| NSF Org: |
DRL Division of Research on Learning in Formal and Informal Settings (DRL) |
| Recipient: |
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| Initial Amendment Date: | August 22, 2014 |
| Latest Amendment Date: | August 22, 2014 |
| Award Number: | 1418181 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Gregg Solomon
gesolomo@nsf.gov (703)292-8333 DRL Division of Research on Learning in Formal and Informal Settings (DRL) EDU Directorate for STEM Education |
| Start Date: | September 1, 2014 |
| End Date: | August 31, 2017 (Estimated) |
| Total Intended Award Amount: | $235,354.00 |
| Total Awarded Amount to Date: | $235,354.00 |
| Funds Obligated to Date: |
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| History of Investigator: |
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| Recipient Sponsored Research Office: |
5000 FORBES AVE PITTSBURGH PA US 15213-3815 (412)268-8746 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
5000 Forbes Ave. Pittsburgh PA US 15213-3819 |
| 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): | REAL |
| 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
This Research on Education and Learning (REAL) project arises from an October 2014 Ideas Lab on Data-intensive Research to Improve Teaching and Learning. The intentions of that effort were to (1) bring together researchers from across disciplines to foster novel, transformative, multidisciplinary approaches to using the data in large education-related data sets to create actionable knowledge for improving STEM teaching and learning environments in the medium term; and (2) revolutionize learning in the longer term. In this project, researchers from Carnegie-Mellon University, Wested, Arizona State University, and Northwestern University will collaborate to enhance understanding of influences on learning, and improve teaching and learning in high school and middle school STEM classes. To accomplish this, they will leverage the latest tools for data processing and many different streams of data that can be collected in technology-rich classrooms to (1) identify classroom factors that affect learning and (2) explore how to use that data to automatically track development of students' understanding and capabilities over time.
Two forces are poised to transform research on learning. First, more and more student work is conducted on computers and online, producing vast amounts of learning-related data. At the same time, advances in computing, data mining, and learning analytics are providing new tools for the collection, analysis, and representation of these data. Together, the available data and analytical tools enable smart and responsive systems that personalize learning experiences for individual learners. The PIs aim to collect highly enriched data that go far beyond typical computer data capture, leveraging the latest tools for data processing to generate new insights about STEM teaching and learning. Working to maximize the potential while mitigating the risks of automated data collection and analysis, they will: (1) collect and integrate diverse sources of data including log files, videos, and written artifacts from across eight different two-week enactments of two different computer supported learning environments (one used in middle school math and one in high school science); and (2) compare analyses of log-file data with analyses of integrated datasets to understand the possibilities and limitations in using log-file data for assessment of student learning and proficiency. The collaborators expect their findings will inform both theories and practical recommendations applicable across a wide range of disciplines and settings.
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.
The “big data” movement is having a significant impact on education, especially on efforts to improve teaching and learning in STEM. More and more student work is conducted on computers and online, producing vast amounts of learning-related data. At the same time, advances in computing, data mining, and learning analytics are providing new tools for the collection, analysis, and representation of this data. Together, the available data and analytical tools enable smart and responsive systems that personalize learning experiences for individual learners. The collaborative research project, Learning Linkages: Integrating Data Streams of Multiple Modalities and Timescales, aimed to create novel tools that allow researchers to seamlessly integrate data from a variety of sources (e.g., log files, video data, written text) to create more complete models of learning.
The project yielded two innovative tools that allow researchers to align data from multiple sources to create actionable knowledge that can be used to develop online systems that are tailored to individual learning needs, provide students with information they can use to gauge their own learning, and support teachers as they make instructional decisions to best serve the needs of their students.
The first tool, Structured TRansactional Event Analysis of Multiple Streams (STREAMS) automatically extracts all relevant segments of audio and/or video data based on user-queried events of interest (e.g., all segments pertaining to a particular skill, or all segments of a particular skill that students answered incorrectly on their first attempt). Studies using this tool in high school chemistry and middle school math demonstrated that linking log file data with video and audio showed promise for identifying where and why students were struggling with content, the effectiveness of collaboration, affective issues during instruction, and other in-room events that affect learning in authentic classroom settings.
The second tool used Natural Language Processing to automatically assess open-ended student responses. The Constructed Response Analysis Tool (CRAT), provides an automated means of gauging student understanding from written responses. The tool has been made freely available to the public and is housed on a number of websites (e.g., see Tools at Soletlab.com).
The “big data” movement is poised to have a significant impact on education, especially on efforts to improve teaching and learning in STEM. This project’s broad impact includes contributions to crucial efforts to create more informative and predictive models of what students know and whether they are engaged, generate actionable knowledge that can be used to develop online systems tailored to individual learning needs, provide students with information they can use to gauge their own learning, and support teachers as they make instructional decisions to best serve the needs of their students. To ensure this project has the broadest possible impact, work from the project has been widely disseminated in multiple journal articles, books, chapters, conference proceedings papers, and conference presentations.
Last Modified: 12/14/2017
Modified by: John Stamper
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