Design and Instantiation of an Interactive Multidimensional Ontology for Game Design Elements – a Design and Behavioral Approach

While games and play are commonly perceived as leisure tools, focus on the strategic implementation of isolated gameful elements outside of games has risen in recent years under the term gamification. Given their ease of implementation and impact in competitive games, a small set of game design elements, namely points, badges, and leaderboards, initially dominated research and practice. However, these elements reflect only a small group of components that game designers use to achieve positive outcomes in their systems. Current research has shifted towards focusing on the game design process instead of the isolated implementation of single elements under the term gameful design. But the problem of a tendency toward a monocultural selection of prominent design elements persists in-game and gameful design, preventing the method from reaching its full potential. This dissertation addresses this problem by designing and developing a digital, interactive game design element ontology that scholars and practitioners can use to make more informed and inspired decisions in creating gameful solutions to their problems. The first part of this work is concerned with the collation and development of the digital ontology. First, two datasets were collated from game design and gamification literature (game design elements and playing motivations). Next, four explorative studies were conducted to add user-relevant metadata and connect their items into an ontological structure. The first two studies use card sorting to assess game theory frameworks regarding their suitability as foundational categories for the game design element dataset and to gain an overview of different viewpoints from which categorizations can be derived. The second set of studies builds on an explorative method of matching dataset entries via their descriptive keywords to arrive at a connected graph. The first of these studies connects items of the playing motivations dataset with themselves, while the second connects them with an additional dataset of human needs. The first part closes with the documentation of the design and development of the tool Kubun, reporting on the outcome of its evaluation via iterative expert interviews and a field study. The results suggest that the tool serves its preset goals of affording intuitive browsing for dedicated searches and serendipitous findings. While the first part of this work reports on the top-down development process of the ontology and related navigation tool, the second part presents an in-depth research of specific learning-oriented game design elements to complement the overall research goal through a complementary bottom-up approach. Therein, two studies on learning-oriented game design elements are reported regarding their effect on performance, long-term learning outcome, and knowledge transfer. The studies are conducted with a game dedicated to teaching correct waste sorting. The first study focuses on a reward-based game design element in terms of its motivatory effect on perfect play. The second study evaluates two learning-enhancing game design elements, repeat, and look-up, in terms of their contribution to a long-term learning outcome. The comprehensive insights gained through the in-depth research manifest in the design of a module dedicated to reporting research outcomes in the ontology. The dissertation concludes with a discussion on the studies’ varying limitations and an outlook on pathways for future research

