Roger Azevedo - US grants

EIA-0088081
Walsh, Gregory C.
University of Maryland

CRCD: A Curriculum in Networked and Distributed Systems

This project develops an innovative senior/masters-level curriculum designed to: (a) bring the important new technologies in Wireless and Networked Distributed Systems into the classroom, (b) make use of novel teaching and evaluation methods to enhance faculty productivity in laboratory and project courses, and (c) improve the educational value of students' experiences in laboratory and project courses. The laboratory facilities, which are part of this work, also play a key role in enabling multi-disciplinary research in networks, communications, embedded systems, and controls. The project crosses several disciplinary boundaries and has clearly defined deliverables that expose students to bodies of knowledge in great demand in the workforce. Two leading companies in this emerging area, United Technologies and General Electric, mentor this project.

This project focuses on the role of self-regulated
learning (SRL) in students' understanding of science with hypermedia. SRL is
emerging as a significant issue in educational and psychological research. SRL is an active, constructive process
whereby learners set goals for their learning and then attempt to monitor,
regulate, and control their cognition, motivation, and behavior in the service
of those goals. SRL is guided and constrained by both personal characteristics
and the contextual features of the environment (Pintrich, 2000). The focus of
SRL research over the last three decades has been on learners' academic learning
and achievement and has progressively included emphases on cognitive strategies,
metacognition, motivation, and task engagement (for a recent review see Paris &
Paris, 2001). The broad scope of SRL appeals to educational researchers who seek
to understand how students become adept and independent in their educational
pursuits. Whether SRL is viewed as a set of skills that can be taught explicitly
or as developmental processes of self-regulation that emerge with experience
(within a domain, topic, or task), teachers can provide information and
opportunities to students of all ages that will help them become strategic,
motivated, and independent learners. There are, however, several theoretical and
empirical issues that need further research before practical classroom
implications can be put forth. How do students' regulate their own learning when
using a hypermedia environment to learn about complex science topics? Which
processes related to self- and co-regulation do student pairs and teachers use
during collaborative learning of complex science topics with hypermedia? What
kinds of instructional conditions are more effective in fostering SRL? How can
science teachers provide information and opportunities to students of all ages
that can help them become more strategic, motivated, and independent learners?
Can SRL be taught explicitly as a set of skills or is it a developmental process
that emerges from experience (within a task, topic, or domain).This NSF Career project will explore these
questions through research that forges new directions in the area of students'
self-regulated learning of two complex science topics (the circulatory system
and ecological systems) with hypermedia environments (CircSysWeb and RiverWeb).
In doing so, the research goals will be: (1) To scale-up research on
self-regulated learning across developmental levels and contexts; (2) To examine
the role of self- and co-regulation during individual and collaborative learning
with hypermedia environments; (3) To examine the effectiveness of
co-construction of goals (between teacher and students) during learning of
science with hypermedia environments; (4) To examine the effectiveness of
strategy instruction training in fostering students' self- and co-regulated
learning with hypermedia; and, (5) To examine the effectiveness of adaptive
web-based hypermedia environments in detecting, modeling, and fostering
students' self- and co-regulated learning of science.

This award focuses on examining the effectiveness of using animated pedagogical agents (APAs) as external regulatory agents designed to foster middle school and college students' understanding of complex and challenging science topics (e.g., the circulatory system). Contemporary cognitive and educational research provides evidence that the potential of computer-based learning environments for facilitating learning may be severely undermined by students' inability to regulate several aspects of the learning. For example, students should regulate key cognitive, metacognitive, motivational, social, and affective processes in order to learn about complex and challenging science topics. This research will be conducted in the context of a mixed-initiative intelligent tutoring system called AutoTutor that simulates the discourse patterns and pedagogical strategies of human tutors. The focus of our grant is on conducting interdisciplinary research examining: (1) the role of embedded animated pedagogical agents in collecting data of the complex interactions between cognitive and metacognitive processes during learning about complex science topics with AutoTutor; (2) the effectiveness of animated pedagogical agents as external regulating agents used to detect, trace, model, and foster students' self-regulatory processes during learning about complex science topics with AutoTutor; and (3) the effectiveness of scaffolding methods delivered by animated pedagogical agents in facilitating middle school and college students' selfregulated learning about complex science topics with AutoTutor.

