Restricted Research - Award List, Note/Discussion Page
Fiscal Year: 2023
304 The University of Texas Rio Grande Valley (142192)
Principal Investigator: Akundi Vyasa Venkata Naga,Satya Aditya
Total Amount of Contract, Award, or Gift (Annual before 2011): $ 284,583
Exceeds $250,000 (Is it flagged?): Yes
Start and End Dates: 1/1/23 - 12/31/25
Restricted Research: YES
Academic Discipline: Informatics & Engineering Syst
Department, Center, School, or Institute: Informatics & Engineering Syst
Title of Contract, Award, or Gift: Improving Students’ Modeling Skills for Engineering Complex Systems
Name of Granting or Contracting Agency/Entity:
National Science Foundation
CFDA Link: NSF
47.076
Program Title:
STEM Education (formerly Education and Human Resources)
CFDA Linked: Education and Human Resources
Note:
SAMs 1.1.1, 1.3.1 --The lack of well-founded efforts nationwide to enable a digital engineering workforce that can manage a system lifecycle in collaborative virtual environments serves as the motivation for this research project. This research project brings together two major Hispanic serving institutions in the United States i.e., The University of Texas at Rio Grande Valley (UTRGV) and The University of Texas at El Paso (UTEP) to pilot and explore a curriculum integration effort aimed at improving the technical skills of the emerging STEM workforce of the Rio Grande Valley region and the Paso Del Norte region in systems engineering with a focal area of Model-based Systems Engineering (MBSE). The objective of this proposal is to prepare and improve the technical skills of the emerging STEM workforce in Systems Engineering (SE) with a focal area of Model-Based Systems Engineering (MBSE). The project objective will be accomplished by focusing on four research areas: (a) Developing an undergraduate course on Model-Based Systems Engineering to be integrated with undergraduate curricula as a special topics course for senior level students (15 students each year) at The University of Texas Rio Grande Valley and at The University of Texas at El Paso, (b) Enabling a collaborative self-organizing dynamic team experience for geographically separated student teams, (c) Developing a text mining-based machine learning technique to analyze and understand the knowledge gain trajectory of geographically separated student teams on the concepts, guiding principles, and the use of MBSE, and (d) Creating an open-source centralized platform to share project materials and insights with academic institutions seeking to teach MBSE. This project builds on a previously funded NSF ECR: PEER grant (# 1952634) where the PI engaged with subject matter experts in MBSE to identify MBSE tools, techniques, and methodologies regarded to be necessary and important for training future workforce toward a model-based workforce transition. The outcome of this project will aid in strengthening UTRGV and UTEP’s capacity in training the future workforce in MBSE, while also serving as a generalizable knowledge platform for other institutions to explore and integrate dynamic cross-collaborative learning experiences for teaching MBSE.
Discussion: No discussion notes