719 resultados para games as learning environments
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What does the lesson “Finding Citations,” the game “Trivial Pursuit,” and the mechanic “Bluffing” all have in common? In this bootcamp brainstorm facilitated by a CUNY professor, attendees are broken up into design teams whose job it is to enhance a traditional lesson with the mechanics of popular board games in only 20 minutes. Whether you have to teach the rules of citation or the rules of interviewing, there is usually a game plan that can help. This game teaches you how to integrate educational games into your classroom, while providing a fun introduction to the principles of game-based learning.
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Games are known for leveraging enthusiasm, engagement, energy, knowledge, and passion on gamers; areas that are fundamentally important in higher education. Our panelists will share their perspectives on how Higher Education can take advantage of the potential of game based learning to create a more engaging student learning experien
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Fan culture is a subculture that has developed explosively on the internet over the last decades. Fans are creating their own films, translations, fiction, fan art, blogs, role play and also various forms that are all based on familiar popular culture creations like TV-series, bestsellers, anime, manga stories and games. In our project, we analyze two of these subculture genres, fan fiction and scanlation. Amateurs, and sometimes professional writers, create new stories by adapting and developing existing storylines and characters from the original. In this way, a "network" of texts occurs, and writers step into an intertextual dialogue with established writers such as JK Rowling (Harry Potter) and Stephanie Meyer (Twilight). Literary reception and creation then merge into a rich reciprocal creative activity which includes comments and feedback from the participators in the community. The critical attitude of the fans regarding quality and the frustration at waiting for the official translation of manga books led to the development of scanlation, which is an amateur translation of manga distributed on the internet. Today, young internet users get involved in conceptual discussions of intertextuality and narrative structures through fan activity. In the case of scanlation, the scanlators practice the skills and techniques of translating in an informal environment. This phenomenon of participatory culture has been observed by scholars and it is concluded that they contribute to the development of a student’s literacy and foreign language skills. Furthermore, there is no doubt that the fandom related to Japanese cultural products such as manga, anime and videogames is one of the strong motives for foreign students to start learning Japanese. This is something to take into pedagogical consideration when we develop web-based courses. Fan fiction and fan culture make it possible to have an intensive transcultural dialogue between participators throughout the world and is of great interest when studying the interaction between formal and informal learning that puts the student in focus
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This thesis focuses upon a series of empirical studies which examine communication and learning in online glocal communities within higher education in Sweden. A recurring theme in the theoretical framework deals with issues of languaging in virtual multimodal environments as well as the making of identity and negotiation of meaning in these settings; analyzing the activity, what people do, in contraposition to the study of how people talk about their activity. The studies arise from netnographic work during two online Italian for Beginners courses offered by a Swedish university. Microanalyses of the interactions occurring through multimodal video-conferencing software are amplified by the study of the courses’ organisation of space and time and have allowed for the identification of communicative strategies and interactional patterns in virtual learning sites when participants communicate in a language variety with which they have a limited experience. The findings from the four studies included in the thesis indicate that students who are part of institutional virtual higher educational settings make use of several resources in order to perform their identity positions inside the group as a way to enrich and nurture the process of communication and learning in this online glocal community. The sociocultural dialogical analyses also shed light on the ways in which participants gathering in discursive technological spaces benefit from the opportunity to go to class without commuting to the physical building of the institution providing the course. This identity position is, thus, both experienced by participants in interaction, and also afforded by the ‘spaceless’ nature of the online environment.
