SBIR/STTR Research Award

SBIR Phase I: Creating the Critical University-to-Workforce Connection: Providing Quantitative Evidence of Collaboration Skills

Award identifier 616f6ea57d1e9567a612f005692916ec8c5839872f20185a2db92eeac0d8f1abAwarding agency National Science Foundation

Award amount

$224,690

Program and phase

SBIR · Phase I

Award year

2019

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Award details

Awarding agency
National Science Foundation
Branch
Not reported
Recipient company
Beagle Learning, Inc.
Proposal award date
2019-02-01
Contract end date
2020-01-31
UEI
E4NQK5T2H817
Solicitation number
Not reported
Topic code
EA

Research abstract

This SBIR Phase I project will develop new methods for training and assessing the skill of providing and receiving constructive feedback in collaborative environments. The United States higher education system is failing to train collaboration skills, despite companies listing it as one of the most important skills for new hires to have. Past research has identified specific types of feedback that most effectively encourage peers to improve, but today's higher education instructors have no ready means to effectively assess students' abilities to use these peer feedback strategies. This project will develop simple-to-use software tools to help instructors train peer feedback skills and new methods for automatically assessing student ability to provide and receive constructive feedback. By democratizing the training of the collaborative skills most sought after by today's employers, this project aims to reduce underemployment of college graduates and increase the economic output and tax revenues of the United States by building a better trained and more productive workforce. This project will develop an innovative peer-assessment tool coupled with novel natural language processing methods producing accurate measurements of student ability to give and receive constructive feedback based on written feedback. The natural language processing methods will combine word vectors for efficient language processing, a generalizable neural net structure for sentence categorization outlined in existing research literature, and manually defined features based on existing peer feedback research. Research efforts will focus on gathering a large training dataset from partnering higher education classrooms, and iteratively refining a deep-learning natural language processing algorithm to achieve a target 90% accuracy. Automating this assessment process will allow broad and continuous assessment of student collaborative ability without requiring expensive and time-consuming retraining of the United States instructor workforce. 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.

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

2 SBIR/STTR records share this UEI (2019–2020). Reported award amounts total $274,689. Reported amounts are not federal obligations.

Phases: Phase I

Agencies: Department of Defense, National Science Foundation

Agency intelligence

National Science Foundation

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