This is funding to support travel by 20 students currently enrolled in PhD programs in the United States to participate in the AI-ED Doctoral Student Consortium, at the upcoming International Artificial Intelligence in Education Conference (AI-ED), to be held July 9-13, 2007, in Los Angeles, California. The AI-ED International Conference is the premier biennial event for promoting promotes rigorous research and development of interactive and adaptive learning environments for learners of all ages; AI ED will be the 6th event in the series. The interdisciplinary areas that AI-ED represents, comprising cognitive science, computer science, and educational technology, are critical research domains that enhance the effectiveness and usability of software learning systems. Active participation of young researchers in this conference is very important, both for the health of the field and for the researchers themselves. The AI-ED '07 Doctoral Consortium provides a unique opportunity for PhD students partway through their dissertation research to receive valuable feedback and individual mentoring from top researchers in the field. This support for doctoral students is particularly apt given that currently only 4% of the over 600 members of the AIED society are students, even with 44 countries represented. It is thus timely for NSF to offer this student support. The PI and co-PI both hold active leadership roles within AI-ED '07, and both hold over $2 million of NSF grants in the areas of human-language and advanced learning technologies. The AI-ED '07 Doctoral Consortium Committee will be comprised of the two PIs together with the current president of the AIED society.

Broader Impact: Bringing young and creative researchers to AI-ED '07 will help advance an important and socially valuable interdisciplinary research field. For many graduate students, the cost of attending the AI-ED conference exceeds their travel budget. Thus, NSF funding will significantly impact the careers of the next generation of AIED researchers, by enabling a number of them to take part in an important event they would otherwise have to miss; in particular, those who lack funding from other sources (e.g., advisor's grants). The students will have an opportunity to gain wider exposure in the community for their innovative work, and to obtain feedback and guidance from senior members of the research community. Participation will also help foster a sense of community among these young researchers, by allowing them to create a social network both among themselves and with senior researchers at a critical stage in their professional development. The PI and co-PI have indicated that they will act to assure participation by members of traditionally under-represented institutions, and will pay close attention to inclusion of minorities and women.

Understanding the deployment of cognitive, affective, metacognitive, and motivation (CAMM) processes in the context of hypermedia learning is key to understanding the linear, iterative, and dynamic unfolding of these processes during self-regulated learning (SRL) among successful and unsuccessful learners. However, this is a very difficult problem. The goal of this exploratory project is to use cutting-edge sensors (i.e., physiological devices (e.g., EMG sensors), video capturing and recording devices, eye-tracking equipment, and voice recording to attempt to systematically capture, identify, and analyze the deployment of these processes during complex learning. This project will extend current theoretical,methodological, and analytical methods and tools by: (1) studying how students accomplish learning goals during SRL with hypermedia by experimentally inducing specific SRL processes through the deliberate design of a hypermedia environment; (2) examining the fluctuations in the CAMM processes and how they are related to learning outcomes; and (3) establishing research protocols that maximize researchers? ability to converge and concurrently collect the temporal deployment of CAMM states during SRL with hypermedia via the use of several physiological sensors.

The broader impact of this research includes the design and development of learning interventions for adaptive computer-based learning environments designed to detect, model, trace, and foster students? self-regulated learning. More specifically, instructional prescriptions will be derived focusing on key learning issues such as adaptivity, the role of metacognitive monitoring and control, and the regulation of motivation and affect during hypermedia learning.

This is funding to support travel by 18 intermediate and advanced doctoral students to participate in the AI-ED Doctoral Student Consortium, at the upcoming International Artificial Intelligence in Education Conference (AI-ED), to be held to be held in Brighton, United Kingdom from July 6 to 10, 2009. The AI-ED International Conference is the premier biennial event for promoting promotes rigorous research and development of interactive and adaptive learning environments for learners of all ages; AI ED will be the 7th event in the series. The interdisciplinary areas that AI-ED represents, comprising cognitive science, computer science, and educational technology, are critical research domains that enhance the effectiveness and usability of software learning systems. Active participation of young researchers in this conference is very important, both for the health of the field and for the researchers themselves. The AI-ED Doctoral Consortium provides a unique opportunity for PhD students partway through their dissertation research to receive valuable feedback and individual mentoring from top researchers in the field. The PI and co-PI both hold active leadership roles within AI-ED, and both hold over $2 million of NSF grants in the areas of human-language and advanced learning technologies. The AI-ED Doctoral Consortium Committee will be comprised of the two PIs together with two other senior professors/researchers.