Injustice and Balance in Pervasive Video Games

Sendo um meio artistico intimamente relacionado com o avanço da tecnologia, alguns dos video-jogos mais interessantes são feitos através da expansão dos limites técnicos do que é considerado um jogo. Tal como o salto de gráficos em 2D para 3D, o impacto da internet em jogos multi-player e a inter-conectividade entre jogadores, ou a forma como a realidade virtual e a realidade aumentada têm mudado o que é possível dentro de um mundo virtual, há um outro género que tem aumentado em popularidade com a chegada de tecnologia nova - jogos pervasivos. O género de jogos pervasivos engloba jogos que misturam o mundo virtual do jogo e o mundo real através dos dados contextuais e de localização do jogador, servindo-se dos novos avanços tecnológicos acerca de tecnologia móvel. O fenómeno de Pokémon GO liderou a quebra do género para o mainstream, permitindo a que muitos outros jogos semelhantes tenham sucesso e obtendo para si uma audiência mundial considerável. Ao contrário de jogos de realidade aumentada - que esperam que o jogador veja o mundo através de uma câmara e a ela aplica os elementos do mundo virtual - os jogos pervasivos usam dados do mundo real como a sua base de construção. Existe bastante junção entre estes dois géneros de jogo, como vendo os dinossauros do Jurassic Park Alive através do telemóvel após os encontrar num parque local. Um grande problema presente nos jogos pervasivos é central ao aspeto que os torna únicos: devido a serem tão dependentes do contexto e localização do jogador, jogos pervasivos têm maior tendência para perconceitos que tornam alguns contextos melhores que outros. Um exemplo relevante é como no Pokémon GO, jogadores em zonas rurais têm uma quantidade reduzida ou nula de geração de Pokémons, enquanto jogadores em grandes cidades têm uma enchente constante de novos monstros para apanhar. Para resolver este problema, esta dissertação tem como objetivo desenvolver uma plataforma de análise de dados para jogos pervasivos que permita aos desenvolvedores obter um parecer relativamente ao balanço do seu jogo. Este objetivo será alcançado fazendo um cruzamento entre os dados do jogador e os contextuais, servindo-se de técnicas de aprendizagem automática para entender o que está a funcionar no jogo e o que não está. Esta tese não tem apenas a finalidade de oferecer aos desenvolvedores uma ferramenta que permita melhorar o balanço de jogos pré-existentes, mas sim resolver um problema significativo que tem estado a impedir o avanço e a experimentação de jogos pervasivos com experiências mais complexas e profundas.Being an art medium closely tied with the advancement of technology, some of the most interesting video games get made by expanding the technical limits of play in many new ways. Just like the jump from 2D graphics to 3D, how the internet shaped multiplayer games and inter-connectivity between players, and the way that now Virtual Reality and Augmented Reality are changing what is possible in a virtual world, another game genre has grown in popularity with the advent of new, exciting technology - pervasive games. The genre of pervasive games encompasses games that merge the game's virtual world and the real world together by taking advantage of the player's location data and contextual information, using the new leaps in technology regarding mobile internet. The phenomenon of Pokémon GO spearheaded the genre's break into the mainstream, allowing many other similar games to thrive and carving for itself a really large audience worldwide. Unlike augmented reality games - that expect you to see the world through a camera, that it then applies elements of the virtual world to - pervasive games uses the data from the real world as its base. There is a lot of cross-over between these two genres, like seeing the dinosaurs in Jurassic Park Alive through your phone after finding them in a local park. A big issue with pervasive games is central to its claim-to-fame: due to being so context and location-dependent, pervasive games are bound to have biases that make some contexts much better than others. A prime example of this is how in Pokémon GO, players in rural areas have nearly no Pokémon spawns, while players in cities have a constant stream of new monsters to catch. To solve this, this dissertation aims to develop an analytics platform for pervasive games that allows developers to obtain feedback on their game's balance. This goal will be achieved by cross-referencing player data and context data, using machine learning techniques to discover what works and what doesn't. This work's goal isn't simply to offer a tool to developers that help balance their games but also to help solve a significant issue holding pervasive games back from experimenting with more complex and deep experiences

Advanced Threat Intelligence: Interpretation of Anomalous Behavior in Ubiquitous Kernel Processes

Targeted attacks on digital infrastructures are a rising threat against the confidentiality, integrity, and availability of both IT systems and sensitive data. With the emergence of advanced persistent threats (APTs), identifying and understanding such attacks has become an increasingly difficult task. Current signature-based systems are heavily reliant on fixed patterns that struggle with unknown or evasive applications, while behavior-based solutions usually leave most of the interpretative work to a human analyst. This thesis presents a multi-stage system able to detect and classify anomalous behavior within a user session by observing and analyzing ubiquitous kernel processes. Application candidates suitable for monitoring are initially selected through an adapted sentiment mining process using a score based on the log likelihood ratio (LLR). For transparent anomaly detection within a corpus of associated events, the author utilizes star structures, a bipartite representation designed to approximate the edit distance between graphs. Templates describing nominal behavior are generated automatically and are used for the computation of both an anomaly score and a report containing all deviating events. The extracted anomalies are classified using the Random Forest (RF) and Support Vector Machine (SVM) algorithms. Ultimately, the newly labeled patterns are mapped to a dedicated APT attacker–defender model that considers objectives, actions, actors, as well as assets, thereby bridging the gap between attack indicators and detailed threat semantics. This enables both risk assessment and decision support for mitigating targeted attacks. Results show that the prototype system is capable of identifying 99.8% of all star structure anomalies as benign or malicious. In multi-class scenarios that seek to associate each anomaly with a distinct attack pattern belonging to a particular APT stage we achieve a solid accuracy of 95.7%. Furthermore, we demonstrate that 88.3% of observed attacks could be identified by analyzing and classifying a single ubiquitous Windows process for a mere 10 seconds, thereby eliminating the necessity to monitor each and every (unknown) application running on a system. With its semantic take on threat detection and classification, the proposed system offers a formal as well as technical solution to an information security challenge of great significance.The financial support by the Christian Doppler Research Association, the Austrian Federal Ministry for Digital and Economic Affairs, and the National Foundation for Research, Technology and Development is gratefully acknowledged

