Current Projects


Interpersonal Coordination and Coregulation during Collaborative Problem Solving

Collaborative problem solving (CPS) is an essential 21st century skill in our increasingly connected and globalized world. The hypothesis is that CPS is fundamentally about communication and coordination among people who have thoughts, feelings, and behaviors, and who both react to and influence each other’s thoughts, feelings, and behaviors. The research is theoretically grounded within dynamical systems frameworks, which posit that human interaction is fundamentally coupled and structured by self-organization into functional synergies. One goal of this project is to discover how interpersonal interactions arise and influence CPS processes and outcomes in digital STEM learning environments. A second goal is to model dynamic CPS processes using nonlinear time series analyses and multimodal deep recurrent neural networks. Increasing basic understanding and computational modeling of emergent interpersonal processes is a critical step towards designing next-generation STEM learning environments that aim to make CPS more enjoyable, engaging, and effective. This project is in collaboration with Nick Duran from Arizona State U and Val Shute from Florida State U. 

[This project is funded by the National Science Foundation].


Attention-aware Cyberlearning to Detect and Combat Inattentiveness during Learning

Working with Jim Brockmole, we aim to blend basic research focused on why, when, and how minds wander with advances in eye tracking, mental state estimation, and conversational learning technologies to advance a new genre of attention-aware learning technologies that automatically detect and combat wandering minds. Our exemplary technological innovation leverages emerging consumer-grade eye tracking  and recent advances in mental state estimation to add an attention-aware computing layer to Guru, a cyberlearning technology for high-school biology.  The research will be conducted in 9th grade biology classrooms in Northern Indiana. Learn more here.

[This project is funded by the National Science Foundation]


Precision Education: The Virtual Learning Lab

The R&D Virtual Learning Center is a collaboration between the University of Florida, Notre Dame, and StudyEdge. It will be at the forefront of an emerging field known as “precision education,” using prior students’ data to support the learning opportunities for future students. It will address the challenge of improving algebra outcomes for low-achieving students using Algebra Nation. The research team will personalize Algebra Nation for students by analyzing data from prior users, develop indicators of engagement during learning, and design professional development to help teachers use learning analytics to differentiate instruction. Visit project page here.

[This project is funded by the Institute for Education Sciences]

Exploring adaptive cognitive and affective learning support for next-generation STEM learning games

In collaboration with Val Shute at Florida State University and Ryan Baker at University of Pennsylvania, we will study theoretically-guided learning supports and elements of their design as malleable factors that can improve both the learning experience and  outcomes in STEM learning games. Our goal is to enhance understanding of the types of cognitive and affective supports that promote formal STEM learning, enhance science interest, and improve the learning experience. The knowledge gained will contribute to the design of next-generation learning games that  blur the distinction between assessment and learning.

[This project is funded by the Institute for Education Sciences]



A Big Biodata Approach to Mindsets, Learning Environments, and College Success

We  study how high-school seniors’ mindsets about intelligence and effort, adduced from large volumes of biographical data (biodata), predict college success, and how aspects of the family, home, and neighborhood environment influence thee relationships. Our project leverages a large six-year longitudinal dataset  from a nationally representative sample of U.S. undergraduate students who enrolled in college in 2009. Its sheer size affords a “big biodata” approach to mindset science, featuring machine learning, multivariate clustering, and an emphasis on moderation and generalizability. This project is in collaboration with Angela Duckworth from  University of Pennsylvania.

[This project is funded by the Mindset Scholars Network]

Performance Task Measures of Self-Control and Grit

Our aim is to create a suite of reliable, valid, cost-effective, and easy-to-administer tasks which middle and high school students can complete during a typical school period.  Because they do not rely upon the subjective judgments of students (or teachers or parents), performance tasks offer a potential solution to the reference bias problem. That is, because student behavior on our tasks will be assayed directly, they obviate subjective judgments about behavior that are necessarily influenced by standards of comparison that may vary across schools (or countries or even across time as individuals develop new standards for what “average” looks like).  As an exploratory aim, we will investigate alternatives to performance tasks for assessing grit and self-control, including “big data” techniques (e.g., analyzing patterns of use of Kahn Academy). This project is in collaboration with Angela Duckworth from the University of Pennsylvania and Louis Tay from Purdue University.

