Nilam Ram, Ph.D. - US grants

DESCRIPTION (provided by applicant): This application addresses broad Challenge Area 01: Behavior, Behavioral Change, and Prevention, and specific Challenge Topic 01-AG-103: Individual-based Model of Social Behavior. Innovations in developmental theory have outpaced methodological development and application. Needed are methods that allow for precise articulation of within-person/intra individual dynamics of behavior that unfold over multiple time scales (days, years, decades). New tools that explicitly tether dynamic ideas, within-person methods, and real-world observations must be developed, implemented, and evaluated - substantively and methodologically - to advance our understanding of the dynamic social processes that influence health and well-being. Our approach to this Challenge includes the following specific aims: (1) developing and organizing analytical methods for measuring and articulating the dynamic characteristics and dynamic processes implicated in individual-based models of interpersonal/social behavior, (2) elaborating and implementing a data collection/design framework for examining the variability and change in interpersonal behavior, health, and well-being occurring at micro-, meso-, and macro- time scales (i.e., days to decades), and (3) predicting and testing how the dynamic characteristics and processes of interpersonal behavior, health, and well-being change over the course of hours/days, weeks/months and how those characteristics, processes, and changes differ across early-, mid-, and later adulthood. By deliberately tethering methodological advances to theoretical conceptions, and providing illustrations of how researchers can make use of these tethers, this project will provide a valuable resource for both methodological and substantive researchers interested in life-span development, interpersonal behavior, and health/well-being. Our research will deliver new methods and insights that allow investigators to link precisely modeled dynamic characteristics and dynamic processes of individuals'interpersonal processes to important health outcomes and well-being across early, mid-, and later adulthood. PUBLIC HEALTH RELEVANCE: Interpersonal behavior affects individuals'emotions, arousal, stress, and facilitated or impairs social support, intimacy, and relational satisfaction. This project will deliver new methods and models that allow for more specific articulation and linking of when and how interpersonal interaction processes contribute to health, well-being, and aging.

DESCRIPTION (provided by applicant): Evidence is building that late-life changes in sensory and cognitive function are marked by pronounced, proximate to death terminal decline. Relatively little is known, however, about if and when other aspects of psychological functioning may deteriorate with approaching death or how such declines may be interrelated. The proposed project moves beyond current, single domain, examinations of terminal decline to consider and develop a multidimensional view of the processes underlying late-life change - "terminal cascades." We will examine late-life change across five domains broadly representative of the functional system in old age (sensory, physical, cognitive, well-being and social relations) and explore how these changes are interrelated. Objectives include (1) testing for unique associations between changes in functionality and subsequent mortality, (2) determining whether age-, death-, or pathology-related processes are the primary contributors to late-life change, and (3) describing the prototypical, multivariate change trajectories individuals follow into death. Recent and forthcoming innovations in the analysis of change will be applied to multidisciplinary 15-year longitudinal data from deceased participants of the Berlin Aging Study (N = 430+;70-103 years). New analytic tools will be developed for modeling multivariate change. These tools and analyses shall allow for more precise descriptions of how individuals progress from one multivariate within-person profile of functioning to another over time (age, distance-to-death, pathology) and will thereby help to clarify which segments of society are likely to experience which types of decline. Empirical results will shed light on how and why some individuals age more successfully than others (e.g., experience less decline prior to death), point to particular pathways by which more and more individuals can maintain a life lived fully and with dignity into advanced ages, and yield practical information for the design and implementation of preventive interventions focused on alleviating the societal and personal costs of late-life decline. PUBLIC HEALTH RELEVANCE: The end of life in old age is often characterized by costly deteriorations in functionality and health - seemingly inevitable declines that contribute substantially to health care costs and societal burden. Little is known, however, about which aspects of late-life psychological function deteriorate, in what order, or how quickly such declines occur. As a first step in identifying the pathways by which more individuals can maintain a life lived fully and with dignity, the major goal of the proposed project is to obtain a better understanding of how late-life changes across domains of sensory, physical, cognitive, well-being, and social functionality are structured and interrelated. This knowledge will contribute to developing effective programs and policies that target the areas most likely to alleviate the social and personal costs of late-life decline (e.g., designing suitable health or hospice care).

DESCRIPTION (provided by applicant): Self-regulation is a key aspect of healthy development and impairments in self-regulation are associated with many poor health outcomes. This application addresses the NIH's call for greater consistency and integration in research on self-regulation. Building from a core framework, wherein self-regulation is defined as the recruitment of executive processes (e.g. attention control) to alter prepotent responses (e.g. emotion) we develop a set of mathematical models that aim to unify theoretical perspectives, and to capture the dynamic nature of self-regulation using intensive time-series data. Models are developed and tested using intensive time-series data archived in 5 independent studies of early childhood self-regulation and parent-directed regulation of infant state. In each, behaviors and/or physiology indexing prepotent and executive processes were coded on a second-by-second time scale. Knowledge gained from the unified framework is then used to design and collect new data that provides for tests of predictive validity, developmental differences, and generalization across multiple manifestations of both young children's and parents' self-regulation.

Big data and the data science skills to analyze data are critical in all science, technology, engineering, and mathematics (STEM) areas. Within this data-rich context, incoming STEM graduate students with different levels of data science and STEM training can complicate curricular approaches for mastery of data science techniques. Linear and uniform preparation models may even widen the performance gap among students with diverse levels of preparation. This issue might be circumvented by replacing traditional "one-size-fits-all" course content with personalized electronic training modules that are tailored to each student's unique strengths, weaknesses, and training goals. This National Science Foundation Innovations in Graduate Education award to Penn State University aims to develop, test, and refine a set of digital educational tools brought together by the Individualized Pathways and Resources to Adaptive Control Theory-Inspired Scientific Education system (iPRACTISE). The goal of iPRACTISE is to direct each student toward a personally optimized training pathway for mastery of data science techniques. The iPRACTISE system will allow students to specify their own learning goals, provide customized assessments to evaluate their performance levels, and guide them to educational resources that help them reach their goals. In this way, the iPRACTISE system serves as an initial proof-of-concept for a personalized, digital graduate educational system that could be adapted for use in a broad array of educational settings to enhance individual learning.

Personalized education can be viewed as a control theory problem in which students seek ongoing input, such as classes, curricular resources and training exercises to minimize the discrepancies between their actual and targeted levels of expertise. The iPRACTISE system will include: (1) digital training materials curated from existing teaching resources and a user-interface for instructors to populate the system with new training materials; (2) a user interface to specify training goals; (3) a computerized assessment system that evaluates students' current ability levels; and (4) control theory algorithms that automate the delivery of optimal, individualized training modules. The project will collect test data from a calibration sample consisting of graduate and advanced undergraduate students from multiple campuses at Penn State University to develop and test the assessment system (for evaluating student competency) and the control theory algorithm (for making personalized recommendations on training contents). After initial calibration, the iPRACTISE system offerings will be expanded and learning outcomes compared across diverse student cohorts.

The Innovations in Graduate Education (IGE) program is focused on research in graduate education. The goals of IGE are to pilot, test and validate innovative approaches to graduate education and to generate the knowledge required to move these approaches into the broader community.

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.