| NSF Org: |
IIS Division of Information & Intelligent Systems |
| Recipient: |
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| Initial Amendment Date: | August 11, 2018 |
| Latest Amendment Date: | February 14, 2019 |
| Award Number: | 1842693 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Soo-Siang Lim
slim@nsf.gov (703)292-7878 IIS Division of Information & Intelligent Systems CSE Directorate for Computer and Information Science and Engineering |
| Start Date: | September 1, 2018 |
| End Date: | August 31, 2021 (Estimated) |
| Total Intended Award Amount: | $300,000.00 |
| Total Awarded Amount to Date: | $300,000.00 |
| Funds Obligated to Date: |
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| History of Investigator: |
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| Recipient Sponsored Research Office: |
926 DALNEY ST NW ATLANTA GA US 30318-6395 (404)894-4819 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
225 North Avenue, NW Atlanta GA US 30332-0002 |
| Primary Place of
Performance Congressional District: |
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| Unique Entity Identifier (UEI): |
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| Parent UEI: |
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| NSF Program(s): | Cyberlearn & Future Learn Tech |
| Primary Program Source: |
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| Program Reference Code(s): |
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| Program Element Code(s): |
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| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.070 |
ABSTRACT
As the world workforce increasingly pursues technology-focused careers, we see more technology learning environments emerge, such as online courses. There are many advantages to online learning that make it appealing to adult learners, such as low cost, flexibility in times and pacing, and convenience of location. However, there are also many disadvantages that can be attributed to the lack of face-to-face interaction. In online learning environments, instructors cannot observe if students are motivated, engaged, lost or frustrated. This project will investigate the feasibility of using wearable technologies and other types of sensing to gather more context about the online learners. The research will develop techniques for incorporating these complementary sensing technologies to learn more about the online student's environment and their subjective experiences during their participation in online courses. This will generate methods to measure social context in online learning environments. The second key aim of the project is to use insights from wearable devices and other sensing technologies, to design new online learning environments - and the live instruction being given to online students - to better meet learners' needs.
The innovation in this project lies in multi-modal sensing, coupled with modeling of students' cognitive and affective states. Our primary objectives are to collect and identify correlations between wearable sensor data and learning measures; to explore real-time interaction between modeling and online teaching; and to inform improvement of learning systems. The research team will study the needs of students, teachers and administrators of online courses in computer science. This will create a large database; computer infrastructure; methods for integrating across sensing, analytic, and modeling modalities. The project will develop a real-world pilot, with online learners and instructors. Studies of the pilot will confirm the feasibility and acceptability of the infrastructure, and the ability to capture signs of boredom, frustration, delight, and cognitive load.
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.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
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PROJECT OUTCOMES REPORT
Disclaimer
This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.
This study contributed to determining the feasibility of identifying correlations between wearable sensor data and learning measures, and to explore opportunities to help teachers in online courses measure student engagement - similar to the ways teachers "read the room" in an in-person class. Most previous research has relied on self-reporting to identify affect and engagement in classroom settings, which does not reflect just-in-time data that would help teachers "read the room." We decided, thus, to use third-party observations, interviews, surveys, and sensor data in a real-world classroom setting .to provide a more accurate and detailed account of student engagement to correlate with wearable sensor data. The interview and survey provided more promising results for future directions for sensor data informing learning. Based on students' perspectives, we outlined design guidelines to mitigate students' concerns when their affect data are used to improve teaching efficiency at scale and present design recommendations on the physical design of affect sensors in large-scale classrooms. These design guidelines focus on using sensor data to help students with self-regulation rather than informing teachers or administrators. In seeking to correlate sensor data with our observed ground-truth data we found no correlations, which is in contrast to much of the research that shows correlations based upon student self-reporting of affect. This suggests that the current state of wearable sensors cannot be relied upon to accurately track on-task or off-task emotional engagement in online class formats, which suggests we should seek other alternatives to do remote sensing that will be helpful to teachers.
Last Modified: 12/31/2021
Modified by: Betsy Disalvo
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