SBIR/STTR Research Award

SBIR Phase II: Developing a web-based authoring framework for animated interactive university STEM web content via curated crowdsourcing

Award identifier 8592e1588adc8de0576692697973247dea06236f21c55e3c7005911b4ca8b5c8Awarding agency National Science Foundation

Award amount

$709,882

Program and phase

SBIR · Phase II

Award year

2014

Explore This Research Award in CaptureIQ

Research the technology, evaluate the recipient and agency, identify related opportunities, and explore potential follow-on work with CaptureIQ.

Award details

Awarding agency
National Science Foundation
Branch
Not reported
Recipient company
ZYANTE INC.
Proposal award date
2014-09-01
Contract end date
2016-08-31
UEI
KVGGQUMF68J9
Solicitation number
Not reported
Topic code
EA

Research abstract

This SBIR Phase II project develops a new web-based publishing paradigm that creates interactive learning content for university-level science, technology, engineering, and math (STEM) fields. The interactive content, featuring animations, interactive question sets, and online tools, along with some text, may replace or enhance traditional textbooks, with studies showing significant improvements in student engagement and learning outcomes. The project addresses the nationwide high dropout rates of STEM students, due in part to outmoded learning materials. Such dropout rates not only prevent students from achieving their career goals, but result in a shortage of college graduates in nationally-critical STEM fields. The project creates novel web-based tools that empower teachers, and even students, to straightforwardly contribute new interactive content, thus tapping the latent talent and energy of many thousands of people. The project creates automated and human-overseen processes that efficiently and effectively percolate the highest-quality contributions to the top of lists, enabling content to be created and maintained at large scale and low cost. The project's new publishing paradigm can disrupt the existing publishing industry. The interactive quality content can increase the number of students who successfully graduate in STEM fields, which can strengthen the nation's economy and competitiveness. The project develops a new web-based content authoring and delivery framework that supports a novel curated-crowdsourced publishing paradigm. The framework tightly integrates collaborative web-based authoring with delivery of animated interactive web-based content. The project develops elegant tools for instructors, and even students, to contribute new interactive items. Such tools include a novel browser-based animation creation tool, which allows powerful animations to be easily and quickly created via any web browser without requiring application installation or requiring extensive tool training. The project creates and tests new processes that, via a combination of peer evaluations, usage data, and assessments, automatically percolate quality contributions to the top, for final curation by instructors, authors, or editors. Such percolation crowdsourcing can yield superior learning materials, at scale and cost-effectively, while simultaneously yielding substantially lower prices than traditional textbooks.

Technology keywords

Not reported

Other awards by this recipient

Verified by the same UEI. These are other public SBIR/STTR award pages, not a merged company record.

Research funding history

5 SBIR/STTR records share this UEI (2013–2018). Reported award amounts total $1,959,882. Reported amounts are not federal obligations.

Phases: Phase I, Phase II

Agencies: National Science Foundation, Department of Education

Agency intelligence

National Science Foundation

Back to SBIR/STTR Research Awards

Don't take our word for it. Ask AI about RFPCapture.

See what leading AI assistants have to say about RFPCapture and CaptureIQ.

Gemini opens without a prefilled question. The question is copied to your clipboard when the button works. You can also copy it here:

What is RFPCapture.com and its CaptureIQ platform from Smart Logic AI? How does it help federal contractors discover opportunities, evaluate bid/no-bid decisions, research government contracts, and prepare proposals? How does it compare with other federal capture platforms? Use independent sources where available.