
NSF Org: |
IIS Division of Information & Intelligent Systems |
Recipient: |
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Initial Amendment Date: | September 7, 2016 |
Latest Amendment Date: | September 7, 2016 |
Award Number: | 1628976 |
Award Instrument: | Standard Grant |
Program Manager: |
Tatiana Korelsky
IIS Division of Information & Intelligent Systems CSE Directorate for Computer and Information Science and Engineering |
Start Date: | September 1, 2016 |
End Date: | August 31, 2021 (Estimated) |
Total Intended Award Amount: | $1,349,576.00 |
Total Awarded Amount to Date: | $1,349,576.00 |
Funds Obligated to Date: |
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History of Investigator: |
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Recipient Sponsored Research Office: |
506 S WRIGHT ST URBANA IL US 61801-3620 (217)333-2187 |
Sponsor Congressional District: |
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Primary Place of Performance: |
1901 S. First Street, Suite A Champaign IL US 61820-7473 |
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): |
Science of Learning, S-STEM-Schlr Sci Tech Eng&Math, Cyberlearn & Future Learn Tech |
Primary Program Source: |
1300XXXXDB H-1B FUND, EDU, NSF |
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
The Cyberlearning and Future Learning Technologies Program funds efforts that support envisioning the future of learning technologies and advance what we know about how people learn in technology-rich environments. Development and Implementation (DIP) Projects build on proof-of-concept work that shows the possibilities of the proposed new type of learning technology to build and refine a minimally-viable example of their proposed innovation that allows them to understand how such technology should be designed and used in the future and that allows them to answer questions about how people learn, how to foster or assess learning, and/or how to design for learning. This project is focused on the teaching of collaborative problem solving activities in introductory engineering courses and builds on a prior project to design tools for collaborative sketching in these courses. The project is based on a recognition of the importance of collaborating in engineering, the need for student to learn this skill, the value of collaborative learning tasks for engaging students in authentic problem solving activities, and the difficulty that graduate student teaching assistants (TAs) encounter when trying to teach in this way. There are two parts to the technology innovation. The first part is a set of tools for the teaching assistants, to help them manage the classroom technologies, and to help them understand how to intervene in groups who are struggling with the content or collaborative processes. The second part is a set of tools for the students. Building on the collaborative sketch software previously developed, prompts to support their collaborative processes will be embedded in the software students will use, based on analysis of the logfiles that help determine who needs what prompts when. Research goals include understanding how receiving prompts changes the nature of students' collaborative activity, and how receiving insight into the difficulties students are having helps TAs learn about to foster collaborative learning in their classes.
The PIs are addressing the difficulties encountered implementing collaborative learning activities in engineering courses by designing and studying tools for TAs and students in these classes. Through an iterative design approach, the PIs will design and study tools for TAs to orchestrate the classroom and collaboration activities and to tools for students which support their collaborative problem solving processes. The PIs will investigate the use of learning analytics in evaluating the collaborative practices of students using these tools; in particular, logfiles will be examined for collaborative indicators based on prior research on collaborative processes, then clustered to look for patterns of engagement, and finally used to create regression models of successful collaboration processes using machine learning techniques. Cross-validation of the models will be done with both logfile and video data to avoid overfitting. These insights will be provided to TAs to examine whether such information is helpful in determining how and when to intervene in groups. Findings from the research will provide insight into: 1) The knowledge that TAs need in order to successfully implement collaborative problem solving in undergraduate courses; 2) Whether TAs can learn more about collaborative problem solving with the support of tools aimed at helping them implement this form of pedagogy; 3) Whether students can learn collaborative problem solving skills through embedded prompts during multi-week collaborative activities and 4) The potential of analytics in determining when and how to reduce the collaboration supports from groups.
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.
It is essential that students graduate from STEM programs with strong collaboration skills in order to effectively participate in the workforce of today and tomorrow. Additionally, the use of collaborative problem-solving activities allows students to learn how to use the language of their discipline, and how to approach complex problems. However, implementing successful collaboration in classrooms is not simple, particularly in large undergraduate required courses, which are often taught by graduate student teaching assistants with little or no teaching preparation.
In this project, we focused on collaboration in an introductory required engineering course, that enrolls about 1,000 students per year, with students meeting in discussion sections of about 30 students each week. Building on our prior work, we used a tablet application that allows group members’ work to be synchronized within their group, allowing for the creating of joint representations during collaborative problem solving. We built an instructor dashboard that allowed the instructors to track activity of each group, and in the second iteration, provided live alerts to problematic behavior.
From the student software, we collected log data of groups of about 100 students in group of 3-4 solving collaborative problems each week, as well as video data of their interactions. We identified interaction behaviors in 20-second clips of video, and then created models of log data using data mining techniques, that corresponded with the interactions.
We identified two key behaviors in the log data: off-topic activity and silent on-task (everyone was working, but not talking) that could be reliably identified to produce a proof-of-concept tool. In the next semester, instructors were sent alerts when these behaviors were identified in the logs. The instructors was alerted to monitor the group for the behavior, and if they agreed the behavior was present, they confirmed the alert and received a suggestion for how to intervene. Initial analysis found that instructors who valued collaboration were more likely to use the tool and find the prompts useful. Future work will extend the live prompt tools to a broader range of behaviors.
The project had an impact on the teaching of engineering at the University of Illinois Urbana Champaign by adapting tasks to support collaborative learning, and providing guides for future instructors for how to successfully implement collaborative learning. Over 300 students used the tools we developed, while over 3000 students per year (across math and engineering) use tasks developed during this project.
Last Modified: 02/23/2022
Modified by: Emma Mercier
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