Restricted Research - Award List, Note/Discussion Page

Fiscal Year: 2021

134  University of North Texas  (84430)

Principal Investigator: Dantu,Ramanamurthy

Total Amount of Contract, Award, or Gift (Annual before 2011): $ 200,000

Exceeds $250,000 (Is it flagged?): No

Start and End Dates: - 12/15/21

Restricted Research: YES

Academic Discipline: Computer Science & Engineering

Department, Center, School, or Institute: College of Engineering

Title of Contract, Award, or Gift: 2020 University of North Texas DoD CySP Grant

Name of Granting or Contracting Agency/Entity: National Security Agency
CFDA Link: DOD
12.902

Program Title: N/A
CFDA Linked: Information Security Grant Program

Note:

A Novel Work-Skill-Readiness Tool (WSR): We propose to create a Work-Skill-Readiness (WSR) tool that automates the mapping of assessments to the workSkillCredits and KSAs. WorkSkillCredit calculations will be automated in Canvas/Blackboard. This tool is built on top of learning management software (LMS) such as Canvas, Blackboard, and Moodle. When an instructor designs an assessment, the tool determines which workSkillCredits (derived from the job descriptions) are covered and ties them to the assignment so that the Learning Management Software (LMS e.g., Canvas, Blackboard, Moodle) can automatically calculate the workSkillCredit scores. In particular, we are planning to develop special menus/designs where the instructor can select (from a work role dataset in the NICE framework or in portals such as those found on monster.com) a workSkillCredit for a problem in any assessment (refer to the datasets that we created in the previous sections). This would especially apply to online courses at UNT where CLEAR, our LMS management team, requires this mapping (at the Learning Outcome and Module Outcome level). Based on the preliminary results, we successfully matched the UNT computer science program to categories in the work roles of the NICE framework. For further validation of our methodology, we plan to recruit five colleges, implement the tool, and run through all the steps required to match student work skills to the employment categories. All data will be anonymized during analysis. No student names or IDs will be revealed. We will randomly select students in a class and calculate the scores of workSkillcredit scores of the students in all the five colleges. Next, we determine the match percentage of each student's workSkillCredits to the work role's KSA categories. Finally, we compare this match (%) with the ground truth (how many really match to the category with an employer).

Discussion: No discussion notes

 

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