Evidence for a Cultural Mindset: Combining Process Data, Theory, and Simulation.

Despite large literature on Cross-Cultural Competence (3C) there is a gap in understanding learning processes and mechanisms by which people arrive at successful 3C. We present a novel perspective for 3C learning and decision-making in innovative assessment contexts. We use Mindset theory (i.e., believing ability is fixed or changeable) because it is shown to be a powerful motivator for general learning and performance and in cross-cultural contexts. We propose the notion of cultural mindsets - beliefs, affect, and cognition that govern how people adapt, learn, and update cultural information. To understand how cultural mindset affects learning and performance, we apply computational cognitive modeling using Markov decision process (MDP). Using logfile data from an interactive 3C task, we operationalize behavioral differences in actions and decision making based on Mindset theory, developing cognitive models of fixed and malleable cultural mindsets based on mechanisms of initial beliefs, goals, and belief updating. To explore the validity of our theory, we develop computational MDP models, generate simulated data, and examine whether performance patterns fit our expectations. We expected the malleable cultural mindset would be better at learning the cultural norms in the assessment, more persistent in cultural interactions, quit less before accomplishing the task goal, and would be more likely to modify behavior after negative feedback. We find evidence of distinct patterns of cultural learning, decision-making, and performance with more malleable cultural mindsets showing significantly greater cultural learning, persistence, and responsiveness to feedback, and more openness to exploring current cultural norms and behavior. Moreover, our model was supported in that we were able to accurately classify 83% of the simulated records from the generating model. We argue that cultural mindsets are important mechanisms involved in effectively navigating cross-cultural situations and should be considered in a variety of areas of future research including education, business, health, and military institutions.

Multiple Patient Casualty Scenarios: A Measurement Tool for Teamwork.

INTRODUCTION: Teamwork is a critical aspect of patient care and is especially salient in response to multiple patient casualties. Effective training and measurement improve team performance. However, the literature currently lacks a scientifically developed measure of team performance within multiple causality scenarios, making training and feedback efforts difficult. The present effort addresses this gap by integrating the input of subject matter experts and the science of multicasualty teams and training to (1) identify overarching teamwork processes and corresponding behaviors necessary for team performance and (2) develop a behavioral observation tool to optimize teamwork in multicasualty training efforts. METHOD: A search of articles including team performance frameworks associated with team training was conducted, leading to the identification of a total of 14 articles. Trained coders extracted teamwork processes and the corresponding team behaviors indicative of effective performance from these articles. Five subject matter experts were interviewed using the critical incident technique to identify additional behaviors. RESULTS: Team situation awareness, team leadership, coordination, and information exchange emerged as the four core team processes required for team performance in scenarios with multiple patient casualties. Relevant behaviors and subbehaviors within these overarching processes were identified to inform a pilot behavioral framework of team performance. CONCLUSIONS: The processes and associated behaviors identified within this effort serve as scientifically grounded behaviors of team performance in the case of multiple patient casualties simulated training scenarios. Future work can use and further refine these results to ensure that measures of team performance are grounded in specific, observable, and scientifically delineated behaviors.

Markov Decision Process Measurement Model.

Within-task actions can provide additional information on student competencies but are challenging to model. This paper explores the potential of using a cognitive model for decision making, the Markov decision process, to provide a mapping between within-task actions and latent traits of interest. Psychometric properties of the model are explored, and simulation studies report on parameter recovery within the context of a simple strategy game. The model is then applied to empirical data from an educational game. Estimates from the model are found to correlate more strongly with posttest results than a partial-credit IRT model based on outcome data alone.

Inferring learners' knowledge from their actions.

Watching another person take actions to complete a goal and making inferences about that person's knowledge is a relatively natural task for people. This ability can be especially important in educational settings, where the inferences can be used for assessment, diagnosing misconceptions, and providing informative feedback. In this paper, we develop a general framework for automatically making such inferences based on observed actions; this framework is particularly relevant for inferring student knowledge in educational games and other interactive virtual environments. Our approach relies on modeling action planning: We formalize the problem as a Markov decision process in which one must choose what actions to take to complete a goal, where choices will be dependent on one's beliefs about how actions affect the environment. We use a variation of inverse reinforcement learning to infer these beliefs. Through two lab experiments, we show that this model can recover people's beliefs in a simple environment, with accuracy comparable to that of human observers. We then demonstrate that the model can be used to provide real-time feedback and to model data from an existing educational game.