Mark Hasegawa-Johnson (Expertise in CA speech processing, ECE); Suma Bhat (Expertise in Natural Language Processing, ECE); Dan Morrow (Expertise in behavioral-social science, Educational Psychology); James Graumlich (Expertise in medicine, UIC Medical School and OSF Health System)
The research team’s goal is to develop and evaluate a Conversational Agent (CA) system to support self-care. Interactive CAs in particular have the potential to present health information tailored to older adults when and where needed, thus expanding opportunity for patient education in a health care system where providers do not have the time to effectively educate their patients. More broadly, CA systems may support independent living and work at home in times of public health crises such as the current COVID-19 pandemic. The project aims to conduct a follow-up study to further investigate the potential of CA-based teachback to support older adult learning about self-care and also refine the interactive capabilities of the CA system.
Nigel Bosch (Principal investigator, Expertise in Self-regulated learning, machine learning and equity for students from traditionally underrepresented groups in digital learning environments); Suma Bhat (Co-principal investigator, human-computer interaction, machine learning, and analysis of online STEM learning environments); Paul Hur (Conducting in-depth interviews in HCI, PhD student)
This research team is proposing a generalizable, interdisciplinary approach to provide individualized Self-Regulated Learning (SRL) training in online courses as a means of addressing the growing need to improve online learning and the issues of equity that go with it. In particular, they propose to integrate research on SRL with machine learning to intelligently select and display interventions designed to promote the SRL skills most needed by a particular student at the right time. Their method aims to provide personalized SRL interventions in large-scale online courses, utilizing machine learning to predict which students need intervention (via student outcome prediction) and what that intervention should be (via Shapley analysis of the model’s prediction for that student).
Stina Krist (PI, Assistant professor in Curriculum & Instruction UIUC, Expertise in students’ epistemic views and practices in K-12 science learning settings, how teachers learn to create and sustain environments that foster students’ epistemic agency); Eric Kuo (Co-PI, Assistant professor in Physics UIUC, Expertise in students’ epistemic views in physics, and how they inform the learning and application of problem-solving strategies in undergraduate courses); Joshua Rosenberg (Co-PI, Assistant professor of STEM Education at the University of Tennessee, Expertise in student engagement across a range of STEM learning environments and the use of technology in education particularly for teacher professional development )
This project proposes to implement a feedback system for formative, automated assessment of student thinking (FAAST) and to use this feedback to fuel teacher professional development. The FAAST feedback system uses a blend of machine-learning techniques and human-driven inductive coding to provide immediate feedback to students and teachers on classroom-level patterns in thinking. Currently, the FAAST system has been designed to categorize epistemic thinking about how general or specific scientific explanations should be. This project will use the FAAST system to support teacher reflection on epistemic learning goals in science.
Kevin Wise (PI, Professor of Advertising); Matthew Peterson (Co-PI, Assistant Professor of Graphic Design at North Carolina State University); Xiaoyu Xu (PhD student at Institute of Communications Research)
This research will explore the relative effects of adjuncts and textual labels as interactive overlays in multimedia for science learning. To what extent do virtual adjuncts and configurations enhance visual cognition, resource availability, and meaning comprehension? To what extent do students utilize these features while consuming multimedia for science learning, and how do their effects unfold dynamically over time? The answers to these questions have the potential to broadly impact both the understanding and design of educational technology across a wide range of learning domains (e.g. biology, ecology, physics) and, to a lesser extent, individual differences (e.g., age, expertise, socioeconomic status) among users of such technology. This project will help to realize the potential of the taxonomy for improving scientific visualization, providing experimental evidence of how cognitive image functions impact the interpretational processes that lead to learning.
Paul Hur is a doctoral student at the School of Information Sciences. Before coming to UIUC, he studied psychology and UX research at the University of North Carolina at Chapel Hill and University of Michigan, respectively. He has previously worked on projects involving knowledge representation, information crowdsourcing, and behavioral economics. His current research combines methods from UX research, educational data mining, and learning theory to study self-regulated learning and learning behaviors. Paul’s TIER-ED Fellowship project aims to improve the consistency of engagement quality in online forum discourse in classes at UIUC by integrating nudging, incentive-centered system design, and automatic text quality detection. He believes this project will unveil insights about learners’ perception and motivation for participating in online forum discourse, and the extent to which interventions may support learners.
Hannah Valdiviejas a fourth-year doctoral student in Educational Psychology under the cognitive science of teaching and learning (CSTL) division. She completed B.A. in Psychology and Spanish from Northeastern Illinois University in 2017 and recently received M.S. in Educational Psychology from UIUC. Hannah is a proud product of a community college and Hispanic serving institution, which shaped her academic and career goals. Her scholarly interests center around STEM educational equity within topics like dual-language math education, self-regulated learning in the online context, and learning analytics. Hannah’s research interests include understanding the experiences of students of color in STEM majors and programs at predominantly White institutions. In doing so, she aims to inform existing models of self-regulated learning to be culturally sensitive, responsive, and inclusive. Hannah’s current research focuses on creating a measure that captures expressions of the “Twice As” phenomenon, rooted in the pop-cultural saying that emphasizes the need for people of color to “work twice as hard to get half as far” as White counterparts in the U.S.. I am interested in how the “Twice As” phenomenon (i.e., academic over-exertion to disprove stereotypes regarding intelligence) psychologically and academically impacts STEM students of color in online education.
Cameron Merrill is a PhD candidate in Human-Computer Interaction in the Department of Computer Science. He is passionate about building and researching technologies to increase the accessibility of learning experiences. Cameron’s research investigates immersive virtual learning environments and game-based learning. Cameron’s previous research has explored virtual reality educational applications in a variety of contexts including Archaeology and Second-Language Learning. For much of his graduate degree, he has led development on the Virtual Archaeology (VRchaeology.com) project, creating virtual reality cave systems and excavation pits for students to learn Archaeological concepts not usually taught in the classroom. An Unreal Engine enthusiast, Cameron received an Epic Games MegaGrant in 2020 to continue the project. Before joining the Human-Computer Interaction group in the Department of Computer Science, Cameron was a cybersecurity researcher at Michigan State University and Harvard University. Outside of the research lab, he is a Big Ten football and basketball fan, and frequents Krannert classical music concerts.
Kutasha Bryan-Silva is a doctoral candidate in Curriculum & Instruction. Her research has centered on early childhood and focused on foundational literacy development in Puerto Rico, Global Citizenship Education in Hong Kong, and best practices for early childhood e-learning. As a 2021 Fulbright Scholar she aims to look through the international lens of Uruguay, South America to investigate how young children can be taught sustainable development concepts through the intentional interconnection of technology and traditional ecological knowledges. The research cite selected is considered a national model for educational technologies and environmental education in Uruguay. Through the theoretical framework of Knowledge Building, Bryan-Silva aims to investigate how educational technologies (e.g. micro bits, QR codes, iPad) support young children at the public ecological school in developing competencies to create clean energy and food security that is beneficial to their immediate communities. Her goal is that data collection in Uruguay will provide an understanding of what can be possible in the United States if greater emphasis on educational technologies and sustainable development was placed within educational policy and national curricula.