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Research objectives Poker and responsible gambling both entail the use of the executive functions (EF), which are higher-level cognitive abilities. The main objective of this work was to assess if online poker players of different ability show different performances in their EF and if so, which functions are the most discriminating ones. The secondary objective was to assess if the EF performance can predict the quality of gambling, according to the Gambling Related Cognition Scale (GRCS), the South Oaks Gambling Screen (SOGS) and the Problem Gambling Severity Index (PGSI). Sample and methods The study design consisted of two stages: 46 Italian active players (41m, 5f; age 32±7,1ys; education 14,8±3ys) fulfilled the PGSI in a secure IT web system and uploaded their own hand history files, which were anonymized and then evaluated by two poker experts. 36 of these players (31m, 5f; age 33±7,3ys; education 15±3ys) accepted to take part in the second stage: the administration of an extensive neuropsychological test battery by a blinded trained professional. To answer the main research question we collected all final and intermediate scores of the EF tests on each player together with the scoring on the playing ability. To answer the secondary research question, we referred to GRCS, PGSI and SOGS scores. We determined which variables that are good predictors of the playing ability score using statistical techniques able to deal with many regressors and few observations (LASSO, best subset algorithms and CART). In this context information criteria and cross-validation errors play a key role for the selection of the relevant regressors, while significance testing and goodness-of-fit measures can lead to wrong conclusions. Preliminary findings We found significant predictors of the poker ability score in various tests. In particular, there are good predictors 1) in some Wisconsin Card Sorting Test items that measure flexibility in choosing strategy of problem-solving, strategic planning, modulating impulsive responding, goal setting and self-monitoring, 2) in those Cognitive Estimates Test variables related to deductive reasoning, problem solving, development of an appropriate strategy and self-monitoring, 3) in the Emotional Quotient Inventory Short (EQ-i:S) Stress Management score, composed by the Stress Tolerance and Impulse Control scores, and in the Interpersonal score (Empathy, Social Responsibility, Interpersonal Relationship). As for the quality of gambling, some EQ-i:S scales scores provide the best predictors: General Mood for the PGSI; Intrapersonal (Self-Regard; Emotional Self-Awareness, Assertiveness, Independence, Self-Actualization) and Adaptability (Reality Testing, Flexibility, Problem Solving) for the SOGS, Adaptability for the GRCS. Implications for the field Through PokerMapper we gathered knowledge and evaluated the feasibility of the construction of short tasks/card games in online poker environments for profiling users’ executive functions. These card games will be part of an IT system able to dynamically profile EF and provide players with a feedback on their expected performance and ability to gamble responsibly in that particular moment. The implementation of such system in existing gambling platforms could lead to an effective proactive tool for supporting responsible gambling.
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Serious games are a category of games which are designed for a specific purpose other than for pure entertainment. It is not a new concept but serious games using real data, coupled with real time modelling and combining model results with social and economic factors opens up a new paradigm for active stakeholder participation. DHI and UNEP-DHI Centre initiated a project called Aqua Republica where a virtual world is developed which allows participants to develop a river basin and visualise the consequences of their decisions. The aim of this project is to raise awareness of the interconnectivity of water and educate on integrated water resources management. Aqua Republica combines a game layer with a water allocation model, MIKE BASIN, to create an interactive, realistic virtual environment where players play the role of a catchment manager of an undeveloped river catchment. Their main objective is to develop the river catchment to be as prosperous as it can be. To achieve that, they will need to generate a good economy in the catchment to provide the funds needed for development, have a steady food supply for their population and enough energy and water for the catchment. Through these actions by the player, a meaningful play is established to engage players and to educate them about the complex relationships between developmental actions in a river basin and the natural environment as well as their consequences. The game layer also consists of a reward system to encourage learning. People can play and replay the game, get rewarded from performing the right principles and penalised from failures in the game. This abstract will explain the concept of the game and how it has been used in a stakeholder participation environment.
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Kalai and Lebrer (93a, b) have recently show that for the case of infinitely repeated games, a coordination assumption on beliefs and optimal strategies ensures convergence to Nash equilibrium. In this paper, we show that for the case of repeated games with long (but finite) horizon, their condition does not imply approximate Nash equilibrium play. Recently Kalai and Lehrer (93a, b) proved that a coordination assumption on beliefs and optimal strategies, ensures that pIayers of an infinitely repeated game eventually pIay 'E-close" to an E-Nash equilibrium. Their coordination assumption requires that if players believes that certain set of outcomes have positive probability then it must be the case that this set of outcomes have, in fact, positive probability. This coordination assumption is called absolute continuity. For the case of finitely repeated games, the absolute continuity assumption is a quite innocuous assumption that just ensures that pIayers' can revise their priors by Bayes' Law. However, for the case of infinitely repeated games, the absolute continuity assumption is a stronger requirement because it also refers to events that can never be observed in finite time.
