Eduardo "Ed" Salas, Ph.D. - US grants

The "Florida Alliance for the Study of Expertise" (FASE) is designed to advance a science of Expertise Studies throughout the United States and throughout the world. FASE leverages the capabilities of world leaders in the study of expertise who are affiliated with Florida Universities with the overarching goal of understanding the nature and development of expertise. This funding will support the creation of a technical and collaborative infrastructure that will unite a multi-university and multi-disciplinary research partnership for the purposes of understanding and encouraging the scientific study of expertise. To understand learning at a fundamental level one must examine learning phenomena up to the level of expert achievement and theories of learning must take such phenomena into account. To address this need, FASE will integrate differing scientific methodologies to enhance the necessary complementarity of laboratory and field research. FASE will support collaboration across universities and across methodologies with the goal of fostering a synergistic combination of methods where the benefits of scientific approaches can be leveraged to improve hypothesis generation and testing along with theory development in a broader context. FASE focuses on the entire human system and how experience alters this system to produce meaningful learning that leads to the highest levels of achievement. FASE will facilitate the understanding of how it is that expert knowledge is acquired in order to determine both how it can be preserved and how others may be taught to engage in the requisite activities to similarly acquire such knowledge.

FASE contributes to the goals of developing a science of learning by connecting learning research to scientific challenges and developing research communities that can capitalize on new opportunities. Further, the cross-section of scientific experience within this alliance allows FASE to address fundamental questions in learning by focusing on the physical, mental, and organizational capabilities associated with exceptional performance. As such, FASE is designed to benefit not only K-12 educational settings, but also workforce development for adult learning in government and industry. FASE is designed to support, not only basic science on how the human system achieves levels of exceptional performance, but also a unique cooperation between science and practice in order to ensure the rapid transition of new knowledge to education, industry, and to government. Last, FASE broadens the participation of underrepresented groups first, by establishing an association with Florida International University, an official minority-serving university so as to develop the capacity to support research and education for minority populations in science. Second, FASE develops the general educational infrastructure to support Webcasts and other distributable media for courses that can be offered for broad dissemination. In short, FASE will significantly contribute to national needs by elucidating how it is that experts achieve exceptional levels of performance while at the same time pursuing this on a pedagogical level.

Our research effort has two overarching goals: the first epistemological and the second methodological. Our epistemological goal concerns naturalistic decision making (NDM) in the context of complex domains. Our application is an important economic problem where the evaluation of the social welfare consequences often leads to conflicting positions by experts and affected non-expert citizens. We argue that much of this conflict is created because experts and non-experts use different cognitive processes when evaluating and forming decisions. We propose a simulation technique designed to facilitate a convergence in these cognitive processes, and hypothesize that this will lead to a reduction in the degree of conflict. We have two methodological goals. First, we will use interactive, immersive virtual-reality (VR) simulation technologies to recreate, in a controlled environment, the rich array of cues and information relied upon by decision-makers in naturalistic domains. The application we have selected, forest management policies, is a good example of a decision environment with a rich set of information cues and interactions, and where the experience of experts is expected to matter in significant ways to the decisions made. Our second methodological goal is to blend the techniques of controlled economics experimentation with those of NDM. The power of experimentation lies in replicability and control, and by extending these capabilities through the power of VR simulations, this research will allow us to explore issues in decision making in ways heretofore not feasible. We will compare decisions made by participants using standard state-of-the-art questionnaires, where scenarios are described in words and with pictures, to those made using the interactive experience of the VR technology. We hypothesize that the differences in values and decisions between experts and non-experts is smaller with the immersive, interactive VR environment than with the standard word and picture descriptions.

The research proposed will be valuable within economics, psychology, and computer science. Within economics, the research will contribute to the extension of standard expected utility theory to include the role of context and familiarity. Within the psychological sciences, this research presents a unique opportunity for theory development and testing in naturalistic domains. The advances in computer science will be in the development of algorithms for real-time, realistic rendering and the integration of these in a comprehensive mixed reality system.