[This project is funded by the Walton Family Foundation]

Automating the Measurement and Assessment of Classroom Discourse

For over a century, research has documented the dominant configuration of lecture, recitation, and seatwork in American schools. Recent research looking at the role of classroom discourse, i.e., interactions between teachers and students, has confirmed, as an alternative to this configuration, the importance of open discussions prompted by open-ended teacher questions ("authentic teacher questions") in reading and literature instruction. The goal of our project, using cutting-edge research in speech recognition, discourse classification, and natural language understanding (NLU), is to develop CLASS 5.0, a computer program that will autonomously code classroom interactions between teachers and their students. Collaborators include Martin Nystrand (University of Wisconsin-Madison), Andrew Olney and Art Graesser (University of Memphis), and Sean Kelly (University of Pittsburgh).  Visit project page here.

[This project is funded by the Institute of Education Sciences]

 Some Past Projects


Boredom and Mind wandering during Reading

The foundational research question is how engagement emerges from complex three-way interactions among the learners themselves (i.e., individual differences), the instructional materials (i.e., text difficulty), and the learning activities (i.e., task control and task value). A distinctive goal is to track the dynamics of emergent engagement trajectories via state-of-the-art technologies and methods from affective computing, eye tracking, and nonlinear dynamical systems. The possibility of promoting engagement and learning will also be considered by developing predictive software that selects activities and materials in a manner that is sensitive to the traits, needs, and styles of individual learners.

[This project is funded by the National Science Foundation]

An Online Performance Measure of Academic Diligence

We develop and validate a suite of performance tasks of academic diligence in middle school children. Our approach is grounded in the deliberate practice framework, which posits that skill is the consequence of sustained effort on challenging practice activities, repeated over time, with feedback and guidance. We hypothesize that diligence may distinguish students who persist in tedious tasks from those who quickly disengage by switching to less beneficial but more enjoyable alternatives. The primary outcome of this project is computer software for the psychometrically validated behavioral measure of diligence. The software will be designed to be scalable to run on any computer or mobile device, and extensible to allow other researchers to customize it as needed. This project is in collaboration with Angela Duckworth from the University of Pennsylvania.

[This project is funded by the John Templeton Foundation]

Understanding and Increasing College Persistence

The overall goal of this project led by  Angela Duckworth from the University of Pennsylvania is to provide new insight into student factors that predict college persistence and develop strategies to cultivate them via school-based interventions. The project entails three complementary components: (1) a longitudinal study of urban high school seniors through their first year of college; (2) an in-depth, multi-method study of urban high school seniors who have demonstrated exceptional ­learning trajectories; (3) a series of double-blind randomized intervention experiments with urban high school seniors aimed at improving their mindsets about their academic potential as well as the intellectual and social meaning of critical feedback from college professors. 

[This project is funded by the Bill & Melinda Gates Foundation] 

Increasing Agency by Promoting a Purpose for Learning

In collaboration with David Yeager and Marlone Henderson at UT Austin, this project aims to (1) create and validate three behavioral measures of “academic perseverance” and  (2) develop and experimentally test in urban public middle schools a) a student-targeted “purpose” intervention designed to encourage adolescents to tie academic pursuits to higher-order goals, imbuing them with a sense of purpose around academic work and b) teacher practices designed to further reinforce in students a sense that their academic work serves a larger purpose.  Each intervention in (2) above is predicted to improve outcomes as assessed by the perseverance measures described in (1) above, in addition to raising overall grades.

[This project is funded by the Raikes Foundation and the John Templeton Foundation] 

Emotions while Students Learn from Newton's Playground

In collaboration with Ryan Baker from Teacher's College Columbia and Valerie Shute from Florida State University, this project focuses on building automated detectors of students emotions while they learn physics by playing a fun and engaging educational game called Newton's Playground. The project combines multimodal assessment of student affect and engagement from automated facial feature analysis and interaction patterns with stealth assessment of conscientiousness and conceptual physics understanding. The goal is to figure out the ways that specific affective states disengaged behaviors, and conscientiousness interact and ultimately influence learning. 