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We analyze a dynamic principal–agent model where an infinitely-lived principal faces a sequence of finitely-lived agents who differ in their ability to produce output. The ability of an agent is initially unknown to both him and the principal. An agent’s effort affects the information on ability that is conveyed by performance. We characterize the equilibrium contracts and show that they display short–term commitment to employment when the impact of effort on output is persistent but delayed. By providing insurance against early termination, commitment encourages agents to exert effort, and thus improves on the principal’s ability to identify their talent. We argue that this helps explain the use of probationary appointments in environments in which there exists uncertainty about individual ability.
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Peer-to-peer markets are highly uncertain environments due to the constant presence of shocks. As a consequence, sellers have to constantly adjust to these shocks. Dynamic Pricing is hard, especially for non-professional sellers. We study it in an accommodation rental marketplace, Airbnb. With scraped data from its website, we: 1) describe pricing patterns consistent with learning; 2) estimate a demand model and use it to simulate a dynamic pricing model. We simulate it under three scenarios: a) with learning; b) without learning; c) with full information. We have found that information is an important feature concerning rental markets. Furthermore, we have found that learning is important for hosts to improve their profits.
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My dissertation focuses on dynamic aspects of coordination processes such as reversibility of early actions, option to delay decisions, and learning of the environment from the observation of other people’s actions. This study proposes the use of tractable dynamic global games where players privately and passively learn about their actions’ true payoffs and are able to adjust early investment decisions to the arrival of new information to investigate the consequences of the presence of liquidity shocks to the performance of a Tobin tax as a policy intended to foster coordination success (chapter 1), and the adequacy of the use of a Tobin tax in order to reduce an economy’s vulnerability to sudden stops (chapter 2). Then, it analyzes players’ incentive to acquire costly information in a sequential decision setting (chapter 3). In chapter 1, a continuum of foreign agents decide whether to enter or not in an investment project. A fraction λ of them are hit by liquidity restrictions in a second period and are forced to withdraw early investment or precluded from investing in the interim period, depending on the actions they chose in the first period. Players not affected by the liquidity shock are able to revise early decisions. Coordination success is increasing in the aggregate investment and decreasing in the aggregate volume of capital exit. Without liquidity shocks, aggregate investment is (in a pivotal contingency) invariant to frictions like a tax on short term capitals. In this case, a Tobin tax always increases success incidence. In the presence of liquidity shocks, this invariance result no longer holds in equilibrium. A Tobin tax becomes harmful to aggregate investment, which may reduces success incidence if the economy does not benefit enough from avoiding capital reversals. It is shown that the Tobin tax that maximizes the ex-ante probability of successfully coordinated investment is decreasing in the liquidity shock. Chapter 2 studies the effects of a Tobin tax in the same setting of the global game model proposed in chapter 1, with the exception that the liquidity shock is considered stochastic, i.e, there is also aggregate uncertainty about the extension of the liquidity restrictions. It identifies conditions under which, in the unique equilibrium of the model with low probability of liquidity shocks but large dry-ups, a Tobin tax is welfare improving, helping agents to coordinate on the good outcome. The model provides a rationale for a Tobin tax on economies that are prone to sudden stops. The optimal Tobin tax tends to be larger when capital reversals are more harmful and when the fraction of agents hit by liquidity shocks is smaller. Chapter 3 focuses on information acquisition in a sequential decision game with payoff complementar- ity and information externality. When information is cheap relatively to players’ incentive to coordinate actions, only the first player chooses to process information; the second player learns about the true payoff distribution from the observation of the first player’s decision and follows her action. Miscoordination requires that both players privately precess information, which tends to happen when it is expensive and the prior knowledge about the distribution of the payoffs has a large variance.
