+Spaces: Intelligent Virtual Spaces for eGovernment

Intelligent Environments most commonly take a physical form such as homes, offices, hotels, restaurants, shops, that are equipped with advanced networked computer based systems, which enable better or new lifestyles for people. However, Intelligent Environments can also take the form of virtual online spaces such as SecondLife, which can both mimic the real world and provide functionalities which could not be provided in reality, such as advanced simulations and movement. There is the growing trend for people to spend more time in such virtual environments and, to these ends, this work in progress paper reports on a new project, +Spaces which is developing a range of virtual world tools for e-government applications, and presents some of the concepts and technical challenges involved in creating these intelligent virtual spaces for e-government. © 2010 IEEE

Modeling Human Group Behavior In Virtual Worlds

Virtual worlds and massively-multiplayer online games are rich sources of information about large-scale teams and groups, offering the tantalizing possibility of harvesting data about group formation, social networks, and network evolution. They provide new outlets for human social interaction that differ from both face-to-face interactions and non-physically-embodied social networking tools such as Facebook and Twitter. We aim to study group dynamics in these virtual worlds by collecting and analyzing public conversational patterns of users grouped in close physical proximity. To do this, we created a set of tools for monitoring, partitioning, and analyzing unstructured conversations between changing groups of participants in Second Life, a massively multi-player online user-constructed environment that allows users to construct and inhabit their own 3D world. Although there are some cues in the dialog, determining social interactions from unstructured chat data alone is a difficult problem, since these environments lack many of the cues that facilitate natural language processing in other conversational settings and different types of social media. Public chat data often features players who speak simultaneously, use jargon and emoticons, and only erratically adhere to conversational norms. Humans are adept social animals capable of identifying friendship groups from a combination of linguistic cues and social network patterns. But what is more important, the content of what people say or their history of social interactions? Moreover, is it possible to identify whether iii people are part of a group with changing membership merely from general network properties, such as measures of centrality and latent communities? These are the questions that we aim to answer in this thesis. The contributions of this thesis include: 1) a link prediction algorithm for identifying friendship relationships from unstructured chat data 2) a method for identifying social groups based on the results of community detection and topic analysis. The output of these two algorithms (links and group membership) are useful for studying a variety of research questions about human behavior in virtual worlds. To demonstrate this we have performed a longitudinal analysis of human groups in different regions of the Second Life virtual world. We believe that studies performed with our tools in virtual worlds will be a useful stepping stone toward creating a rich computational model of human group dynamics

Ratings Use in an Online Discussion System: The Slashdot Case

Large-scale, online communication systems allow many-to-many interactions among users, which can result in a variety of positive outcomes. However, the prevalence of information overload and problems caused by the loss of shared communication channels in text-based environments may create barriers to realizing the benefits of online interactions. Attempts to manage online communication systems in the past depended on techniques that cannot be applied to, or do not allow, large-scale interactions. Slashdot is a large-scale, long-lasting Web discussion community that uses a form of recommendation system to provide feedback about the quality of comments users post to the site. This dissertation examines this novel approach to organizing an online communication system in terms of how users employ the ratings provided by the system, whether comment ratings have an effect on how new users of the site participate, and how users making recommendations about content actually provide ratings. I find that users do employ ratings to change how they view content, but that there is some resistance that prevents them from doing so readily. To overcome this friction, I recommend dynamic changes based on the choices of other users who seem more willing to make interface changes based on comment ratings. I also find that new user participation on Slashdot is affected by feedback on the initial comment made by the new member, but that user observation is just as important in determining how the new member will participate in the future. Finally, I find that ratings are being sufficiently applied to comments, but that some comments are not receiving fair attention because of when or where they are posted within the online discussion. The overall conclusions of this work are that pre-rating content helps to relieve the pressure of attaining sufficient ratings on comments, that rating labels provide valuable feedback for customizing how users with different motivations may read comments, and that comments ratings positively affect user experiences in a large, online discussion system. The Slashdot case shows how the use of recommendations in an online discussion system creates organization that ameliorates the problems of information overload and loss of communication channels, while still allowing for large-scale, heterogeneous interactions.Ph.D.InformationUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/39369/2/lampe_diss_revised.pd

