that can be trusted by all stakeholders; iii) is supported by a robust ethical and legal framework that is compliant with the EU General Data Protection Regulation (GDPR); iv) establishes a proper environment for the training of new generations of data and medical scientists; and v) stimulates research and innovation in transnational collaborations through public and private initiatives and partnerships funded by the EU through Horizon 2020 and Horizon Europe.

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