Broader Impact: Bringing young and creative researchers to AI-ED will help advance an important and socially valuable interdisciplinary research field. For many graduate students, the cost of attending the AI-ED conference exceeds their travel budget. Thus, NSF funding will significantly impact the careers of the next generation of AIED researchers, by enabling a number of them to take part in an important event they would otherwise have to miss; in particular, those who lack funding from other sources (e.g., advisor's grants). The students will have an opportunity to gain wider exposure in the community for their innovative work, and to obtain feedback and guidance from senior members of the research community. Participation will also help foster a sense of community among these young researchers, by allowing them to create a social network both among themselves and with senior researchers at a critical stage in their professional development. The PI and co-PI have indicated that they will act to assure participation by members of traditionally under-represented institutions, and will pay close attention to inclusion of minorities and women.

This 3-year project conducts laboratory based experimental research to extend and apply a multiagent intelligent hypermedia-based learning environment to detect, track, and model college students' multiple self-regulatory processes while learning biology. The hypothesis examined by the researchers is that many college students may be hampered in learning biology by their inability to self-regulate themselves. The researchers have developed a Tutor that is expected to provide cognitive, affective, and metacognitive (CAM) support. The proposed research will test experimentally different versions of the Tutor program to examine how self-regulatory processes emerge and the effects of program variations on self-regulatory behaviors.

The investigators will conduct experiments in a laboratory with college students in Memphis Tennessee and Montreal, Canada. The experiment will detect, track, and model the CAM processes in college students' learning about a complex biology topic. During the experimental session they will collect data using a remote eye-tracker to record participants' eye gaze, fixations, saccades, and regressions. The participants' verbalizations will be recorded with a headset microphone. They will use a Pressure Mouse to capture the amount of pressure placed on the mouse throughout the activity, and the Body Pressure Measurement System (BPMS) to assess gross body movements. They will revise and develop new pretest and posttest learning measures for the circulatory system which will include approximately 15 multiple-choice questions, 10 inference questions, labeling tasks, and mental model essays. The measures will assess declarative, procedural, inferential, and mental models of the circulatory system.

The research investigators will examine several theoretical, empirical, and educational questions about self regulation intended to forge new directions of science learning. The team includes psychologists, computer scientists, psychometricians, and electrical engineers. Methodologies will be incorporated from psychology, education, computer science, and electrical engineering to detect, trace, model, and assess students' CAM self-regulatory processes during learning about a complex and challenging science topic. The proposed research activities are intended to advance the science of learning, methodologies, and quantitative analysis of complex sensing data, and education, research, and evaluation, and demonstrate the power of multi-method research tools, software, and sensing devices capable of analyzing and predicting students' self-regulated learning about complex science topics.

The investigators will research how characteristics of intelligent virtual humans (IVHs) support the ability of students to reflect on and, therefore, improve their learning in undergraduate biology. To date, research has shown mixed effectiveness when human avatars are used in learning technologies. To remedy that, the researchers will first study how expert human tutors use verbal and facial cues in reacting to students' cognitive, affective, metacognitive, and motivational (CAMM) processes. Then, they will use these data to build an enhanced intelligent virtual human tutor (by altering software called "MetaTutor"). The project will advance the field's ability to build more effective intelligent tutors and advance understanding of self-regulated learning.

The researchers propose to experimentally study the effectiveness of the enhanced IVHs on learners' self-regulatory processes and other learning outcomes. Data will be collected on both a natural face and a natural face that has been morphed and presented as a virtual human. The facial and verbal expressions are meant to provide learners with an additional information source they can use to monitor and regulate their ongoing self-regulatory processes, including making accurate emotional appraisals. In addition to the facial data, the researchers will collect self-report data, trace data using a variety of sensors, learning outcomes (e.g., pretest and posttest), and knowledge construction activities (e.g., summaries of content, notes, quizzes). Finally, the project will be disseminated in the form of journal publications, conference presentations, and an enhanced version of MetaTutor.

Intelligent, interactive, and highly networked machines -- with which people increasingly share their autonomy and agency -- are a growing part of the landscape, particularly in regard to work. As automation today moves from the factory floor to knowledge and service occupations, insight and action are needed to reap the benefits in increased productivity and increased job opportunities, and to mitigate social costs. Such innovations also have significant implications and potential value for lifelong learning, skills assessments, and job training/retraining in an environment in which workforce demands are changing rapidly. The workshop supported by this award will promote the convergence of cognitive psychology, learning sciences, data science, computer science, and engineering disciplines to define key challenges and research imperatives of the nexus of humans, technology, and work with focus on human affect, motivation, metacognition, and cognition during learning and problem solving. Convergence is the deep integration of knowledge, theories, methods, and data from multiple fields to form new and expanded frameworks for addressing scientific and societal challenges and opportunities. This convergence workshop addresses the future of work at the human-technology frontier.

