Area:
Psycholinguistics, cognitive neuroscience, autism
We are testing a new system for linking grants to scientists.
The funding information displayed below comes from the
NIH Research Portfolio Online Reporting Tools and the
NSF Award Database.
The grant data on this page is limited to grants awarded in the United States and is thus partial. It can nonetheless be used to understand how funding patterns influence mentorship networks and vice-versa, which has deep implications on how research is done.
You can help! If you notice any innacuracies, please
sign in and mark grants as correct or incorrect matches.
Sign in to see low-probability grants and correct any errors in linkage between grants and researchers.
High-probability grants
According to our matching algorithm, Kai-min K. Chang is the likely recipient of the following grants.
Years |
Recipients |
Code |
Title / Keywords |
Matching score |
2011 — 2015 |
Mostow, David Chang, Kai-Min |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Dip: Exploiting Longitudinal Electroencephalogram (Eeg) Input in a Reading Tutor @ Carnegie-Mellon University
Automated (and human) tutors are limited in their ability to infer what is going on in students' heads based on their observable behavior. The proposed work addresses this limitation by investigating how EEG input from a commercially-available device can be used as evidence about students' mental states. In particular, the project focuses on adding EEG-enhanced feedback to Project LISTEN's Reading Tutor, an intelligent tutoring system that helps children learn to read. The project seeks to answer two questions: (1) How can we use EEG to detect mental states that predict, indicate, or reflect student learning? (2) How can we use such detection to improve student learning? Analysis to answer these questions and to enhance the capabilities of the Reading Tutor draws on existing tools to explore annotate, and mine EEG data logged by the Reading Tutor. The research aims to tell us more about how to use EEG to identify mental states that predict learning and to use machine learning to make an intelligent tutoring system better, and it may also add to what is known about sources of reading difficulties. Expected technological contributions of this work include advances in relating EEG data to children's behavior, cognition, engagement, and learning and advances in elucidating how intelligent tutors can robustly exploit noisy EEG input to better assist learning.
The technological innovation in this project is particularly important for those children who need extra help with sounding out, word recognition, and/or making simple inferences needed for understanding.
|
0.915 |
2014 — 2015 |
Chang, Kai-Min |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
I-Corps: Synmetric @ Carnegie-Mellon University
Attrition rates are a serious problem in U.S. education in both STEM and non-STEM fields and in traditional classroom and Massive Open Online Course (MOOC) settings. By detecting confusion and disengagement automatically and in the moment it occurs, the proposed technology enables instructors to respond immediately, spending more time on material when more help is needed. By preventing students from becoming discouraged and disenchanted, attrition rates may be reduced for STEM majors. Reducing the attrition rate by 7% would create half a million more STEM professionals.
This project addresses the growing e-learning phenomenon as well as traditional learning models. It proposes to use what has been discovered about brain signal technologies to improve learner engagement. Decades of research in neuro-signal processing produced methods for measuring a host of cognitive and affective metrics - attention, cognitive load, and engagement are a few. Building on this work, this team developed SynMetric that utilizes methods for detecting important metrics relevant to e-learning using portable inexpensive brain signal sensors - detecting text difficulty, reading comprehension, student confusion while watching course material, and user frustration while using a spoken dialog interface. SynMetric applies brain signal technologies proven in neuromarketing/neurocinema to help instructors better engage students. With SynMetric, instructors receive real-time information on the engagement and confusion level of students without any conscious effort from the student. This process is automatic so students simply listen to a lecture and minute-by-minute feedback is derived from their brain signals and transferred to an instructor dashboard without any conscious effort from the student. Additionally, this technology may inexpensively provide neurologically based measurements of engagement, expanding the traditional neuromarketing/neurocinema market to many other previously untapped markets e.g. small advertising studios, small film studios, independent branding managers, and small firms building their own branding.
|
0.915 |