StABLE: Making Player Modeling Possible for Sandbox Games

Digital games are increasingly delivered as services. Understanding how players interact with games on an ongoing basis is important for maintenance. Logs of player activity offer a potentially rich window into how and why players interact with games, but can be difficult to render into actionable insights because of their size and complexity. In particular, understanding the sequential behavior in-game logs can be difficult. In this thesis, we present the String Analysis of Behavior Log Elements (StABLE) method, which renders location and activity data from a game log file into a sequence of symbols which can be analyzed using techniques from text mining. We show that by intelligently designing sequences of features, it is possible to cluster players into groups corresponding to experience or motivation by analyzing a dataset containing Minecraft game logs. The findings demonstrate the validity of the proposed method, and illustrate its potential utility in mining readily available data to better understand player behavior

Um modelo para constru??o de gamifica??o personalizada com base nos frameworks hexad e 6D

O objetivo deste trabalho ? construir um modelo para a gamifica??o personalizada, que se baseia nos frameworks Hexad e 6D. Para fins de avalia??o, coletamos dados durante a aplica??o das gamifica??es criadas para as disciplinas de Introdu??o ? Programa??o e Algoritmo e Programa??o Estruturada do curso de Redes de Computadores e Sistemas para Internet no Instituto Federal da Para?ba (IFPB). Por isso, o nosso modelo tem como objetivo fornecer uma forma sistem?tica de construir gamifica??o personalizada para auxiliar os profissionais que a utilizam, assim como atrair mais jogadores/usu?rios. Em seguida, realizamos um mapeamento sistem?tico da literatura para melhor entender as necessidades de gamifica??o. Descobrimos que, na maioria das vezes, as gamifica??es s?o criadas sem um foco espec?fico nos usu?rios e sem um modelo pensado para guiar sua cria??o. Para verificar a efic?cia de nossa abordagem, criamos duas gamifica??es, uma gen?rica e outra personalizada, que foram aplicadas nas turmas. Durante o processo de cria??o do modelo, usamos alguns passos pr?-fixados do framework 6D e levamos em considera??o o perfil de jogador/usu?rio com base no framework Hexad. Ao final, avaliamos o que os professores acharam do modelo e se a gamifica??o personalizada mostrou melhores resultados do que a gamifica??o gen?rica. Ap?s o levantamento do perfil dos alunos, foi aplicado com os professores o modelo proposto para cria??o de gamifica??es personalizadas para cada turma. Uma an?lise cr?tica seguiu-se a cada aplica??o, juntamente com question?rios semiabertos online, para avaliar o comportamento e a influ?ncia dos alunos. Os resultados admitem que os alunos preferem a gamifica??o personalizada, embora sejam detectados alguns elementos a serem aprimorados na gamifica??o gen?rica utilizada na turma do professor C1. No entanto, na turma do professor C2, os resultados da an?lise revelaram uma prefer?ncia quase igualit?ria de ambas as gamifica??es. Al?m disso, os professores sentiram-se satisfeitos com a abordagem proposta e com a gamifica??o personalizada

Why so serious?:game-based learning in health profession education: state of the art and future directions

If you look around carefully, you see a lot of use of game elements that aim to motivate people towards a certain behaviour. From smileys on posts that aim to lower your driving speed, to earning stars in language learning apps. Game-based learning is the use of game elements to make learning more attractive and to encourage people to continue their learning. This is logical right? The longer you learn, the better the outcome. Or not? This doctoral thesis examines the effects of using game-based learning in medical education. Why and when should it be applied? We have investigated whether it is advisable to develop a game suitable for everyone. We discovered that there are 5 different game personas (player types): competitors, socializers, social achievers, explorers and trolls. Everyone has their own preferences when it comes to social interactions and achieving goals within a game. From this we were able to develop a taxonomy, which has been tested at almost all medical universities in the Netherlands. It shows that medical students are mainly socially oriented players. While most game based learnings are not at all. This doctoral research can offer perspective in current developments, gives direction where it could go, but also has a critical note on the use of game-based learning that is should not be applied too much

Social Addictive Gameful Engineering (SAGE): A Game-based Learning and Assessment System for Computational Thinking