Engineering - Other (59)
Teamwork predominates in engineering education, yet the careful design and assessment of methods for students to learn teamwork skills is not widespread. Teaching teaming skills is a complex challenge, one for which most engineering faculty have not been prepared. This project is building on a successful, web-based system for assigning and assessing teaming in engineering education, expanding this resource to additional institutions and increasing the capabilities of the tool. The goals of this project are to equip students to work on teams by providing them with training and feedback, to equip faculty to manage student teams by providing them with information and tools to facilitate best practices, and to equip researchers to understand teams by broadening the system's capabilities to collect additional types of data through a secure researcher interface. The use of the teaming tools in STEM disciplines outside of engineering is also being investigated through this project.

This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5). Organizations are restructuring into collaborative systems in order to address complex problems by combining expertise distributed across business functions, knowledge specialties, and geographic locations. Often times these systems face complex and multifaceted goals requiring distinct teams to coordinate their efforts and compile crucial information distributed across a network of teams (i.e., multiteam system). This project will render an understanding of the requisite motivational, cognitive, affective, and behavioral dynamics that enable virtual organizations to function effectively.

This research tests components of a theoretically-based model of virtual organizational effectiveness grounded in multiteam systems theory and network theory. Methods include meta-analytic integration, field research on emergency response systems, and laboratory experimentation. Meta-analysis will be used to identify core principles linking virtuality features (e.g, synchronicity) and structural characteristics (e.g, nature of distribution) to essential behavioral (e.g., information sharing), motivational (e.g, efficacy), affective (e.g, cross-team trust), and cognitive (e.g, transactive memory) dynamics. Field methods will be used to examine the most pressing practical problems, and their relation to network features, in multiagency coordination structures operating in highly complex virtual settings. Laboratory experimentation will evaluate standardization of communication norms and pre-disaster network formation in enabling the coherent functioning of virtual organizations.

Findings from this multidisciplinary, multimethod investigation will contribute insights to the literatures on virtual teams and multiteam systems (e.g., intra-organizational and interorganizational level). These findings will ultimately improve the functioning of a variety of virtual organizations operating in emergency response and recovery, military, and academic research. In particular, this knowledge will inform needed alterations to the design, leadership, training, policy, and feedback systems in such organizations.

The question of shared leadership is growing more important as complex world issues increasingly require distributed collaboration. Organizational researchers lack an understanding of the multiple kinds of leadership roles that virtual teams rely on and of the optimal balance of these roles. Previous research has confounded virtuality and distribution or included only a few combinations of these characteristics.
Systematically examining the combined effects of degree of virtuality and geographical distribution on emerging forms of leadership and outcomes, this research includes a lab study to understand leadership structure in virtual teams. It investigates how team cognitive processes and performance are affected by: (1) how leadership is shared or distributed among internal and external team members, (2) the degree of virtuality inherent in the tools used for collaboration (e.g. synchronicity, informational value), and (3) the degree of distribution among team members (e.g. fully distributed, partially distributed). Results will extend existing theory on shared leadership and team cognition to hybrid teams of virtual and face-to-face interactions to illuminate the relationship between leader behaviors and team outcomes.

These findings will ultimately provide evidence-based guidelines for practitioners regarding the form(s) of leadership best suited for particular types of virtual teams and the corresponding processes required for effective performance. Additionally, findings will contribute to knowledge concerning how virtual team structure interacts with computer and communication support technology to impact team process and performance with potentially transformative results.

The project will contribute to the national need for well-educated scientists, mathematicians, engineers, and technicians by supporting the retention and graduation of high-achieving, low-income students with demonstrated financial need at Rice University, Texas Southern University, Prairie View A&M University, and Jackson State University. Across all four partner institutions, the project will support roughly 220 academically talented domestic students from low-income backgrounds with demonstrated financial need to pursue and complete master's degrees in engineering, computer science, mathematics and data science. Students will select from among disciplines in one of three technical tracks: biotechnology; sustainability and resilience; and digital twinning. (The latter refers to the use of a virtual model of a system that is updated with real-time data and used to support simulations and decision making.) The core of the project is a carefully crafted combination of scholarships, academic and career development, mentoring, and cohort development based on preliminary student data and the literature on scholar success and workforce development. The programmatic elements are structured to remove barriers and to foster students’ successful matriculation, graduation, and entry into the workforce. Rather than focus on traditional education models that often set up a dichotomy between research experiences and internships that lead to singular career paths, the Flexible Internships-Research-Education (FIRE) model employed here is designed to give students experiences that integrate both research and internships, facilitated by workforce partners, at both the undergraduate and graduate level. Towards this end an important component of the project is a partnership with the Engineer Research and Development Center, a major employer specializing in civil and military engineering, geospatial sciences, water resources, and environmental sciences.