Personalized Game Content Generation and Recommendation for Gamified Systems

Gamification, that is, the usage of game content in non-game contexts, has been successfully employed in several application domains to foster engagement, as well as to influence the behavior of end users. Although gamification is often effective in inducing behavioral changes in citizens, the difficulty in retaining players and sustaining the acquired behavior over time, shows some limitations of this technology. That is especially unfortunate, because changing players’ demeanor (which have been shaped for a long time), cannot be immediately internalized; rather, the gamification incentive must be reinforced to lead to stabilization. This issue could be sourced from utilizing static game content and a one-size-fits-all strategy in generating the content during the game. This reveals the need for dynamic personalization over the course of the game. Our research hypothesis is that we can overcome these limitations with Procedural Content Generation (PCG) of playable units that appeal to each individual player and make her user experience more varied and compelling. In this thesis, we propose a deep, large and long solution, deployed in two main phases of Design and Integration to tackle these limitations. To support the former phase, we present a “PCG and Recommender system” to automate the generation and recommendation of playable units, named “Challenges”, which are Personalized and Contextualized on the basis of players’ preferences, skills, etc., and the game ulterior objectives. To this end, we develop a multi-layered framework to generate the personalized game content to be assigned and recommended to the players involved in the gamified system. To support the latter phase, we integrate two modules into the system including Machine Learning (ML) and Player Modeling, in order to optimize the challenge selection process and learning players’ behavior to further improve the personalization, by deriving the style of the player, respectively. We have carried out the implementation and evaluation of the proposed framework and its integration in two different contexts. First, we assess our Automatic Procedural Content Generation and Recommendation (APCGR) system within a large-scale and long-running open field experiment promoting sustainable urban mobility that lasted twelve weeks and involved more than 400 active players. Then, we implement the “Player Modeling” module (in the integration phase) in an educational interactive game domain to assess the performance of the proposed play style extraction approach. The contributions of this dissertation are a first step toward the application of machine learning in automating the procedural content generation and recommendation in gamification systems

A literature review of the use of Web 2.0 tools in Higher Education

This review focuses on the use of Web 2.0 tools in Higher Education. It provides a synthesis of the research literature in the field and a series of illustrative examples of how these tools are being used in learning and teaching. It draws out the perceived benefits that these new technologies appear to offer, and highlights some of the challenges and issues surrounding their use. The review forms the basis for a HE Academy funded project, ‘Peals in the Cloud’, which is exploring how Web 2.0 tools can be used to support evidence-based practices in learning and teaching. The project has also produced two in-depth case studies, which are reported elsewhere (Galley et al., 2010, Alevizou et al., 2010). The case studies focus on evaluation of a recently developed site for learning and teaching, Cloudworks, which harnesses Web 2.0 functionality to facilitate the sharing and discussion of educational practice. The case studies aim to explore to what extent the Web 2.0 affordances of the site are successfully promoting the sharing of ideas, as well as scholarly reflections, on learning and teaching

Dynamic Personalization of Gameful Interactive Systems

Gameful design, the process of creating a system with affordances for gameful experiences, can be used to increase user engagement and enjoyment of digital interactive systems. It can also be used to create applications for behaviour change in areas such as health, wellness, education, customer loyalty, and employee management. However, existing research suggests that the qualities of users, such as their personality traits, preferences, or identification with a task, can influence gamification outcomes. It is important to understand how to personalize gameful systems, given how user qualities shape the gameful experience. Current evidence suggests that personalized gameful systems can lead to increased user engagement and be more effective in helping users achieve their goals than generic ones. However, to create these kinds of systems, designers need a specific method to guide them in personalizing the gameful experience to their target audience. To address this need, this thesis proposes a novel method for personalized gameful design divided into three steps: (1) classification of user preferences, (2) classification and selection of gameful design elements, and (3) heuristic evaluation of the design. Regarding the classification of user preferences, this thesis evaluates and validates the Hexad Gamification User Types Scale, which scores a person in six user types: philanthropist, socialiser, free spirit, achiever, player, and disruptor. Results show that the scale’s structural validity is acceptable for gamification studies through reliability analysis and factor analysis. For classification and selection of gameful design elements, this thesis presents a conceptual framework based on participants’ self-reported preferences, which classifies elements in eight groups organized into three categories: individual motivations (immersion and progression), external motivations (risk/reward, customization, and incentives), and social motivations (socialization, altruism, and assistance). And to evaluate the design of gameful applications, this thesis introduces a set of 28 gameful design heuristics, which are based on motivational theories and gameful design methods and enable user experience professionals to conduct a heuristic evaluation of a gameful application. Furthermore, this thesis describes the design, implementation, and pilot evaluation of a software platform for the study of personalized gameful design. It integrates nine gameful design elements built around a main instrumental task, enabling researchers to observe and study the gameful experience of participants. The platform is flexible so the instrumental task can be changed, game elements can be added or removed, and the level and type of personalization or customization can be controlled. This allows researchers to generate different experimental conditions to study a broad range of research questions. Our personalized gameful design method provides practical tools and clear guidelines to help designers effectively build personalized gameful systems