Privacy as a Public Good

Privacy is commonly studied as a private good: my personal data is mine to protect and control, and yours is yours. This conception of privacy misses an important component of the policy problem. An individual who is careless with data exposes not only extensive information about herself, but about others as well. The negative externalities imposed on nonconsenting outsiders by such carelessness can be productively studied in terms of welfare economics. If all relevant individuals maximize private benefit, and expect all other relevant individuals to do the same, neoclassical economic theory predicts that society will achieve a suboptimal level of privacy. This prediction holds even if all individuals cherish privacy with the same intensity. As the theoretical literature would have it, the struggle for privacy is destined to become a tragedy. But according to the experimental public-goods literature, there is hope. Like in real life, people in experiments cooperate in groups at rates well above those predicted by neoclassical theory. Groups can be aided in their struggle to produce public goods by institutions, such as communication, framing, or sanction. With these institutions, communities can manage public goods without heavy-handed government intervention. Legal scholarship has not fully engaged this problem in these terms. In this Article, we explain why privacy has aspects of a public good, and we draw lessons from both the theoretical and the empirical literature on public goods to inform the policy discourse on privacy

Improving serious games evaluation by applying learning analytics and data mining techniques

Tesis inédita de la Universidad Complutense de Madrid, Facultad de Informática, Departamento de Ingeniería del Software e Inteligencia Artificial, leída el 15/06/2017. Tesis formato europeo (compendio de artículos)Serious games are highly motivational resources effective to teach, raise awareness, or change the perceptions of players. To foster their application in education, teachers and institutions require clear and formal evidences to assess students' learning while they are playing the games. However, traditional assessment techniques rely on external questionnaires, typically carried out before and after playing, that fail to measure players' learning while it is happening. The multiple interactions carried out by players in the games can provide more precise information about how players play, and even be used to assess them. In this regard, game learning analytics techiques propose the collection and analysis of such interactions for multiple purposes, including assessment. The potentially large game learning analytics data collected can be further analyzed with data mining techniques to discover unexpected patterns and to provide measures to evaluate the effect of fames on their players and assess their learning...Los juegos serios son recursos altamente motivadores y efectivos para enseñar, concienciar, o cambiar las percepciones de sus jugadores. Para fomentar su aplicación en educación, los profesores y las instituciones necesitan pruebas claras y automáticas con las que evaluar el aprendizaje de sus estudiantes mientras utilizan los juegos. Tradicionalmente, la evaluación con juegos serios se basa en cuestionarios externos, realizados normalmente antes y después de jugar, que no miden el aprendizaje de los jugadores durante el proceso en sí. Las múltiples interacciones que realizan los jugadores al jugar pueden proporcionar una información más precisa sobre cómo juegan los jugadores e, incluso, utilizarse para evaluar su aprendizaje. En este sentido, las analíticas de aprendizaje para juegos proponen técnicas para la recogida y el análisis de dichas interacciones con múltiples fines, incluida la evaluación de los jugadores. Los datos (potencialmente numerosos) de las analíticas de aprendizaje para juegos pueden analizarse en mayor detalle con técnicas d minería de datos que permiten descubrir patrones ocultos a simple vista y proporcionar mejores medidas para estudiar el efecto de los juegos en los estudiantes y evaluar su aprendizaje...Fac. de InformáticaTRUEunpu

Proceedings of Mathsport international 2017 conference

Proceedings of MathSport International 2017 Conference, held in the Botanical Garden of the University of Padua, June 26-28, 2017. MathSport International organizes biennial conferences dedicated to all topics where mathematics and sport meet. Topics include: performance measures, optimization of sports performance, statistics and probability models, mathematical and physical models in sports, competitive strategies, statistics and probability match outcome models, optimal tournament design and scheduling, decision support systems, analysis of rules and adjudication, econometrics in sport, analysis of sporting technologies, financial valuation in sport, e-sports (gaming), betting and sports

CHORUS Deliverable 2.1: State of the Art on Multimedia Search Engines

Based on the information provided by European projects and national initiatives related to multimedia search as well as domains experts that participated in the CHORUS Think-thanks and workshops, this document reports on the state of the art related to multimedia content search from, a technical, and socio-economic perspective. The technical perspective includes an up to date view on content based indexing and retrieval technologies, multimedia search in the context of mobile devices and peer-to-peer networks, and an overview of current evaluation and benchmark inititiatives to measure the performance of multimedia search engines. From a socio-economic perspective we inventorize the impact and legal consequences of these technical advances and point out future directions of research

