Learning about the Energy of a Hurricane System through an Estimation Epistemic Game

As part of a study into students' problem solving behaviors, we asked upper-division physics students to solve estimation problems in clinical interviews. We use the Resources Framework and epistemic games to describe students' problem solving moves. We present a new epistemic game, the "estimation epistemic game". In the estimation epistemic game, students break the larger problem into a series of smaller, tractable problems. Within each sub-problem, they try to remember a method for solving the problem, and use estimation and reasoning abilities to justify their answers. We demonstrate how a single case study student plays the game to estimate the total energy in a hurricane. Finally, we discuss the implications of epistemic game analysis for other estimation problems.Comment: 4 pages. Submitted to Physics Education Research Conference 201

On Making Good Games - Using Player Virtue Ethics and Gameplay Design Patterns to Identify Generally Desirable Gameplay Features

This paper uses a framework of player virtues to perform a theoretical exploration of what is required to make a game good. The choice of player virtues is based upon the view that games can be seen as implements, and that these are good if they support an intended use, and the intended use of games is to support people to be good players. A collection of gameplay design patterns, identified through their relation to the virtues, is presented to provide specific starting points for considering design options for this type of good games. 24 patterns are identified supporting the virtues, including RISK/REWARD, DYNAMIC ALLIANCES, GAME MASTERS, and PLAYER DECIDED RESULTS, as are 7 countering three or more virtues, including ANALYSIS PARALYSIS, EARLY ELIMINATION, and GRINDING. The paper concludes by identifying limitations of the approach as well as by showing how it can be applied using other views of what are preferable features in games

On Making Good Games - Using Player Virtue Ethics and Gameplay Design Patterns to Identify Generally Desirable Gameplay Features

This paper uses a framework of player virtues to perform a theoretical exploration of what is required to make a game good. The choice of player virtues is based upon the view that games can be seen as implements, and that these are good if they support an intended use, and the intended use of games is to support people to be good players. A collection of gameplay design patterns, identified through their relation to the virtues, is presented to provide specific starting points for considering design options for this type of good games. 24 patterns are identified supporting the virtues, including RISK/REWARD, DYNAMIC ALLIANCES, GAME MASTERS, and PLAYER DECIDED RESULTS, as are 7 countering three or more virtues, including ANALYSIS PARALYSIS, EARLY ELIMINATION, and GRINDING. The paper concludes by identifying limitations of the approach as well as by showing how it can be applied using other views of what are preferable features in games

Dark Patterns in the Design of Games

Game designers are typically regarded as advocates for players. However, a game creator’s interests may not align with the players’. We examine some of the ways in which those opposed interests can manifest in a game’s design. In particular, we examine those elements of a game’s design whose purpose can be argued as questionable and perhaps even unethical. Building upon earlier work in design patterns, we call these abstracted elements Dark Game Design Patterns. In this paper, we develop the concept of dark design patterns in games, present examples of such patterns, explore some of the subtleties involved in identifying them, and provide questions that can be asked to help guide in the specification and identification of future Dark Patterns. Our goal is not to criticize creators but rather to contribute to an ongoing discussion regarding the values in games and the role that designers and creators have in this process

The Minority Game Unpacked: Coordination and Competition in a Team-based Experiment

In minority games, players in a group must decide at each round which of two available options to choose, knowing that only subjects who picked the minority op- tion obtain a positive reward. Previous experiments on the minority and similar congestion games have shown that players interacting repeatedly are remarkably able to coordinate eciently, despite not conforming to Nash equilibrium behavior. We conduct an experiment on a minority-of-three game in which each player is a team composed by three subjects. Each team can freely discuss its strategies in the game and decisions must be made via a majority rule. Team discussions are recorded and their content analyzed to detect evidence of strategy co-evolution among teams playing together. Our main results of team discussion analysis show no evidence sup- porting the mixed strategy Nash equilibrium solution, and support a low-rationality, backward-looking approach to model behavior in the game, more consistent with reinforcement learning models than with belief-based models. Showing level-2 ratio- nality (i.e., reasoning about others' beliefs) is positively and signicantly correlated with higher than average earnings in the game, showing that a mildly sophisticated approach pays off. In addition, teams that are more successful tend to become more egocentric over time, paying more attention to their own past successes than to the behavior of other teams. Finally, we nd evidence of mutual adaptation over time, as teams that are more strategic (i.e., they pay more attention to other teams' moves) induce competing teams to be more egocentric instead. Our results contribute to the understanding of coordination dynamics resting on heterogeneity and co-evolution of decision rules rather than on conformity to equilibrium behavior. In addition, they provide support at the decision process level to the validity of modeling behavior using low-rationality reinforcement learning models.coordination, minority game, market eciency, information, self-organization, reinforcement learning s

Dialogue as Data in Learning Analytics for Productive Educational Dialogue

This paper provides a novel, conceptually driven stance on the state of the contemporary analytic challenges faced in the treatment of dialogue as a form of data across on- and offline sites of learning. In prior research, preliminary steps have been taken to detect occurrences of such dialogue using automated analysis techniques. Such advances have the potential to foster effective dialogue using learning analytic techniques that scaffold, give feedback on, and provide pedagogic contexts promoting such dialogue. However, the translation of much prior learning science research to online contexts is complex, requiring the operationalization of constructs theorized in different contexts (often face-to-face), and based on different datasets and structures (often spoken dialogue). In this paper, we explore what could constitute the effective analysis of productive online dialogues, arguing that it requires consideration of three key facets of the dialogue: features indicative of productive dialogue; the unit of segmentation; and the interplay of features and segmentation with the temporal underpinning of learning contexts. The paper thus foregrounds key considerations regarding the analysis of dialogue data in emerging learning analytics environments, both for learning-science and for computationally oriented researchers

Thinking about Attention in Games: Backward and Forward Induction

Behavioral economics improves economic analysis by using psychological regularity to suggest limits on rationality and self-interest (e.g. Camerer and Loewenstein 2003). Expressing these regularities in formal terms permits productive theorizing, suggests new experiments, can contribute to psychology, and can be used to shape economic policies which make normal people better off