Data Collection Plan – Masters FSU

HBCU Master’s Degree Program – Data Collection Plan

Purpose of Data Collection

This comprehensive data collection plan ensures systematic tracking of program performance against all six objectives and their associated performance indicators. Data collection supports continuous improvement, accountability, and evidence-based decision-making throughout the grant period.

Key Principles: Data will be collected consistently, analyzed regularly, and used to inform program adjustments. All data collection methods comply with FERPA and institutional research protocols.

Primary Data Sources: Institutional records, online surveys, program logs, faculty reports, and external sources will provide comprehensive evidence of program outcomes and impacts.

Institutional Data

Enrollment, graduation, retention rates from university registrar and IR office

Online Surveys

Student feedback, graduate outcomes, program satisfaction assessments

Program Logs

Scholarship awards, participation tracking, event attendance records

Faculty Reports

Publications, presentations, grant activities, professional development

External Sources

Employer surveys, partnership agreements, state education records

Objective 1: Increase Graduate STEM Enrollment and Degree Completion

Track enrollment growth, degree completion, research engagement, and time-to-degree metrics

Data Point & Description	Data Source	Collection Frequency	Performance Indicator Link
Total Graduate STEM Enrollment Number of students enrolled in MAT, MEd, MBA, MSN, MS programs in targeted STEM disciplines each fall semester	Institutional Data University Registrar’s Office / Institutional Research	Annual Census date each Fall semester	PI 1.1: 3% annual enrollment increase (Baseline: 201, Target: 207, Year 1: 208)
Enrollment by Program Breakdown of students by specific degree program (MAT Math, MEd Science, MBA BIDA, MSN, etc.)	Institutional Data Registrar / Program Coordinators	Annual Each Fall semester	PI 1.1: Track enrollment trends by program to identify growth areas
Graduate Degree Completions Number of students earning master’s degrees in targeted STEM disciplines annually	Institutional Data Registrar’s Office / Institutional Research	Annual End of each academic year (Summer graduation)	PI 1.2: 3% annual increase in degrees earned (Baseline: 65 for 2023-2024)
HBCU Scholar Research/Clinical Participation Number and percentage of scholarship recipients engaged in research projects, apprenticeships, or clinical experiences	Program Logs Faculty Reports Program office tracking + Faculty advisor reports	Semester End of Fall and Spring semesters	PI 1.3: ≥75% of scholars in experiential learning (Year 1: 80%)
Research Project Titles & Details Documentation of specific research projects, apprenticeship placements, and clinical experiences with faculty mentors	Student Reports Program Logs Student research forms + Program database	Semester End of each semester	PI 1.3: Qualitative evidence of research engagement and quality
Median Time to Degree Completion Average time from enrollment to graduation by program type (MAT: 2 years, MEd: 2 years, MBA: 2.5 years, MSN: 2 years, MS: 2 years)	Institutional Data Registrar / Institutional Research cohort analysis	Annual End of each academic year	PI 1.4: Median time meets program-specific targets (2-2.5 years)
Student Demographics Race/ethnicity, socioeconomic status (Pell-eligible), gender breakdown of enrolled students	Institutional Data Student records / Financial Aid office	Annual Fall census date	PI 1.1: Monitor diversity and ensure broad participation across student populations

Why This Data Matters

Objective 1 data demonstrates the program’s core impact on access and completion. Enrollment trends show whether financial assistance and support services are effectively attracting students. Research engagement metrics validate the quality of experiential learning. Time-to-degree data ensures efficiency and student success, while completion rates measure the ultimate goal of degree attainment.

Objective 2: Strengthen STEM Teacher Preparation

Monitor teacher candidate enrollment, licensure completion, program cost-effectiveness, and infrastructure development

