| NSF Org: |
DGE Division Of Graduate Education |
| Recipient: |
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| Initial Amendment Date: | July 27, 2020 |
| Latest Amendment Date: | July 27, 2020 |
| Award Number: | 2039408 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Li Yang
liyang@nsf.gov (703)292-2677 DGE Division Of Graduate Education EDU Directorate for STEM Education |
| Start Date: | August 1, 2020 |
| End Date: | July 31, 2024 (Estimated) |
| Total Intended Award Amount: | $29,719.00 |
| Total Awarded Amount to Date: | $29,719.00 |
| Funds Obligated to Date: |
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| History of Investigator: |
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| Recipient Sponsored Research Office: |
3900 UNIVERSITY BLVD TYLER TX US 75799-6600 (903)565-5670 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
3900 University Blvd. Tyler TX US 75799-0001 |
| 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): | Secure &Trustworthy Cyberspace |
| Primary Program Source: |
04002021DB NSF Education & Human Resource |
| 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
In recent years, researchers have applied artificial intelligence (AI) to effectively solve important problems in cybersecurity. While significant research progress has been made in cybersecurity with the help of AI, there is a shortage of highly educated workers who can solve challenging problems at the intersection of AI and cybersecurity. This project will develop such a workforce by educating qualified individuals from diverse communities in cybersecurity and AI simultaneously. The project team will develop and deliver modular and project-based courses for graduate students that cover the basics of AI and cybersecurity using real-life problems. The development of innovative courses is intended to strengthen the student experience and to build a strong and diverse workforce in AI and cybersecurity that will fill the current voids in government, industry, and academia.
The project team will develop five modular courses for graduate students: (1) Scalable Advanced Analytics, (2) AI including Explainable Machine Learning (ML), (3) ML for Cybersecurity, (4) Cybersecurity for ML (e.g., Adversarial ML), and (5) Secure Blockchain Technologies. The design of these modular and hybrid courses will incorporate research-based pedagogies and innovative technologies. Courses will be offered in both instructor-led and student-directed learning formats to study the differences in learning outcome, if any, between these two different approaches. This project will provide important information regarding optimal methods to deliver interdisciplinary cybersecurity curricula and how the education community can effectively broaden access to cybersecurity education beyond typical classroom courses. The project team will conduct outreach activities to ensure participation by underrepresented populations and will disseminate findings through workshops at relevant meetings of professional societies.
This project is supported by a special initiative of the Secure and Trustworthy Cyberspace (SaTC) program to foster new, previously unexplored, collaborations between the fields of cybersecurity, artificial intelligence, and education. The SaTC program aligns with the Federal Cybersecurity Research and Development Strategic Plan and the National Privacy Research Strategy to protect and preserve the growing social and economic benefits of cyber systems while ensuring security and privacy.
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.
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.
In this grant, we sought to evaluate student performance, self-beliefs, and satisfaction with specialized courses that were developed to address the growing need for a highly skilled cyber security workforce capable of tackling complex challenges. Courses that were evaluated for this grant include: Scalable Advanced Analytics, AI including Explainable Machine Learning, Cybersecurity for Machine Learning, Machine Learning for Cybersecurity and Secure Blockchain Technologies.
Across all five courses, we evaluated if students’ self-efficacy, self-identity as a CyberSecurity/AI professional, and GRIT improved between the beginning and end of the course. Although no statistically significant differences were observed student self-beliefs, we noted that the students generally had better self-beliefs at the end of the course than at the beginning of the course, some of which were practically significant. The largest positive changes occurred in the machine learning for cybersecurity course were self-efficacy and self-identity increased by more than half of a standard deviation.
The scalable data analytics course was offered for credit at UTD three different semesters during the period of the grant and employed both self-directed and instructor-led learning modalities. In the last semester, most students appeared to be satisfied with the learning modalities employed. By the time students reached the final module in the scalable data analytics course, there were no discernible differences in student performance by modality. If these results can be generalized, this suggests that self-directed courses may be created in other disciplines to support the demands of the workforce outside the traditional bounds of University classes. For example, the development of employees in science, engineering, and technology can be facilitated with more autonomous training and development activities. We predict with the rapid development of artificial intelligence, autonomous training, when designed ethically and equitably, will be a viable alternative to instructor-led training.
Last Modified: 10/28/2024
Modified by: Kimmy Nimon
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