The specific focus of this multi-phased workshop approach is to advance fundamental understanding of how to collect and analyze multimodal, multichannel sensor on human affect, motivation, metacognition, and cognition during learning and problem solving, and effectively integrate this data into actionable educational interventions in advanced learning technology environments (e.g., intelligent tutoring systems). The impacts of this research extend to a diverse range of learning environments, and job training and retraining opportunities. A multi-phased workshop approach will be used to explore the implications in multiple job sectors, and the outcomes will be broadly disseminated across geographic and disciplinary boundaries.

The project is supported by the Education and Human Resources Core Research program, which supports fundamental research in STEM learning and learning environments. Reflection plays a critical role in student learning and encompasses a broad range of cognitive and metacognitive processes enabling students to (1) think critically about their learning processes, (2) integrate new information with prior knowledge, (3) form and adapt learning strategies, (4) view concepts and knowledge from multiple perspectives, (5) generate self-explanations to enrich conceptual understanding, (6) compare learning processes and artifacts to those created by experts and peers, and (7) make metacognitive judgments about knowledge. The National Research Council recently concluded that systematic reflection is essential for deep learning and effective educational practice. This project investigates a suite of theoretically grounded, adaptive game-based reflection tools to scaffold students' cognitive and metacognitive reflection with the overarching objective of improving middle school students' science problem-solving processes. These reflection tools are integrated as part of the existing Crystal Island learning environment.

In studying middle school students' cognitive and metacognitive reflective processes, three key research questions will be addressed: 1) How do embedded and retrospective reflection scaffolds promote improved learning outcomes including science problem-solving skills, science content knowledge, metacognitive awareness, and reflection skills? 2) How can learning analytics be leveraged to extend models of reflection and self-regulated learning for problem solving within game-based learning environments? and 3) How can we create adaptive scaffolding for game-based learning environments that best foster reflection during and following science problem solving? The project builds upon a theoretical framework of self-regulated learning to inform the development of embedded and retrospective scaffold tools, gathering both process data (e.g., log data, eye tracking, think-aloud) and outcome data (e.g., pre- post- learning, transfer, metacognition, self-efficacy) to inform design principles and descriptive models of the role of reflection in students' problem-solving processes. Key project outcomes include an empirically grounded theoretical framework for reflection-enhanced learning and learning analytic techniques that yield predictive models of reflection in science problem solving.

The project is supported by the Education and Human Resource Core Research program, which supports fundamental research in STEM learning and learning environments. It is important to find new ways to support students' ability to construct, analyze, critique, and use models of STEM phenomena. Given that teachers are the main mediator of any educational innovation, it is imperative to support STEM teachers to effectively engage students in critical thinking skills. This project involves the research and development of MetaDash, a teacher dashboard that provides information regarding students' cognitive, affective, metacognitive, and motivational self-regulatory learning processes during STEM instruction. The dashboard is informed by multi-modal channels that synthesize information such as student facial expressions, eye gaze behavior, electrodermal activity, and verbalizations. MetaDash will impact current teacher training by providing real-time student data (both individual student and aggregated) to enhance instructional decision-making.

Research methodology centers around the design and testing of Metadash as an intelligent, multichannel data visualization tool that displays key aspects of students' learning processes and knowledge construction in real time. The research approach investigates (1) how and when to present the multi-channel input based on human-computer-interaction design principles and informed by teacher usability studies; (2) how to optimize statistical approaches to handle unstructured data from multiple sources; and (3) how to create behavioral signatures for constructs such as self-regulation, motivation and frustration using multi-modal measures such as eye-tracking and facial expression. The ultimate goal of MetaDash is to foster STEM learning by supporting teachers' and students' monitoring and control of CAMM SRL processes. As such, MetaDash will advance current dashboards by providing teachers with: (1) multichannel STEM learning and cognitive, affective, metacognitive, and motivational (CAMM) self-regulated learning (SRL) data collected from students; and (2) individual student and aggregate data to accelerate teachers' decision-making.