Data Point & Description	Data Source	Collection Frequency	Performance Indicator Link
HBCU-MD STEM Teacher Scholarships Awarded Number of scholarships awarded annually to teachers, teacher candidates, or residency licensure participants in STEM disciplines	Program Logs HBCU Grant Office scholarship database	Annual End of academic year	PI 2.1: Award minimum 10 scholarships annually
Scholarship Recipient Profiles Background information (current teacher, residency participant, or initial licensure candidate), program of study, demographics	Program Logs Institutional Data Scholarship applications + Student records	Annual At scholarship award time	PI 2.1: Ensure scholarships target appropriate teacher populations
MAT/MEd STEM Program Enrollment Total number of students enrolled in MAT and MEd programs in STEM teaching disciplines (Math, Science)	Institutional Data Registrar / College of Education records	Annual Fall census date	PI 2.2: 3% annual enrollment increase (Baseline: 27, Year 1: 28)
Teacher Licensure Completions Number of graduates obtaining initial or advanced teaching licensure in STEM disciplines	Institutional Data External Data College of Education + NC DPI licensure records	Annual End of academic year	PI 2.2: 3% annual increase in completions (Baseline: 13)
Cost of Attendance per Graduate Total program cost divided by number of graduates in supported academic programs	Institutional Data Program Logs Finance office + Grant expenditure tracking	Annual End of fiscal year	PI 2.3: Cost per degree ≤ $60,000 (Year 1: $21,743)
COE STEM Education Center Construction Progress Milestone completion percentages: Planning (Year 1), Construction (Years 2-3), Completion (Year 4)	Project Reports Facilities Management + Construction contractor updates	Quarterly Construction progress reports	PI 2.4: 20% Year 1, construction 2024-2026, completion 2026-2027 (Year 1: 20% complete)
Teacher Placement Data Employment outcomes for teacher graduates (school district, grade level, subject area)	Graduate Survey External Data Alumni surveys + NC DPI employment records	Annual 6-12 months post-graduation	PI 2.2: Validate completions translate to teacher workforce contribution

Why This Data Matters

Objective 2 data addresses the critical teacher shortage in STEM fields. Scholarship and enrollment data demonstrate program reach to teacher candidates. Licensure completion rates validate program effectiveness in preparing qualified STEM teachers. Cost-effectiveness metrics ensure efficient use of grant funds. Infrastructure tracking ensures modern facilities support teacher preparation. Placement data confirms graduates enter the teaching workforce, directly impacting K-12 STEM education quality.

Objective 3: Enhance Program Relevance Through Expansion

Track new program development, facility upgrades, and alignment with industry demands

Data Point & Description	Data Source	Collection Frequency	Performance Indicator Link
New Concentrations/Certificates Launched Number and names of new concentrations and certificates implemented (Fintech, Digital Enterprise, Instructional Technology, Cybersecurity, ERP-SAP, etc.)	Institutional Data Program Reports Academic Affairs / Program Directors	Semester When programs launch	PI 3.1: Minimum 3 new concentrations/certificates by Year 4 (Year 1: 2 launched + ERP initiated)
Program Development Milestones Timeline of curriculum development, approval processes, accreditation reviews for new programs	Program Logs Status Reports Program development team + Academic Affairs	Ongoing Tracked continuously	PI 3.1: Monitor progress toward 3+ program launches
Enrollment in New Concentrations Number of students enrolled in each new concentration/certificate program each semester	Institutional Data Registrar / Program Coordinators	Semester Each Fall and Spring	PI 3.1: Assess demand and viability of new programs
MS in Data Science Proposal Status Progress through development stages: Intent to Plan, full proposal development, submission to UNC System, approval status	Status Reports External Data Program development team + UNC System Office communications	Quarterly Development phase tracking	PI 3.2: Proposal submission by Year 3 (Year 1: 50% – Intent to Plan submitted)
Facility Upgrades Completed List and status of laboratory, technology space, and equipment upgrades (Bloomberg Terminals, Data Science Lab, etc.)	Project Logs Facilities Reports Facilities Management + IT Services + Grant office	Ongoing Project completion tracking	PI 3.3: 100% completion by Year 6 (Year 1: 50% complete)
Technology Acquisitions Equipment, software, and technology purchased and deployed (with costs and implementation dates)	Purchase Logs Institutional Data Procurement + IT Services + Grant budget tracking	Ongoing As purchases occur	PI 3.3: Document technology infrastructure enhancements
Industry Partnership Agreements Documentation of partnerships with companies (SAP, SAS, IBM, etc.), including services and resources provided	Partnership Docs Program Logs Partnership agreements + Program office records	Annual Partnership review	PI 3.1 & 3.3: Demonstrate industry alignment and support for new programs
Student Satisfaction with Facilities Survey responses on quality and adequacy of labs, technology spaces, and equipment	Student Survey End-of-semester program evaluation surveys	Semester End of Fall and Spring	PI 3.3: Validate that upgrades enhance student learning experience

Why This Data Matters

Objective 3 data demonstrates the program’s responsiveness to evolving industry needs and educational trends. New concentration tracking shows curriculum innovation in high-demand fields like fintech, cybersecurity, and data science. The MS in Data Science development represents a major institutional expansion. Facility upgrade documentation validates investment in learning infrastructure that will serve students beyond the grant period. Industry partnership data confirms programs meet employer needs, enhancing graduate employability.