Data-Driven Analytics for Decision Making in Game Sports

Performance analysis and good decision making in sports is important to maximize chances of winning. Over the last years the amount and quality of data which is available for the analysis has increased enormously due to technical developments like, e.g., of sensor technologies or computer vision technology. However, the data-driven analysis of athletes and team performances is very demanding. One reason is the so called semantic gap of sports analytics. This means that the concepts of coaches are seldomly represented in the data for the analysis. Furthermore, sports in general and game sports in particular present a huge challenge due to its dynamic characteristics and the multi-factorial influences on an athlete’s performance like, e.g., the numerous interaction processes during a match. This requires different types of analyses like, e.g., qualitative analyses and thus anecdotal descriptions of performances up to quantitative analyses with which performances can be described through statistics and indicators. Additionally, coaches and analysts have to work under an enormous time pressure and decisions have to be made very quickly. In order to facilitate the demanding task of game sports analysts and coaches we present a generic approach how to conceptualize and design a Data Analytics System (DAS) for an efficient support of the decision making processes in practice. We first introduce a theoretical model and present a way how to bridge the semantic gap of sports analytics. This ensures that DASs will provide relevant information for the decision makers. Moreover, we show that DASs need to combine qualitative and quantitative analyses as well as visualizations. Additionally, we introduce different query types which are required for a holistic retrieval of sports data. We furthermore show a model for the user-centered planning and designing of the User Experience (UX) of a DAS. Having introduced the theoretical basis we present SportSense, a DAS to support decision making in game sports. Its generic architecture allows a fast adaptation to the individual characteristics and requirements of different game sports. SportSense is novel with respect to the fact that it unites raw data, event data, and video data. Furthermore, it supports different query types including an intuitive sketch-based retrieval and seamlessly combines qualitative and quantitative analyses as well as several data visualization options. Moreover, we present the two applications SportSense Football and SportSense Ice Hockey which contain sport-specific concepts and cover (high-level) tactical analyses

The impact of learner metacognition and goal orientation on problem-solving in a serious game environment

To understand the impact of two learner characteristics—metacognition and goal orientation—on problem-solving, this study investigated 159 undergraduate learners’ metacognition, goal orientations, and problem-solving performances and processes in a laboratory setting using a Serious Game (SG) environment—Alien Rescue (AR)—that adopts Problem-based Learning (PBL) pedagogy for teaching space science. Utilizing multiple data sources, including computer log data and problem-solving solution scores within the SG, survey data, gameplay screencast videos, and interview data, this study combined a sequential mixed method design and serious games analytics techniques to answer the following two questions: (a) To what extent are learner problem-solving performance differences based on learner characteristics, and why? (b) To what extent are learner problem-solving process differences based on learner characteristics, and why? The results indicated that (a) learner metacognition affected problem-solving. Specifically, there were statistically significant differences in learner problem-solving performances based on metacognition, and learners also demonstrated different problem-solving processes based on metacognition. (b) Learner goal orientation impacted problem-solving. Particularly, learners in different goal orientation groups had different problem-solving processes. (c) The interaction between metacognition and goal orientations had an impact on learner problem-solving performances. Specifically, learners were clustered into three groups based on these two characteristics, including (a) high metacognition and high multiple goal orientations, (b) low metacognition and medium multiple goal orientations, and (c) medium metacognition and low multiple goal orientations. Learner problem-solving performances were statistically significant based on these three clusters. In addition, learner metacognition and goal orientations together could predict learner problem-solving performances. (d) The interaction between metacognition and goal orientations also had an impact on learner problem-solving processes. These differences in learner problem-solving performances and processes can be explained by learner characteristic differences, the problem complexity, SG design, and Dunning-Kruger effects (i.e., the cognitive bias that people of low metacognitive ability might mistakenly assess their metacognitive level as higher than it is). In addition, this study summarized 10 steps of how to be a successful and efficient problem solver in AR. These steps are as follows: 1) identify the problem correctly; 2) explore the 3D environment by visiting all rooms in AR and look over all tools; 3) discover what one alien species needs to survive in Alien Database; 4) search the Solar System Database for possible planets; 5) develop hypotheses about where this alien species can live; 6) figure out if there is any missing information needed for making a decision; 7) launch probes to gather information in the Probe Design room; 8) check the data from the probe in the Mission Control room; 9) decide whether the selected planet is a good choice for the selected alien species; 10) if so, write a recommendation message with the justification in the Communication Center—if not, go back to step 4. This research offers additional understanding of learner characteristic impacts on problem-solving in SG environments with PBL pedagogy. It can also contribute to future designs of these environments to benefit learners based on their metacognitive levels. In addition, the study limitations and further research in this area are discussed.Curriculum and Instructio