Ambitious Mashups: Trends at NSF and Beyond

Lead author: Sarita Pillai; Contributors: Jeremy Roschelle & Judi Fusco

Definition

Cyberlearning projects have been conceptualized and implemented in the midst of a changing educational, policy, and funding landscape. A significant influence on this community has been the evolution of NSF’s mid- and long-term research and investment priorities. In 2017, some of these priorities coalesced in the agency’s 10 Big Ideas. Among the Big Ideas, three have already had notable influence in cyberlearning projects:

  1. Harnessing the Data Revolution. Engaging NSF’s research community in the pursuit of fundamental research in data science and engineering, the development of a cohesive, federated, national-scale approach to research data infrastructure, and the development of a 21st century data-capable workforce.
  2. Convergence Research. Merging ideas, approaches, tools, and technologies from widely diverse fields of science and engineering to stimulate discovery and innovation.
  3. Future of Work at the Human-Technology Frontier. Catalyzing interdisciplinary science and engineering research to understand and build the human-technology relationship; design new technologies to augment human performance; illuminate the emerging socio-technological landscape; and foster lifelong and pervasive learning with technology.

A fourth Big Idea, INCLUDES, forms alliances to transform education and career pathways to help broaden participation in science and engineering. And more recently, NSF 2026 which seeks community input into foundational research areas of the future that are boundary-crossing and out of the programmatic ‘box’ and that require long-term commitment at a significant scale.

Please see the full report for additional information on how this theme changed over time in cyberlearning research and for some questions that arose as we investigated the theme.


Project Examples and Resources

Data Science Learning

  • 20 Projects
  • 5 sessions at cyberlearning convenings

Stimulating Quotes and Snippets

  • “All of the problems that face our civilization require people who are expert with data to be part of the solution. There is already a severe shortage of such people. As students in K-12 schools learn with data they will develop the intuitions, skills, and data habits of mind that will turn them into 21st century problem solvers” – Bill FInzer
  • “They are using it to answer questions, and they are providing brand-new, hyper-local data to scientists that they would never have had otherwise. Plus, rural community members are connecting with scientists and experts who can provide valuable insight into local decision making that the rural communities would have never had” – Ruth Kermish Allen

Example Project Abstracts:

Related Primers/Spotlights/Reports:

Showcase Videos and/or Gallery Posters:

Cross Connections:
Data Visualization, Smart & Connected

Exit Survey Highlights (30 total responses): Data Science Learning

  • Projects tagged as Data science education: 3
  • Project Implementation Setting:
    • 1: Formal school setting
    • 1: Formal school setting and informal learning setting (summer program)
    • 1: Workplace
  • Special populations targeted:
    • 1 data science education project targeted learners in schools with 50% or more students receiving FRPL and women/girls.
  • Project included teacher/practitioner partnerships: All 3 projects included a teacher/practitioner collaboration or partnership, indicating that these projects are actively seeking out teacher partnerships and incorporating their input into the development of project resources/products for teaching and learning data science.
  • PI proposals and awards:
    • PI received new Cyberlearning award: 1
    • PI submitted proposal to Future of Work program: 0
    • PI received an NSF award for a program other than Cyberlearning: 2 (ITEST, STEM+C, AISL)
  • Project included teacher/practitioner partnerships: 5/10
    • Only 50% of these projects included a practitioner partnership, which is a lower percentage than projects addressing other CL themes.
  • Project included 2 or more grad students on project staff: 13/17; CL projects focused on methodological approaches provide good opportunities for engaging and training grad students, with several projects employing 5+ grad students.
  • PI proposals and awards
    • PI received new Cyberlearning award: 5
    • PI submitted proposal to Future of Work program: 3 (more than projects addressing the other CL themes)
    • PI received an NSF award for a program other than Cyberlearning: 8
    • More PIs received awards from other NSF programs, such as CS for All, AISL, STEM+C, and ITEST, than CL awards.
  • Project Publications:
    • 1/3 indicated that they published project findings in scholarly journals, including: Technological Innovations in Statistics Education
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