Objective 4: Support Faculty Research and Development

Monitor faculty scholarly productivity, professional development, and research support

Data Point & Description	Data Source	Collection Frequency	Performance Indicator Link
Faculty Travel Grants Awarded Number of faculty receiving travel grants for conferences, workshops, and professional development events	Program Logs Grant office travel grant database	Annual End of fiscal year	PI 4.1: Minimum 10 faculty travel grants annually (Year 1: 9)
Conference/Event Details Names of conferences attended, dates, locations, purpose (presentation, workshop participation, networking)	Faculty Reports Travel Logs Travel request forms + Post-travel reports	Ongoing After each event	PI 4.1: Document types and quality of professional development activities
Faculty Research Mini-Grants Awarded Number of research mini-grants awarded starting Year 3, with project titles and principal investigators	Program Logs Grant office mini-grant database	Annual Starting Year 3 (2025-2026)	PI 4.2: Minimum 5 mini-grants annually beginning Year 3
Mini-Grant Research Outcomes Publications, presentations, or other scholarly products resulting from mini-grant funded research	Faculty Reports Required final reports from grant recipients	Annual End of grant period	PI 4.2: Assess productivity and impact of research support
Faculty Publications Number and types of publications (journal articles, book chapters, books) by STEM faculty annually	Faculty Reports Institutional Data Faculty annual reports + Institutional Research	Annual End of academic year	PI 4.3: Minimum 50 publications annually (Year 1: 191 – far exceeded)
Publication Citations Full citations for faculty publications including journal names, titles, authors, DOIs	Faculty CVs Institutional Data Faculty CV repository + Annual activity reports	Annual Faculty reporting period	PI 4.3: Document scholarly contributions and quality
Conference Presentations Number and titles of conference presentations, posters, and invited talks by faculty	Faculty Reports Faculty annual activity reports	Annual End of academic year	PI 4.3: Measure dissemination of research beyond publications
Faculty-Student Research Collaborations Number of faculty mentoring students in research, with co-authored publications or presentations	Faculty Reports Research Logs Research symposium records + Faculty reports	Annual End of academic year	PI 4.3: Connect faculty research to student learning outcomes
External Grant Applications & Awards Number of external grant proposals submitted and awarded by STEM faculty (with funding amounts)	Institutional Data Office of Sponsored Programs	Annual Fiscal year end	PI 4.2 & 4.3: Measure broader research competitiveness and capacity building

Why This Data Matters

Objective 4 data demonstrates investment in faculty capacity, which directly benefits students through improved instruction and research mentorship. Travel grant data shows professional development reach. Publication metrics (191 in Year 1 vs. 50 target) indicate exceptionally strong scholarly productivity. Mini-grants (starting Year 3) will seed research projects that enhance FSU’s research profile. Faculty-student collaborations link research support to student outcomes. This data validates that supporting faculty excellence creates a ripple effect throughout the program.

Objective 5: Develop and Revise STEM Curricula

Track course development, instructional tool acquisition, and curriculum quality improvements

