| NSF Org: |
DRL Division of Research on Learning in Formal and Informal Settings (DRL) |
| Recipient: |
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| Initial Amendment Date: | August 21, 2014 |
| Latest Amendment Date: | August 21, 2014 |
| Award Number: | 1417663 |
| 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: | $124,374.00 |
| Total Awarded Amount to Date: | $124,374.00 |
| Funds Obligated to Date: |
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| History of Investigator: |
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| Recipient Sponsored Research Office: |
4333 BROOKLYN AVE NE SEATTLE WA US 98195-1016 (206)543-4043 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
4333 Brooklyn Ave NE Seattle WA US 98105-1016 |
| 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 REAL project arises from the 2013 solicitation on Data-intensive Research to Improve Teaching and Learning. The intention of that effort is to 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 to revolutionize learning in the longer term. This project addresses the issue of how to represent and communicate data to young people so that they can track their learning and weaknesses and take advantage of what they learn through that tracking. The project team aims to address this challenge by giving young people (middle schoolers) the tools and support to create, manipulate, analyze, and share representations of their own understanding, capabilities, and participation within the Scratch environment. Scratch is a programming language and online community in which youngsters (mostly middle schoolers) engage in programming together, sometimes to make scientific models and sometimes to express themselves artistically using sophisticated computer algorithms. Scratch community participants are often interested in keeping track of what they are learning, so this population is a good one for exploring ways of helping young people make sense of data that records their participation and learning. The team will extend the Scratch programming language with facilities for manipulating, analyzing, and representing such data, and Scratch participants will be challenged to make sense of their learning and participation data and helped to use the new facilities to do write programs to carry out such interpretation. Scratch participants will become visualizers of their participation patterns and learning trajectories; research will address how such data explorations influence their learning trajectories. Scratch and its community are the place for the proposed investigations, but what is learned will apply far more broadly to construction of tools for allowing learners to understand their participation and learning across a broad range of environments.
This project addresses the sixth challenge in the program solicitation: how can information extracted from large datasets be represented and communicated to maximize its usefulness in real-time educational stings, and what delivery mechanisms are right for that? The PIs go right to the learners; rather than looking for delivery mechanisms for communicating the data representations, they give young people tools and support to create manipulate, analyze, and share those representations, bringing together approaches to quantitative evidence-based learning analytics with the constructionist tradition of learning through design experiences. In addition to helping us learn about how to help youngsters analyze data about their perforance and self-assess, the PIs expect that their endeavor will help us better learn how to help young people become data analyzers, an important part of computational thinking. Learners will, in the process of engaging with data representing their development and participation, interact with visualizations, model and troubleshoot data sets, and search for patterns in large data sets. In addition, the tools being developed as part of this project will be applicable for analysis of other types of data sets. The results that will transfer beyond Scratch and the Scratch community, are (1) the kinds of tools that make such analysis possible for youngsters, (2) the kinds of challenges that will get youngsters interested in doing such analyses, (3) the kinds of data youngsters can handle, and (4) the kinds of scaffolding and coaching youngsters need to make sense of that data.
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 New Pathways into Data Science project developed a new way to engage young people (ages 10 to 16) in the practice of data science, introducing new technologies and activities that enable young people to access, analyze, and represent data about their own learning experiences.
In collaboration with a team from the MIT, the University of Washington team provided young people with the opportunity to program personalized projects drawing on data from the Scratch online community (scratch.mit.edu). With Scratch, millions of young people around the world program their own interactive stories, games, and animations – then share their creations with one another in the online community. This project prototyped and tested a new set of graphical programming blocks called “Scratch community blocks” that enable young people to analyze data about participation in the online community, including social data (e.g., the number of times each project had been loved or remixed by others in the community) and programming data (e.g., which programming blocks were used to create a project).
Young people used these new data blocks to design, program, and share a wide range of creative data visualizations. In addition to creating line graphs and pie charts, young people also created new types of data representations, including visualizations based on objects from their everyday lives, such as scoops of ice cream to represent social connections in the online community or colorful balloons to represent different categories of programming blocks. These new types of representations enabled young people to relate to the data in new ways and provided novel ways to communicate about data with peers.
Through ethnographic observations and interviews, the project found that:
- Young people were motivated to analyze data and create visualizations that connected with their interests, experience, and aesthetic sensibilities.
- Explorations involving personally-meaningful data prompted young people to self-reflect upon their own learning and social participation in Scratch.
- As young people analyzed their own data, they engaged in critiques that mirrored some of the current scholarly debates around data science, reflecting on issues such as privacy, anonymity, and algorithmic bias.
Additionally, the project:
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Developed new quantitative techniques for measuring learning in the types of informal programming environments where millions of young people learn to program
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Used these new techniques to test several influential theories about learning in these environments
Since this project was built on top of Scratch, it is well positioned for broad dissemination to the tens of millions of registered members in the Scratch online community. In addition, this research offers valuable insights for other educational initiatives that aim to introduce young people to the ideas and issues of data science. Finally, the project resulted in the creation of a dataset that will make the kinds of studies of learning conducted as part of this work possible by large numbers of researchers who could not have done so before.
Last Modified: 11/28/2017
Modified by: Benjamin Mako Hill
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