Courses Taught
- HIED 801: Foundations of Institutional Research. Provides graduate students with a thorough overview of the institutional research profession. Covers various institutional research functions, including an overview of national data sets, planning and budgeting, enrollment management, faculty studies and instructional analysis, accreditation, effectiveness, and student outcomes assessment.
- HIED 830: Designing Institutional Research Studies. Develops and equips graduate students with best practices and necessary quantitative and qualitative research design skills, including sampling and measurement methods, survey research, interviews, focus groups, statistical tools, communicating results to stakeholders, and dashboard development.
- PSYC 2441: Psychological Statistics. Prepares social science majors with skills to analyze data obtained through a variety of study designs and methodologies. The course covers descriptive and inferential statistics used in psychological research and assessment. It includes measurement, characteristics of distributions, measures of central tendency and variability, transformed scores, correlation and regression, probability theory, and hypotheses testing and inference.
- PSYC 2301: General Psychology. Introduces undergraduate students to behavioral and cognitive processes as they affect the individual, both human and animal. The course focuses on biological foundations, learning processes, research methodologies, personality, human development and abnormal and social psychology.
Teaching Statement
My teaching philosophy centers on the belief that education should empower professionals to lead meaningful, evidence-based change in their organizations. Many graduate students arrive with deep experience and a clear sense of purpose. My role is to honor that expertise, challenge them to think critically and ethically, and support their development as scholar-practitioners who can translate inquiry into action.
I have taught in both face-to-face and online environments over the past two decades, most recently serving as an Adjunct Assistant Teaching Professor in Penn State’s College of Education. Through Penn State’s World Campus, I teach HIED 801: Foundations of Institutional Research and HIED 830: Designing Institutional Research Studies—courses aligned with the principles of the Carnegie Project on the Education Doctorate (CPED), which emphasizes the use of inquiry to address problems of practice. I strive to cultivate inclusive, intellectually stimulating online spaces where students feel supported and engaged.
In asynchronous settings, thoughtful design is essential. I structure each course around clear weekly modules, peer-to-peer interaction, applied assignments, and individualized feedback. Real-world data sets, institutional case studies, and discussion prompts help students connect theory to their work contexts. I view technology not merely as a delivery tool, but as a means to foster accessibility, reflection, and collaboration.
My professional background informs my teaching. I have led institutional research and assessment efforts in both K–12 and higher education, including service as Associate Vice Provost for Institutional Research, Planning, and Assessment at Penn State and Director of Assessment for a public school district. These roles deepened my commitment to equity, data literacy, and practitioner-led inquiry and informed the priorities I bring to the classroom.
I guide master’s and doctoral students through research design methods, including quantitative and qualitative approaches, laying a strong foundation for their research. Whether they aspire to teach, lead IR offices, shape policy, or lead student success initiatives, I support their growth as methodologically rigorous, equity-minded, and ethically grounded leaders.
Student feedback consistently reinforces what I value most about teaching: creating a supportive and challenging learning environment. One student noted, “Dr. Vance’s weekly messages were always spot on. Communication was excellent. She is always willing to answer questions, talk through something, or schedule a Zoom. A theme throughout the course was her ability to provide constructive feedback framed in a positive manner. Dr. Vance is superb at this, and I appreciated practicing improvements in this regard.” This kind of feedback affirms my commitment to responsive teaching and growth-oriented dialogue.
Three values guide my teaching: relevance, rigor, and respect. I work diligently to present content that is applicable to students’ careers, uphold high standards for evidence-based reasoning, and foster a sense of belonging in every course. I am continually inspired by the work graduate students do in their communities and am honored to be part of their journey.
My Philosophy on the Use of Generative AI
I want to share how generative AI fits into students’ learning experiences in my courses. AI is transforming how researchers analyze information, communicate findings, and design studies. It’s important that we learn to use these tools thoughtfully, ethically, and skillfully.
Why AI Matters in Institutional Research
IR is all about using evidence to guide decisions that support students, faculty, staff, and communities. In my courses, students build skills across what Patrick Terenzini describes as the three intelligences of effective IR practice:
- Technical/Analytical Intelligence – designing high-quality studies and interpreting data accurately
- Issue Intelligence – understanding the higher ed landscape and asking meaningful questions
- Contextual Intelligence – communicating results in ways that resonate within a particular institutional culture
Generative AI can support each of these but only if used intentionally and critically.
My Philosophy: AI as a Tool and an Assistant, not a Substitute
I encourage students to use AI in my courses, and assignment directions include specific ways to do so. But here’s the core idea:
AI should improve our thinking, not replace it.
My philosophy aligns general AI guidelines seen across institutions, which emphasize:
- protecting personal and institutional data
- keeping the use of AI transparent
- checking AI-generated content for accuracy, bias, and quality
- verifying sources rather than assuming AI output is correct
Using AI doesn’t mean outsourcing learning. Instead, it means using these tools to sharpen our work: to brainstorm, compare approaches, or evaluate our ideas from a different angle.
How Students Can Use (and evaluate) AI
Throughout the semester, students have opportunities to explore both the strengths and the limitations of AI in IR. We might:
- Compare student-generated survey items with AI-generated ones and evaluate the differences
- Use AI to check whether survey questions follow common design principles
- Explore how AI might assist with data storytelling and when it might fall short
- Reflect on how AI could fit into IR workflows at institutions
These activities help us develop the judgment needed in professional practice, where AI tools are increasingly common.
Transparency: AI Use Statements
Any time AI is used for an assignment, students should include a short AI Use Statement. Something as simple as:
“I used ChatGPT to brainstorm initial interview questions. I reviewed, evaluated, and revised all AI-generated content before including it.”
Being transparent builds trust and strengthens credibility as a researcher.
The Bottom Line
Generative AI is a powerful tool, but it doesn’t replace curiosity, critical judgment, or ethical responsibility. In my courses students learn how to use AI wisely, transparently, and effectively, which are skills that will serve us well in institutional research and beyond.
I’m excited to explore this evolving landscape with students. Let’s learn, think, and design with integrity.
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