Data Point & Description	Data Source	Collection Frequency	Performance Indicator Link
New Courses Developed List of new STEM courses created with course numbers, titles, credit hours, and program alignment	Institutional Data Program Reports Academic Affairs / Curriculum committees	Semester When approved/launched	PI 5.1: Develop minimum 10 new courses by Year 6 (Year 1: Planning begun for Fintech, Instructional Tech, Data Science)
Course Development Timeline Development stages for each course: planning, syllabus creation, approval, first offering	Program Logs Status Reports Curriculum development team documentation	Ongoing Continuous tracking	PI 5.1: Monitor progress toward 10-course target
Evidence of Course Development Steps Documentation of course development process: needs assessment, learning outcomes, curriculum proposals, syllabi drafts, materials developed, pilot feedback	Development Docs Program Logs Course development files + Academic Affairs records	Per Course Throughout development cycle	PI 5.1: Document quality and rigor of course development process
Courses Revised List of existing STEM courses updated with revision details (content updates, technology integration, alignment improvements)	Institutional Data Faculty Reports Curriculum committees / Faculty documentation	Semester When revisions implemented	PI 5.2: Revise minimum 8 courses by Year 6 (Year 1: Identification phase)
Course Revision Rationale Documentation of why courses were revised and what improvements were made (industry alignment, pedagogy, technology)	Faculty Reports Course revision proposals and reports	As Completed Per revision project	PI 5.2: Demonstrate quality and relevance of revisions
Teacher Licensure Tools Acquired List of instructional tools purchased (SchoolSims, Pearson products, etc.) with implementation status	Purchase Logs Implementation Reports Procurement records + COE usage reports	Ongoing As acquired	PI 5.3: Acquire licensure tools throughout grant period (Year 1: SchoolSims acquired)
Licensure Tool Usage Data Number of students using tools, frequency of use, integration into courses	Faculty Reports Usage Logs Platform usage analytics + Faculty reports	Semester End of Fall and Spring	PI 5.3: Validate tools enhance teacher preparation
Software & Data Licenses Acquired List of software licenses, data licenses, and equipment purchased (Bloomberg, VMock, SAP, SAS, IBM, nursing simulation, etc.)	Purchase Logs IT Services + Grant office procurement tracking	Ongoing As acquired	PI 5.4: Acquire licenses/equipment throughout grant (Year 1: 4 major acquisitions – exceeded target)
Software/License Integration Courses using each software/license, number of students with access, certifications earned	Institutional Data Usage Reports LMS data + Software usage analytics + Certification records	Semester End of each term	PI 5.4: Document student engagement with professional tools and certification achievement
Student Satisfaction with Curriculum Survey responses on course quality, relevance, technology integration, and preparation for careers	Course Evaluations Program Surveys Course evaluation system + Program-specific surveys	Semester End of each course	PI 5.1 & 5.2: Assess student perception of curriculum quality and relevance
Industry Certification Achievement Number and percentage of students earning industry certifications (SAP, SAS, IBM AI, etc.)	Institutional Data External Data Program records + Certification vendor data	Semester After certification exams	PI 5.4: Validate curriculum prepares students for professional credentials

Why This Data Matters

Objective 5 data demonstrates continuous curriculum improvement and industry alignment. New course development (target: 10) and revisions (target: 8) show responsiveness to emerging fields and pedagogical best practices. Documentation of course development steps provides evidence of rigorous planning, learning outcomes design, and quality assurance processes. Teacher licensure tools (SchoolSims acquired Year 1) enhance preparation quality. Software/license acquisition (4 in Year 1, exceeding targets) provides students with industry-standard tools. Certification achievement validates curriculum rigor and career readiness. This data proves the program stays current with field demands and employer needs.

Objective 6: Provide Comprehensive Student Support

Monitor graduation outcomes, employment success, and professional development engagement