Newcomer Retention and Productivity in Online Peer-Production Communities

University of Minnesota Ph.D. dissertation. July 2018. Major: Computer Science. Advisor: Joseph Konstan. 1 computer file (PDF); x, 159 pages.Online communities are online interaction spaces for people that break the barriers of time, space, and scale and provide opportunities for companionship and social support, information exchange, retail, and entertainment. Among them are online peer production communities that have a fantastic business model where volunteers come together to produce content and drive traffic to these sites. Although as a class these communities are successful, the success of individual communities greatly varies. To become and remain successful, these communities must meet a number of challenges related to starting communities, retention of members, encouraging commitment, and contribution from their members, regulating the behavior of members and so on. This dissertation focuses on the specific challenge of newcomer retention and productivity in the context of online peer-production communities. Exploring three different communities with entirely different structures and compositions – MovieLens, GitHub, and Wikipedia and building upon prior work in this space, this dissertation offers a number of important predictors of retention and productivity of newcomers. First, this dissertation explores the value of early activity diversity in the presence of the amount of early activity as a predictor of newcomer retention. Second, this dissertation digs into more fundamental psychological traits of newcomers such as personality and presents findings on relationships between personality and newcomer retention, preferences, and productivity. Third, this dissertation explores and presents results on the relationship between community interactions (apart from norms, policies and rigid structures) and newcomer retention. Fourth, this dissertation studies and presents the effects of various kinds of prior experience of newcomers on retention and productivity in a new group they join. This dissertation concludes by offering a number of directions for future research

Opportunities and Risks in Online Gaming Environments

Massively Multiplayer Online Role Playing Games (MMORPGs) have evolved from traditional video games in that they embrace both the technology of the Internet and video games. The massive “exodus” from the physical offline world to online gaming communities brings with it not only a number of unique and exciting opportunities, but also a number of emerging and serious risks. This research set out to examine the unique opportunities and risks to vulnerable individuals, namely, young adults, teenagers and young children; all of whom are considered by many to be priority groups in the protection from harm. The purpose was to examine the reality of vulnerable individuals encountering these opportunities and risks. This research combined a number of methodologies supported by underpinning qualitative and quantitative theories. Questionnaires, semi-structured interviews and focus groups gathered information from teenagers, adults and children in order to critically examine the unique opportunities and risks encountered in Massively Multiplayer Online Role Playing Games. The findings from these interactions identified specific examples of opportunities and risk posed to vulnerable individuals. The findings demonstrated that there was a need for a support and protection mechanism that promoted the identification and awareness of the potential risk among vulnerable individuals. Emerging from these findings was a set of concepts that provided the evidence base for a Novel Taxonomy of Opportunities and Risks in Massively Multiplayer Online Role Playing Game environments that was designed to assist in the assessment of risk. Validation of the proposed taxonomy was achieved by means of an ethnographic study of (World of Warcraft) online gamers’ behaviour and social interactions through unobtrusive video capture of gaming sessions. The Novel Taxonomy of Opportunities and Risks provided a basis for the development of a proof-of-concept Decision Support System; the purpose of which was to assist both social work practitioners and individuals to identify and reduce risks. Representatives from both user groups were consulted for evaluation of the acceptability of such an approach. Favourable responses from participants demonstrated acceptability of the aforementioned approach. The evaluation process also demonstrated how the prototype would serve as a useful tool to make individual users aware of potential dangers. This research presents three novel facets: (1) it advances understanding of the unique opportunities and risks within MMORPG environments; (2) provides a framework for the assessment of risks in MMORPGs through the Novel Taxonomy and (3) demonstrates a novel Decision Support System to assist in the identification and reduction of risk through a proof-of-concept prototype