The objective of this proposal is to study how emerging Augmented Reality (AR) and Building Information Modeling (BIM) technologies can be integrated to increase human performance by improving the retraining of construction workers for retrofitting and repurposing buildings. A widely recognized problem is the growing population of displaced construction and maintenance works due to automation and artificial intelligence. Additionally, the U.S. demolishes and replaces one billion square feet of building stock with new structures. The impact of the waste produced by the demolition of these buildings on the environment is significant and costly as such materials are often not recycled and are landfilled. Retrofitting construction projects are gaining in popularity because they increase a structure's lifespan, preserve historic elements, and minimize waste. This proposal addresses these critical needs by studying how to help workers more easily transfer trade skills and effectively increase problem-solving skills for retrofitting and repurposing building structures. The investigators will study how AR and BIM increase skill development, productivity, and efficiency to help promote the future of work at the human technology frontier in Architecture, Engineering, and Construction (AEC).

The researchers propose a novel interdisciplinary approach to explore how AR devices superimpose BIM visual information to gain a better and safer understanding of retrofitting and maintenance issues. This grant will make significant research contributions by addressing the following research questions: (1) Will AR technology be useful and impactful for retraining displaced workers in building construction and retrofitting skills? (2) How does the type of visual presentation of building information modeling data and user-interface in the AR display aid and assist retraining skills and problem-solving retrofitting? (3) Does an AR interface help workers problem solve retrofitting tasks more accurately and more efficiently (e.g., in time and waste of materials) than without an AR interface? The study will develop a testbed of mini-labs for improving long-term problem-solving abilities of retrained workers by creating an augmented workforce through in situ training. The investigators will develop and disseminate their research outcomes both to the academic community as well as skilled workers in the building trades. This will pave the way for broad use and adoption of AR and BIM to solve building retrofitting and maintenance challenges and improve workforce capabilities.

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.

Telehealth is greatly affecting our nation’s healthcare system, economy, and workforce, including the training of healthcare professionals (i.e., physicians, residents). This project advances health, psychological, educational, medical, engineering, and computing sciences related to human-machine AI collaboration in delivering high-quality patient care and augmenting medical training and expertise development in telehealth. An interdisciplinary team of researchers from the University of Central Florida (UCF), along with hospital and educational partners from UCF’s College of Medicine, Orlando VA Medical Center, Nemours Children’s Hospital, and AdventHealth University, will augment healthcare-professional training, expertise development, and diagnostic reasoning using AI-based immersive technologies in telehealth. Given the transformative and disruptive impact of telehealth across all societal facets (e.g., racial disparities, economic burden on patients, organizational disruptions, lack of medical training in telehealth), this project assembles, connects, engages, and addresses current local, state, and national disparities by involving academics across disciplines and leveraging evidence-based research with a variety of stakeholders, including government officials, hospital administrators, healthcare professionals and patients, employers, and industry partners. The interdisciplinary research team and collaborating stakeholders work together in the design of a paper-based prototype intelligent collaborative immersive telehealth system for training healthcare professionals and interacting with real human patients, transforming current medical training, fostering expertise development, and significantly enhancing patient outcomes.

This planning grant has three goals. (1) To secure, engage, collaborate, and develop a network with stakeholders, academics, and industry partners across multiple sectors to advance understanding of workforce, health, economic, organizational, AI, technological, and social issues related to telehealth. (2) To test the effectiveness of existing telehealth technologies used by healthcare professionals to understand their impact on diagnostic reasoning by collecting multiple sources of data from healthcare professionals and patients to examine physiological-cognitive-affective-metacognitive-social processes during telehealth interactions. And (3) to design an intelligent collaborative virtual telehealth system prototype that supports healthcare professionals’ diagnostic reasoning, expertise development, and training that can be used in various healthcare scenarios. Intelligent collaborative immersive telehealth systems will augment and transform healthcare professionals’ education, training, and delivery of high-quality medical care. Such systems are key to addressing major societal, health, educational, technological, economic, organizational, and human challenges (e.g., quality of medical care, lack of quality telehealth education and training). The project’s immediate impact will be broadening participation across local, state, and national stakeholders in addressing major issues related to telehealth and how it will transform and disrupt society. Various data types collected from this project will provide evidence of the issues related to medical expertise, diagnostic reasoning, medical errors, and organizational disruptions, and new metrics for measuring the impacts of telehealth. Additionally, hundreds of community members from all sectors will participate in workshops, data collection, and design sessions where they explore current telehealth environments and design a paper-based prototype intelligent collaborative telehealth system to enhance medical education and training to accelerate expertise development, minimize medical errors, and deliver high-quality medical care while fostering the workforce pipeline in healthcare professions and AI in medicine. Additionally, these findings will be broadly applicable to training middle-schoolers and high-schoolers interested in STEM and healthcare professions.

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.