At an unrivaled and enduring pace, computing has transformed the world, resulting in demand for a universal fourth foundation beyond reading, writing, and arithmetic: computational thinking (CT). Despite increasingly widespread acceptance of CT as a crucial competency for all, transforming education systems accordingly has proven complex. The principal hypothesis of this thesis is that we can improve the efficiency and efficacy of teaching and learning CT by building gameful learning and assessment systems on top of block-based programming environments. Additionally, we believe this can be accomplished at scale and cost conducive to accelerating CT dissemination for all. After introducing the requirements, approach, and architecture, we present a solution named Gameful Direct Instruction. This involves embedding Parsons Programming Puzzles (PPPs) in Scratch, which is a block-based programming environment currently used prevalently in grades 6-8. PPPs encourage students to practice CT by assembling into correct order sets of mixed-up blocks that comprise samples of well-written code which focus on individual concepts. The structure provided by PPPs enable instructors to design games that steer learner attention toward targeted learning goals through puzzle-solving play. Learners receive continuous automated feedback as they attempt to arrange programming constructs in correct order, leading to more efficient comprehension of core CT concepts than they might otherwise attain through less structured Scratch assignments. We measure this efficiency first via a pilot study conducted after the initial integration of PPPs with Scratch, and second after the addition of scaffolding enhancements in a study involving a larger adult general population. We complement Gameful Direct Instruction with a solution named Gameful Constructionism. This involves integrating with Scratch implicit assessment functionality that facilitates constructionist video game (CVG) design and play. CVGs enable learner to explore CT using construction tools sufficiently expressive for personally meaningful gameplay. Instructors are enabled to guide learning by defining game objectives useful for implicit assessment, while affording learners the opportunity to take ownership of the experience and progress through the sequence of interest and motivation toward sustained engagement. When strategically arranged within a learning progression after PPP gameplay produces evidence of efficient comprehension, CVGs amplify the impact of direct instruction by providing the sculpted context in which learners can apply CT concepts more freely, thereby broadening and deepening understanding, and improving learning efficacy. We measure this efficacy in a study of the general adult population. Since these approaches leverage low fidelity yet motivating gameful techniques, they facilitate the development of learning content at scale and cost supportive of widespread CT uptake. We conclude this thesis with a glance at future work that anticipates further progress in scalability via a solution named Gameful Intelligent Tutoring. This involves augmenting Scratch with Intelligent Tutoring System (ITS) functionality that offers across-activity next-game recommendations, and within-activity just-in-time and on-demand hints. Since these data-driven methods operate without requiring knowledge engineering for each game designed, the instructor can evolve her role from one focused on knowledge transfer to one centered on supporting learning through the design of educational experiences, and we can accelerate the dissemination of CT at scale and reasonable cost while also advancing toward continuously differentiated instruction for each learner

Exploiting physiological changes during the flow experience for assessing virtual-reality game design.

Immersive experiences are considered the principal attraction of video games. Achieving a healthy balance between the game's demands and the user's skills is a particularly challenging goal. However, it is a coveted outcome, as it gives rise to the flow experience – a mental state of deep concentration and game engagement. When this balance fractures, the player may experience considerable disinclination to continue playing, which may be a product of anxiety or boredom. Thus, being able to predict manifestations of these psychological states in video game players is essential for understanding player motivation and designing better games. To this end, we build on earlier work to evaluate flow dynamics from a physiological perspective using a custom video game. Although advancements in this area are growing, there has been little consideration given to the interpersonal characteristics that may influence the expression of the flow experience. In this thesis, two angles are introduced that remain poorly understood. First, the investigation is contextualized in the virtual reality domain, a technology that putatively amplifies affective experiences, yet is still insufficiently addressed in the flow literature. Second, a novel analysis setup is proposed, whereby the recorded physiological responses and psychometric self-ratings are combined to assess the effectiveness of our game's design in a series of experiments. The analysis workflow employed heart rate and eye blink variability, and electroencephalography (EEG) as objective assessment measures of the game's impact, and self-reports as subjective assessment measures. These inputs were submitted to a clustering method, cross-referencing the membership of the observations with self-report ratings of the players they originated from. Next, this information was used to effectively inform specialized decoders of the flow state from the physiological responses. This approach successfully enabled classifiers to operate at high accuracy rates in all our studies. Furthermore, we addressed the compression of medium-resolution EEG sensors to a minimal set required to decode flow. Overall, our findings suggest that the approaches employed in this thesis have wide applicability and potential for improving game designing practices