Ethics education has been recognized as increasingly important to engineering over the past two decades, although disagreement exists concerning how ethics can and should be taught in the classroom. With active learning strategies becoming a preferred method of instruction, a collaboration of authors from four universities (University of Pittsburgh, University of Connecticut, Rowan University and New Jersey Institute of Technology) are investigating how game-based or playful learning with strongly situated components can influence first-year engineering students’ ethical knowledge, awareness, and decision making.
This paper offers an overview and results of the progress to date of this three year, NSF Improving Undergraduate STEM Education (IUSE) grant that aims to (1) characterize the ethical awareness and decision making of first-year engineering students, (2) develop game-based learning interventions focused on ethical decision making, and (3) determine how (and why) game-based approaches affect students’ ethical awareness in engineering and the advantages of such approaches over non game-based approaches. Now in its second year, the authors have conducted a preliminary analysis of first-year students' ethical knowledge and organization via a concept mapping approach and have measured students' ethical reasoning using the Defining Issues Test 2 (DIT2) and Engineering Ethics Reasoning Instrument (EERI). Further, the authors have developed a suite of ethics-driven games that have been implemented across three of the universities, engaging over 400 first-year engineering students. Evaluation data has also been gathered for further game development and to assess initial student engagement and learning.
Year 1 has provided insight into where first-year engineering students “are at” in terms of ethical knowledge and reasoning when they come to college, and how game-based instruction can be effective in the development of these students into moral agents who understand the consequences of their decisions. Further results from this investigation will provide the engineering education community with a set of impactful and research-based playful learning pedagogy and assessment that will help students confront social and ethical dilemmas in their professional lives.
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Domain Knowledge and Adaptive Serious Games: Exploring the Relationship of Learner Ability and Affect Adaptability
Detection and responding to a player’s affect are important for serious games. A method for this purpose was tested within Chem-o-crypt, a game that teaches chemical equation balancing. The game automatically detects boredom, flow, and frustration using the Affdex SDK from Affectiva. The sensed affective state is then used to adapt the game play in an attempt to engage the player in the game. A randomized controlled experiment incorporating a Dynamic Bayesian Network that compared results from groups with the affect-sensitive states vs those without revealed that measuring affect and adapting the game improved learning for low domain-knowledge participants.
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- Award ID(s):
- 1828010
- NSF-PAR ID:
- 10344116
- Date Published:
- Journal Name:
- Journal of Educational Computing Research
- Volume:
- 60
- Issue:
- 2
- ISSN:
- 0735-6331
- Page Range / eLocation ID:
- 406 to 432
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Although security games have attracted intensive research attention over the past years, few existing works consider how information from local communities would affect the game. In this paper, we introduce a new player -- a strategic informant, who can observe and report upcoming attacks -- to the defender-attacker security game setting. Characterized by a private type, the informant has his utility structure that leads to his strategic behaviors. We model the game as a 3-player extensive-form game and propose a novel solution concept of Strong Stackelberg-perfect Bayesian equilibrium. To compute the optimal defender strategy, we first show that although the informant can have infinitely many types in general, the optimal defense plan can only include a finite (exponential) number of different patrol strategies. We then prove that there exists a defense plan with only a linear number of patrol strategies that achieve the optimal defender's utility, which significantly reduces the computational burden and allows us to solve the game in polynomial time using linear programming. Finally, we conduct extensive experiments to show the effect of the strategic informant and demonstrate the effectiveness of our algorithm.
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Abstract Comparative approaches to experimental economics have shed light on the evolution of social decision‐making across a range of primate species, including humans. Here we replicate our previous work looking at six pairs of capuchin monkeys' (
Sapajus [Cebus] apella ) responses to scenarios requiring both coordination (Assurance Game) and anti‐coordination (Hawk‐Dove Game). This then provides a foundation for assessing their responses to two additional games, one with a scenario of beneficial cooperation with a temptation to defect (Prisoner's Dilemma) and one with an environment requiring changing strategies within short temporal proximity (Alternating Economic Game). We additionally explored the effects of exogenous oxytocin on decision‐making. Oxytocin did not affect decisions in any of our games. Results from the first two games largely replicated our previous findings. Responses to the Prisoner's Dilemma were more varied than was seen in previous games, with pairs respectively cooperating, defecting, and failing to establish stable strategies. Such variability indicates that this game may be a good assay for individual differences in social decision‐making. Finally, capuchins were able to flexibly switch between their previously established strategies within each of the different games, even when the games were presented within the same session, requiring strategy adjustments within short temporal proximity. These results build on earlier findings showing that capuchins can alter decision‐making strategies as the context demands, which is likely essential for decision‐making in naturally occurring contexts. -
While interacting with a machine, humans will naturally formulate beliefs about the machine's behavior, and these beliefs will affect the interaction. Since humans and machines have imperfect information about each other and their environment, a natural model for their interaction is a game. Such games have been investigated from the perspective of economic game theory, and some results on discrete decision-making have been translated to the neuromechanical setting, but there is little work on continuous sensorimotor games that arise when humans interact in a dynamic closed loop with machines. We study these games both theoretically and experimentally, deriving predictive models for steady-state (i.e. equilibrium) and transient (i.e. learning) behaviors of humans interacting with other agents (humans and machines). Specifically, we consider experiments wherein agents are instructed to control a linear system so as to minimize a given quadratic cost functional, i.e. the agents play a Linear-Quadratic game. Using our recent results on gradient-based learning in continuous games, we derive predictions regarding steady-state and transient play. These predictions are compared with empirical observations of human sensorimotor learning using a teleoperation testbed.more » « less
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Abstract The translocation of individuals around the world is leading to rising incidences of anthropogenic hybridization, particularly between domestic and wild congeners. We apply a landscape genomics approach for thousands of mallard (
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1177/07356331211031287
