| NSF Org: |
DUE Division Of Undergraduate Education |
| Recipient: |
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| Initial Amendment Date: | July 19, 2019 |
| Latest Amendment Date: | July 19, 2019 |
| Award Number: | 1933078 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Abby Ilumoka
DUE Division Of Undergraduate Education EDU Directorate for STEM Education |
| Start Date: | October 1, 2019 |
| End Date: | September 30, 2023 (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: |
21 N PARK ST STE 6301 MADISON WI US 53715-1218 (608)262-3822 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
21 North Park Street Madison WI US 53715-1218 |
| 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): | IUSE |
| 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): | 47.076 |
ABSTRACT
With support from the NSF Improving Undergraduate STEM Education Program: Education and Human Resources (IUSE:EHR), this project aims to serve the national interest by improving engineering students' ability to understand graphs and other visual representations. Engineering instructors often use visual representations, such as Cartesian and polar coordinate graphs, to help students learn. However, these visualizations can be confusing to students unless they know how the visualizations show information. This project will use an intelligent tutoring system to help students learn how particular visual features show specific foundational engineering concepts. It will also target student fluency in understanding visual representations, akin to fluency in a language. The intelligent tutorial system will also train students to imagine the visuals when they are no longer present. It is expected that these "internal" visualization skills will help students learn more easily from text and equations. Experiments will investigate which supports lead to most improvement in students' internal visualization skills and content learning. The experiments will be carried out in situations in which students learn individually and collaboratively, allowing the project team to establish which aspects of visual representations are best learned alone and which are best learned in a team. Such understanding is important for understanding the limits and maximizing the effectiveness of active learning classes.
Internal visualization skills enhance learning when the visual representations are subsequently replaced by abstract equations. Two critical knowledge gaps will be addressed in the project: i) how best to support representational competencies in a way that enhances internal visualization skills; ii) how to effectively support representational competencies for both individual and collaborative activities, the latter being increasingly important in active learning. The project will provide new insights into how instruction can help students benefit from visual representation. Although the project focuses on the learning of foundational engineering concepts, it may yield results that inform the development of educational technology resources for other disciplines. The project will lead to an innovative educational technology resource that provides support for individual and collaborative learning with visuals, which may enhance students' success in STEM. The NSF IUSE: EHR Program supports research and development projects to improve the effectiveness of STEM education for all students. Through the Engaged Student Learning track, the IUSE program supports the creation, exploration, and implementation of promising practices and tools.
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.
Visualizations are often used to teach students about new concepts. Teachers often assume that students understand these visualizations. But visualizations can be confusing if students do not know how they show information about the to-be-learned concepts. The goal of this project was to help students learn with visuals. We developed an educational technology for an undergraduate engineering course on signal processing. The technology, called Signal Tutor, helps students learn how visualizations show information. Within the Signal Tutor, students use interactive visuals to solve engineering problems, similar to those that they would normally work on during class. While they interact with the visualizations, students receive feedback from Signal Tutor about whether they interpreted the visualization correctly.
Through a series of studies, the project tested how best to support students’ ability to interpret visualizations. The project focused on two types of skills: the ability to make sense of how particular visual features show particular concepts, and students’ fluency with visuals (akin to fluency in a language). To this end, we developed supports for sense-making and fluency that we incorporated into Signal Tutor. We then conducted a series of experiments will investigate how best to combine support for sense-making and fluency, with the goal to optimally enhance students’ learning of engineering content knowledge.
A first step in the project was to develop the Signal Tutor system. To this end, the team worked closely with teachers at 2-year and 4-year colleges to examine the structure of problem-solving activities and to try out early versions of the system with students. Throughout the remaining project activities, the team continuously improved Signal Tutor based on feedback and learning data.
The second step was to examine how to assess students’ skills in working with visualizations. Through a series of studies that combined insights from interviews, tests, student explanations with gestures, and log data from Signal Tutor, the team devised of a way of determining students’ knowledge about visualizations based on log data.
The third step was to test what combination of support for sense-making and fluency would best enhance students’ learning outcomes. A series of experiments demonstrated that students’ long-term learning gains benefit most from a combination of sense-making and fluency supports that are provided in each unit of the Signal Tutor system.
Finally, the project involved dissemination activities. Through interactions with teachers, project presentations and publications, and YouTube videos, the findings from the project have become available to broad audiences. Additionally, the Signal Tutor system is available for free to students and instructors.
Last Modified: 05/30/2024
Modified by: Martina A Rau
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