The objectives of this project are to: (1) increase the number of domestic low-income academically talented students with demonstrated financial need obtaining master’s degrees and entering the US STEM workforce in areas of critical need; (2) implement and evaluate the impact of the FIRE model on student success; and (3) implement, study, and disseminate a multiteam systems model for collaboration toward career and educational development. Scholars will be part of multi-disciplinary, multi-institutional cohorts based on their year of matriculation as well as their chosen technical tracks and will be mentored by faculty and practitioners. The project’s mixed-method research plan is guided by three principal research questions centered on better understanding how multi-institutional teams function. First, how do the varied norms, values, and priorities of individuals in different disciplines/organizations manifest in teamwork issues such as communication and work style differences? Second, how does the overall organizational culture interact with that from each discipline or team within the multiteam system, and how does the blended culture impact conflict resolution? Finally, how do leadership structures and institutional or team-level conditions support collaboration and progress towards team goals, in this case successful academic outcomes for scholars? Major data sources include interviews, surveys of scholars and leadership team members, and measures of student success. Project materials, findings and outcomes will be disseminated widely to the STEM education community. This project is funded by NSF’s Scholarships in Science, Technology, Engineering, and Mathematics program, which seeks to increase the number of low-income academically talented students with demonstrated financial need who earn degrees in STEM fields. It also aims to improve the education of future STEM workers, and to generate knowledge about academic success, retention, transfer, graduation, and academic/career pathways of low-income students.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

Cardiac surgery is often needed to address some of the most serious heart problems, resulting in administration of more than 900,000 cardiac procedures each year. The cardiac Operating Room (OR) is a complex environment where healthcare professionals from multiple disciplines -- including surgeons, anesthesiologists, perfusionists, and nurses -- collaborate to administer this life-critical care. To successfully administer care, all members of the surgical team are expected to perform their tasks in lockstep and with full awareness of dynamic situations encountered during surgery. However, achieving such ideal teamwork is difficult in the complex environment of cardiac OR, where human performance is adversely affected by factors such as high workload, fatigue, and interruptions or disruptions during surgery. This project addresses an urgent need for mitigating these preventable human errors and improving patient safety through the design of an Artificial Intelligence (AI)-enabled coaching system (AI Coach) for monitoring, assessing, and enhancing surgical teamwork in the cardiac OR. Central to the functioning of the AI Coach will be a set of novel machine learning and explainable artificial intelligence algorithms to computationally generate interpretable feedback and interventions for enhancing surgical teamwork based on multimodal sensor data. The project will train students in the multi-disciplinary research area of Smart Health. The project will increase public engagement with AI, by incorporating the research results into a planned museum exhibit on human-AI collaboration.<br/> <br/>The project’s overarching goal is to design the AI Coach system comprised of multimodal sensing hardware, data-driven algorithms, and a user interface to enhance surgical teamwork in the cardiac OR. AI Coach will achieve its objectives by pursuing two parallel strategies: (i) addressing the problem of modeling surgical teamwork; (ii) computationally generating feedback to improve this teamwork. The project team will first develop a novel Team Markov Model (TMkM) that reflects the surgical team’s mental model. Then, the computational core of the system will be realized through the development of (a) machine learning algorithms based on novel multi-agent imitation learning methods to arrive at predictive models of teamwork that explicitly depend on latent performance-shaping factors, such as mental models, and (ii) explainable AI techniques to computationally generate interpretable feedback and interventions for enhancing teamwork. Due to the challenge of collecting large data sets of surgical teamwork, the algorithm development will emphasize sample- and label-efficient techniques. The project team will prototype and test usability of the integrated system by employing iterative, user-centered design approaches. The solutions will be developed and evaluated using multi-modal expert-annotated data of surgical teamwork and prototyped in a state-of-the-art OR simulation facility.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.