Teaching
I teach graduate courses in educational measurement, psychometrics, quantitative methods, statistics, data management, large-scale surveys, and machine learning. My teaching emphasizes the connection between methodological foundations and real research problems, with students learning both the underlying concepts and how to apply them to authentic data.
My courses combine conceptual understanding, hands-on data analysis, and research applications, and I encourage students to connect methodological training with their own research interests.
Teaching Philosophy
My teaching is grounded in three principles:
Methods as tools for solving research problems
Quantitative methods are most meaningful when students understand how and why they are used. I connect statistical and psychometric concepts to substantive research questions and use real datasets and published research to demonstrate their application.
Learning through practice
My methods courses emphasize hands-on analysis. Students work with data, statistical software, and research problems throughout the course. Whenever possible, I encourage students to connect course assignments with their own research interests and projects.
Connecting teaching and research
My teaching is closely connected to my methodological research. I bring current research, emerging methods, and research software into the classroom, giving students opportunities to work with approaches that extend beyond traditional coursework.
This integration has also led to collaborative research and publications with students. For example, students in my Item Response Theory course participated in a research project that subsequently resulted in a peer-reviewed publication.
Courses
Quantitative Methods
ED RES 565 — Quantitative Research
Research design and quantitative methods for educational research.ED PSYCH 508 — Educational Statistics
Statistical foundations and applications in educational research.ED PSYCH 512 — Data Management and Visualization
Data organization, management, visualization, and reproducible analysis using SPSS and R.ED PSYCH 569 — Multivariate Data Analysis
Multivariate statistical methods for complex research questions.ED PSYCH 576 — Factor Analytic Procedures
Exploratory and confirmatory factor analytic methods.ED PSYCH 581 — Machine Learning Applications in Education
Machine learning methods and applications in educational research using Python and R.
Measurement and Psychometrics
ED PSYCH 511 — Classical and Modern Test Theory
Foundations of classical test theory and modern measurement approaches.ED PSYCH 577 — Item Response Theory
Item response theory models and applications in educational measurement.ED PSYCH 578 — Advanced Item Response Theory
Advanced IRT models and methodological applications.ED PSYCH 579 — Large-Scale Surveys in Education
Sampling, weighting, variance estimation, and analysis of large-scale survey data.
Emerging Methods
My more recent teaching has expanded into machine learning and computational methods, reflecting the growing importance of these approaches in education and the social sciences.
These topics complement my traditional work in psychometrics and quantitative methodology. I emphasize the integration of machine learning with established statistical and measurement approaches, rather than treating computational methods as separate from traditional quantitative methodology.
My current and emerging teaching interests include:
Machine learning and predictive modeling Large-scale assessment and survey data Advanced psychometric methods Computational and reproducible research
Teaching with Real Data
A central feature of my teaching is the use of authentic research data.
Depending on the course, students may work with:
- Large-scale educational assessment data
- Survey and complex-sample data
- Psychometric and assessment data
- Longitudinal data
- Digital assessment and process data
- Data from students’ own research projects
For example, my earlier teaching included the use of OECD Teaching and Learning International Survey data in Educational Statistics to connect statistical methods with authentic educational research questions.
Graduate Mentoring
Teaching extends beyond the classroom through research mentoring and graduate advising.
I work with graduate students on research design, quantitative analysis, methodological development, conference presentations, publications, and research software. My mentoring has resulted in collaborative publications, conference presentations, software development, and student research experiences.
Student accomplishments connected to my research and mentoring include internships, research training, publications, conference presentations, and competitive awards.
Teaching and Research Software
I use research software as an important part of quantitative-methods training. My R packages provide students and researchers with practical tools for psychometric and statistical analysis.
Teaching and Professional Development
I continue to develop my teaching as quantitative methodology evolves. My goal is to provide students with both a strong foundation in established statistical and psychometric methods and exposure to emerging computational approaches that are increasingly important in research.
My teaching therefore sits at the intersection of measurement, quantitative methodology, data science, and machine learning.