Data Point & Description	Data Source	Collection Frequency	Performance Indicator Link
Annual Graduation Rate Number and percentage of students in targeted STEM disciplines who graduate within expected timeframes	Institutional Data Registrar / Institutional Research	Annual End of academic year	PI 6.1: Track graduation rates with Year 1 baseline (65 graduates) Assess annually beginning Year 2
Graduation Rate by Program Breakdown of graduation rates by specific degree programs (MAT, MEd, MBA concentrations, MSN, MS)	Institutional Data Institutional Research cohort analysis	Annual End of academic year	PI 6.1: Identify program-specific trends and areas needing support
Student Persistence & Retention Fall-to-fall retention rates and semester-to-semester persistence for STEM graduate students	Institutional Data Registrar / Institutional Research	Semester Each Fall and Spring	PI 6.1: Early indicators of graduation success; identify at-risk students
Employment Outcomes Number and percentage of graduates employed in their field of study within 1 year of graduation	Graduate Survey External Data Graduate completer surveys + LinkedIn + employer reports	Annual 6-12 months post-graduation, starting Year 3	PI 6.2: Monitor employment outcomes Tracking begins Year 3 (2025-2026)
Employment Details Job titles, employers, salary ranges, geographic locations, career advancement for graduates	Graduate Survey Detailed graduate outcome surveys	Annual 6-12 months post-grad, starting Year 3	PI 6.2: Validate career relevance and salary outcomes (MBA BIDA avg: $75K)
Graduate Further Education Number of graduates pursuing doctoral degrees or additional certifications	Graduate Survey Graduate follow-up surveys	Annual Starting Year 3	PI 6.2: Additional measure of program success and career trajectory
Scholar Professional Development Participation Number and percentage of HBCU STEM Scholars attending conferences, workshops, seminars, or other PD activities	Attendance Logs Event Reports Event registration systems + Sign-in sheets + Travel records	Ongoing After each event	PI 6.3: ≥80% scholars participate in PD annually (Year 1: 100%)
Professional Development Event Details List of PD opportunities offered: Student Research Symposium, Excellence in Teaching Conference, BIDA/HINF Symposium, external conferences	Program Logs Event Reports Event planning documents + Post-event summaries	Ongoing Per event	PI 6.3: Document range and quality of PD opportunities provided
Academic Support Service Utilization Usage of tutoring, advising, mentoring, writing support, and other academic services by STEM students	Service Logs Institutional Data Academic support center records + Advising logs	Semester End of Fall and Spring	PI 6.1: Assess whether support services reach students and contribute to retention
Non-Academic Support Service Utilization Usage of mental health services, financial counseling, career services, and wellness programs	Institutional Data Student Affairs offices (aggregated, de-identified data)	Semester End of each term	PI 6.1: Holistic support contributes to student success and wellbeing
Student Satisfaction & Program Feedback Survey responses on overall program satisfaction, support services quality, academic experience, and recommendations	Student Surveys Exit Surveys Mid-program and exit surveys + Focus groups	Semester & Annual Mid-program + At graduation	PI 6.1 & 6.3: Continuous improvement feedback on all support dimensions
Scholarship Impact Assessment Student feedback on role of financial support in enrollment decision, persistence, and degree completion	Scholar Surveys HBCU Scholar-specific surveys + Exit interviews	Annual Mid-year and exit	PI 6.1: Document impact of financial assistance on access and completion (Phase I: 67% cited scholarship as primary enrollment reason)

Why This Data Matters

Objective 6 data demonstrates the holistic student support approach. Graduation rates (baseline: 65) show program effectiveness in helping students complete degrees. Employment tracking (beginning Year 3) validates career readiness and demonstrates return on investment for students and the institution. Professional development participation (100% in Year 1, target 80%) shows comprehensive development beyond academics. Academic and non-academic support utilization data guide resource allocation. Student feedback enables continuous improvement. This comprehensive data set proves the program creates an environment where students thrive academically, professionally, and personally.

Data Collection Timeline by Grant Year

Years 1-2 (2023-2025)

Establish all baseline data
Begin tracking enrollment, scholarships, and participation
Track facility upgrades and program launches
Monitor faculty publications and travel grants
Collect course satisfaction data
Document professional development participation

Years 3-4 (2025-2027)

Begin employment outcome tracking
Start faculty research mini-grant data collection
Track COE STEM Center construction completion
Monitor MS in Data Science proposal approval
Assess new concentration enrollment trends
Evaluate mid-grant progress on all objectives

Years 5-6 (2027-2029)

Complete longitudinal graduation trend analysis
Finalize employment outcome assessments
Document program sustainability planning
Conduct comprehensive program evaluation
Prepare final impact reports
Assess institutionalization of initiatives

Implementation Best Practices

Data Quality: Establish standardized data collection protocols with clear definitions for all metrics to ensure consistency across years
Privacy Compliance: All data collection complies with FERPA regulations; student-level data is aggregated and de-identified for reporting
Regular Reporting: Quarterly progress reports to grant leadership; annual performance reports to Department of Education
Data Management: Maintain centralized database with access controls; assign clear responsibilities for each data collection point
Survey Response Rates: Target 80%+ response rates through multiple contact attempts, incentives, and convenient survey timing
Continuous Improvement: Use data to make real-time program adjustments, not just for retrospective reporting
External Evaluation: Independent evaluator reviews data collection methods and findings annually
Stakeholder Engagement: Share data summaries with faculty, students, and partners to demonstrate progress and gather feedback
Technology Integration: Use automated data collection where possible (LMS analytics, registration systems) to reduce manual burden
Documentation: Maintain audit trail for all data sources, collection methods, and analysis procedures