[This project is funded by the 
Bill & Melinda Gates Foundation]

Intelligent tutoring system with EEG-based instructional strategy optimization

We are collaborating with QUASAR USA to develop a computerized tutoring platform that adapts its teaching strategy to students in real-time by monitoring their brain activity. As part of this program, we will conduct trials with high school students using its dry electrode headsets to measure EEG in a classroom environment. EEG has the potential to enhance instruction by giving the tutoring system unique insight into the student's cognitive workload and engagement. 

[This project is funded by the National Science Foundation]

GuruTutor: A Computer Tutor That Models Expert Human Tutors

This project, in collaboration with Andrew Olney, investigates expert tutoring mechanisms at multiple levels including models, modes, and moves. We are developing broad computational models of expert tutors that encompasses their pedagogical and motivational strategies, dialogue, language, affective responses, and gestures.  The overall goal of the project is to develop a computer tutor for high school biology based on strategies and dialogue of expert human tutors. The tutor could have a big impact on Memphis City Schools because it seeks to improve educational outcomes on the Tennessee Gateway Science Test, which high school students must pass in order to receive a diploma.[Read More]

[This project is funded by the 
Institute of Education Sciences] 

Confusion and Cognitive Disequilibrium during Learning

This project focuses on the affective state of confusion with an emphasis on the following research questions: (1) What are the appraisals that lead to confusion? (2) How is confusion expressed in the face, speech, body, physiology, language, and context? (3) What are the temporal dynamics of confusion,? (4) How is confusion effectively regulated? (5) When is confusion beneficial for learning? We have developed computerized interventions that induce, track, and regulate confusion to test the hypothesis that there might be some benefits to productively confusing learners. 

[This project is funded by the National Science Foundation]

Monitoring Emotions while Student Learn with AutoTutor

The goal of this research is to build and test learning environments that coordinate complex learning and learner emotions. The project augments an existing intelligent tutoring system (AutoTutor) that helps learners construct explanations by interacting with them in natural language and helping them use simulation environments. The tutorial dialogue of AutoTutor will be enhanced in the proposed research by incorporating signal processing algorithms and sensing devices that classify various facial patterns and affective states of learners. 

[This project is funded by the National Science Foundation]

Robust Automated Knowledge Capture

Working in collaboration with researchers at Sandia National Laboratories, the University of Memphis, and the University of Notre Dame, we attempt to identify skills that may differentially affect performance of individuals in cognitive tasks relevant to flying airplanes and communicating with team members. The project attempts to identify or develop measures to quantify individual ability with respect to each identified skill, particularly the ability to flexibly switch strategies in response to dynamically changing task constraints.

[This project is funded by Sandia National Laboratories]

Cognitive Computing Research Group

Led by Stan Franklin, LIDA is a cognitive architecture that aspires to model several facets of human and animal cognition. LIDA incorporates sophisticated action selection, a centrally important attention mechanism, and multimodal instructionalist and selectionist learning mechanisms. Empirically grounded in cognitive science and neuroscience, the architecture is strictly neither symbolic nor connectionist, but blends crucial features of each. LIDA is a successor of IDA, an agent that helps the Navy by assigning sailors to jobs. IDA is a very complex agent that perceives e-mails from sailors, deliberates on the right jobs for the sailor and negotiates with the sailor in the context of sailor's preferences and Navy's policies.

Memphis Intelligent Kiosk Initiative (MIKI)

MIKI is a three-dimensional directory assistance-type digital persona displayed on a prominently-positioned 50 inch plasma unit housed at the FedEx Institute of Technology at the University of Memphis. MIKI, which stands for Memphis Intelligent Kiosk Initiative, guides students, faculty and visitors through the Institute’s maze of classrooms, labs, lecture halls and offices through graphically-rich, multidimensional, interactive, touch and voice sensitive digital content. MIKI differs from other intelligent kiosk systems by its advanced natural language understanding capabilities that provide it with the ability to answer informal verbal queries without the need for rigorous phraseology. 

[This project is funded by the FedEx Institute of Technology]

Radio Frequency Identification Consortium

This project focused on characterizing the performance of RFID tags in a GHz Transverse Electromagnetic (GTEM) cell. Performance of four commercially available RFID tags manufactured by different vendors was characterized on the basis of horizontal directivity,vertical directivity, sensitivity, and frequency characteristics.

With these baseline characteristics determined, we moved two of the four tags through a real world environment in three dimensions using an industrial robotic system to determine the effect of asset position in relation to the reader on tag readability.