This project will develop generalizable data-driven tools that addresses the conceptually and practically complex activity of constructing adaptive support for individualized learning in STEM domains.

For many forms of interactive environments, the system's behaviors can be viewed as a sequential decision process wherein, at each discrete step, the system is responsible for selecting the next action to take from a set of alternatives. The objective of this CAREER proposal is to learn robust interaction strategies that will lead to desirable outcomes in complex interactive environments. The central idea of this project is that strategies should not only be effective in complex interactive environments but they should also be efficient, focusing solely on the key features of the domain and the crucial decision points. These are the features and decisions that are not only associated with desirable outcomes, but without which the desirable outcomes are unlikely to occur.

Diabetic retinopathy (DR) is expected to affect over 11 million people in the US by 2030 and is the leading cause of blindness in working age Americans, despite being almost entirely preventable with timely detection, treatment, and adherence to follow-up care. To reach the over 30 million adults living with diabetes in the US, the Retinal Care-DR program is designed to eliminate the deficiencies of the current care delivery model by implementing a first-of-its-kind, end-to-end solution for DR care and blindness prevention. This will be accomplished through the application of machine learning to prioritize patients for care coordination by DR risk, development of patient-specific engagement strategies to identify and modify adherence behaviors, implementation of agent-based simulation to maximize patient health outcomes while minimizing the cost of care coordination, and the identification of care coordination strategies that result in higher rates of screening using a user-centered design process.

In this proposed work, our goal is to automatically design effective personalized ITSs directly from log data. We will combine Co-PI Dr. Barnes data-driven approach on learning what to teach with PI Dr. Chi������������������s data-driven work on learning how to teach. More specifically, we will explore across three important undergraduate stem domains: discrete math, probability, and programming.

This project will focus on laboratory and classroom research. A team of interdisciplinary researchers from UCF (Dr. Roger Azevedo), NCSU Computer Science (Dr. Min Chi), and NCSU STEM Education (Dr. Soonhye Park) will conduct empirical and observational research aimed at improving teachers' decision-making based on their analyses of students' real-time, multi-channel self-regulated learning data. We will use multi-channel data to understand the nature of self-regulatory processes in students while using MetaTutor to understand challenging science topics (e.g., human biological systems). This will be accomplished by aligning and conducting complex computational and statistical analyses of a multitude of trace data (e.g., log-files, eye-tracking, facial expression of emotions), behavioral (e.g., human-pedagogical agent dialogue moves), and physiological measures (e.g., EDA), learning outcomes, and classroom data (e.g., teacher-student interactions, gaze behavior of teachers������������������ attention and use of data presented by the visualization tool). The proposed research, in the context of using MetaTutor and a visualization tool for teachers, is extremely challenging and will help us to better understand the nature and temporal dynamics of these processes in classroom contexts, how they contribute to various types of learning and use of self-regulatory skills, and provide empirical basis for designing an intelligent teacher analytics tool. The results from this grant will contribute significantly to models of social and cognitive bases of student-machine-teacher interactions; statistical and computational methods used to make inferences from complex multi-channel data; theoretical and conceptual understanding of temporally-aligned data streams; enhancing students������������������ understanding of complex science topics by making more sensitive and intelligent advanced learning technologies; and, enhanced understanding of how teachers use real-time student data to enhance their instructional decision-making, based on data presented in teacher analytic tools.

The proposed project seeks to establish a technical basis for, and preliminary development of, a Nearly Autonomous Management and Control (NAMAC) system in advanced reactors. The system is intended to provide recommendations to operators during all modes of plant operation except shutdown operations: plant evolutions ranging from normal operation to accident management. These recommendations are to be derived within a modern, artificial-intelligence (AI) guided system, making use of continuous extensive monitoring of plant status, knowledge of current component status, and plant parameter trends; the system will continuously predict near-term evolution of the plant state, and recommend a course of action to plant personnel.

While intelligent tutors have been shown to increase student learning in programming and other domains, and creative, exploratory programming environments are assumed to promote novice interest and motivation to learn to program, there are no environments that provide both creative tasks and intelligent support. We propose to extend our methods for data-driven hint generation, model tracing, and knowledge tracing to augment Snap and Java programming environments to be more supportive for novice programmers doing open-ended creative tasks.

Nonprofit hunger relief organizations operate in a complex environment consisting of a large and diverse donor base and a dynamic distribution network. These organizations generate a large amount of unstructured and complex data on food collection, inventory management, and distribution activities. However, existing information systems lack the infrastructure to interpret this large-scale data to provide real-time policy recommendations and support operational and strategic decision-making. The proposed smart service system will synthesize data from disparate sources to create a real-time perspective of the environment and learn from the actions of the decision maker. Specifically, this system will automatically predict, visualize, and recommend decisions that will advance operational effectiveness of food collection, distribution, and resource management in a way that is efficient and equitable and will identify opportunities to improve a food bank������������������s capability to satisfy hunger need.

The overall goal of this Phase 1 Convergence Accelerator (C-Accel) proposal is to develop what we know to be the first public-facing AI platform that assists individual workers and small employers with upskilling and career changes in a labor market increasingly characterized by automation, technological disruption, and AI recruiting. It will address key challenges faced by employees and employers in occupations most impacted by AI with labor market research, credential gap diagnostics, and support for job search and retraining in AI recruiting. Focusing on manufacturing in Phase I, we will develop and build support for an occupation predicted to lose about 20% jobs to automation by 2026, namely, machine operation hiring mostly male non-college workers. Exploring retraining resources, job search strategies in AI recruiting, and reemployment opportunities in related occupations requiring complementary skills, we aim to assist manufacturing workers with upskilling and retraining while developing educational materials to help prepare young generations for future jobs. Our innovative solution will be scaled up to a wide range of occupations and retraining programs in Phase II.

Following the landfall of hurricane Florence, thousands of families including children and seniors are out of power, food and water. Food Bank of Central and Eastern North Carolina (FBCENC), as one of the food banks serving in this area, is now operating at extended capacity to recover from the storm. FBCENC is facing an unique situation as 22 counties of their service territory fall within the affected area including two of their branch locations. Many of their partner agencies are still out of operation and it took about two weeks after the storm to bring the affected FBCENC branches back in operation. The purpose of this RAPID NSF project is to document the challenges encountered by FBCENC after Hurricane Florence. Specifically, we intend to collect data to quantify the extent of this disruptive event in order to provide insight on how nonprofit food distribution organizations can prepare, respond, and recover from disruptions to their network. Food Bank networks are unique in that normal operations involve responding to another type of disaster (hunger need) which is considered slow onset. However, given a sudden onset disaster, they simultaneously meet the existing hunger need within their service area while responding to the needs of the population in the affected area. This is particularly complicated if the affected area lies within their service area, which can bring increased demand given transportation network and capacity uncertainty.