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Current policies on education to visually impaired point for a growing trend of including students with special educational needs in regular schools. However, most often this inclusion is not accompanied by an appropriate professional trained or infrastructure, which has been presented as a big problem for regular school teachers who have students with visual impairments in their classroom. Based on this situation, the Group of Extension in Tactile Cartography from UNESP - University of the State of São Paulo - Campus de Rio Claro - SP - Brazil has been developing educational material of geography and cartography to blind students at a special school. Among the materials developed in this study highlight the development of graphics and board games provided with sound capabilities through MAPAVOX, software developed in partnership with UFRJ - Federal University from Rio de Janeiro - RJ - Brazil. Through this software, sound capabilities can be inserted into built materials, giving them a multi-sensory character. In most cases the necessary conditions for building specific materials to students with visual impairments is expensive and beyond the reach of features from a regular school, so the survey sought to use easy access and low cost materials like Cork, leaf aluminum, material for fixing and others. The development of these materials was supported by preparation in laboratory and its subsequent test through practices involving blind students. The methodology used on the survey is based on qualitative research and non comparative analysis of the results. In other words, the material is built based on the special students perception and reality construction, not being mere adaptations of visual materials, but a construction focused on the reality of the visually impaired. The results proved were quite successful as the materials prepared were effective on mediating the learning process of students with disabilities. Geographical and cartographic concepts were seized by the students through the technology used, associated with the use of materials that took into account in its building process the perception of the students.
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In multi-robot systems, both control architecture and work strategy represent a challenge for researchers. It is important to have a robust architecture that can be easily adapted to requirement changes. It is also important that work strategy allows robots to complete tasks efficiently, considering that robots interact directly in environments with humans. In this context, this work explores two approaches for robot soccer team coordination for cooperative tasks development. Both approaches are based on a combination of imitation learning and reinforcement learning. Thus, in the first approach was developed a control architecture, a fuzzy inference engine for recognizing situations in robot soccer games, a software for narration of robot soccer games based on the inference engine and the implementation of learning by imitation from observation and analysis of others robotic teams. Moreover, state abstraction was efficiently implemented in reinforcement learning applied to the robot soccer standard problem. Finally, reinforcement learning was implemented in a form where actions are explored only in some states (for example, states where an specialist robot system used them) differently to the traditional form, where actions have to be tested in all states. In the second approach reinforcement learning was implemented with function approximation, for which an algorithm called RBF-Sarsa($lambda$) was created. In both approaches batch reinforcement learning algorithms were implemented and imitation learning was used as a seed for reinforcement learning. Moreover, learning from robotic teams controlled by humans was explored. The proposal in this work had revealed efficient in the robot soccer standard problem and, when implemented in other robotics systems, they will allow that these robotics systems can efficiently and effectively develop assigned tasks. These approaches will give high adaptation capabilities to requirements and environment changes.
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On-line learning methods have been applied successfully in multi-agent systems to achieve coordination among agents. Learning in multi-agent systems implies in a non-stationary scenario perceived by the agents, since the behavior of other agents may change as they simultaneously learn how to improve their actions. Non-stationary scenarios can be modeled as Markov Games, which can be solved using the Minimax-Q algorithm a combination of Q-learning (a Reinforcement Learning (RL) algorithm which directly learns an optimal control policy) and the Minimax algorithm. However, finding optimal control policies using any RL algorithm (Q-learning and Minimax-Q included) can be very time consuming. Trying to improve the learning time of Q-learning, we considered the QS-algorithm. in which a single experience can update more than a single action value by using a spreading function. In this paper, we contribute a Minimax-QS algorithm which combines the Minimax-Q algorithm and the QS-algorithm. We conduct a series of empirical evaluation of the algorithm in a simplified simulator of the soccer domain. We show that even using a very simple domain-dependent spreading function, the performance of the learning algorithm can be improved.
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Includes bibliography
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Includes bibliography