Courses

EDPY 501 is a graduate-level course concerned with research methodologies available for educational research. The course begins with the preliminary considerations (e.g., selecting research questions and reviewing the literature) that go into selecting a qualitative, quantitative, or mixed methods research design. In the second part, a methodological overview is presented, including methodological approaches, sampling, data collection tools, and a data analysis plan. The course ends with the communication of research (e.g., writing and presenting the findings of a research study and the APA formatting guidelines).

EDPY 504 aims to help students develop an understanding of basic survey research methods, particularly those that apply to educational settings with research applications in education, psychology, and the social sciences. The course is taught from the theoretical basis of Social Exchange Theory and includes the review of state-of-the-art research on survey methods. The course also includes practical applications of survey data analysis using open-source software programs and covers the main principles of reporting survey results in publications and presentations. 

EDPY 507 is designed for graduate students, researchers, and practitioners who will develop, evaluate, and select measurement instruments in their professional roles. The course provides an introduction to the measurement concepts and models related to both classical test theory (CTT) and item response theory (IRT). The foundational concepts necessary to understand both CTT and IRT are be presented. In addition, students get the opportunity to conduct psychometric data analysis based on the CTT and IRT perspectives using a psychometric software program such as R.

EDPY 607 serves as the second step in the graduate–level measurement sequence of EDPY 507 Measurement Theory I and EDPY 607 Measurement Theory II. The main purpose of EDPY 607 is to bring students as close to current practice in educational measurement as possible. EDPY 607 focuses on advanced measurement topics (e.g., explanatory item response modeling, computerized and multistage adaptive testing, and process data in digital assessments) and their applications using the R programming language. 

EDPY 604 is designed to prepare researchers with the unique knowledge and skills to accomplish the requisite integration of qualitative and quantitative perspectives for mixed methods research. This course intends to build mixed methods research-specific competencies by studying core characteristics, mixing purposes, and guiding mixed methods research questions as well as describing mixed methods designs, data collection, analysis, and integration strategies. 

EDPY 615 is designed to introduce students to the complexities inherent to social and program evaluation as a consultative process and examine contemporary practice dilemmas. Learners engage in bridging theory with practice across the five competency practice domains identified by the Canadian Evaluation Society (2018) including reflective practice, technical practice, situational practice, management practice, and interpersonal practice.

EDPY 502 is a data science course that presents students with a variety of educational data mining techniques, covering topics in both supervised and unsupervised machine learning, with an emphasis on conceptual understanding and applications. Although the course does not require any prior programming experience, students will learn how to implement these techniques using the R or Python programming languages.

EDPY 506 is a data science course that focuses on fundamental machine learning (ML) algorithms, ranging from supervised to unsupervised learning ML techniques and their applications in social sciences. The course is both theoretically and practically oriented as it aims to help students build a solid background in ML, learn how to implement different algorithms in the Python or R programming languages for different data sources, and visualize, evaluate, and interpret results from data analysis.

EDPY 505 is an introductory-level statistics course that covers a wide range of univariate statistical techniques used in educational and psychological research. The course primarily focuses on the analysis of data from experiments and surveys using the Analysis of Variance (ANOVA). The goal of EDPY 505 is to help students acquire knowledge of underlying statistical models and skills in applying models to research designs, learn to use computer software to conduct appropriate statistical analyses, interpret results of statistical analyses and report findings. 

EDPY 605 serves as the second step in the graduate–level measurement sequence of EDPY 505 Quantitative Methods I and EDPY 605 Quantitative Methods II. The course aims to help students become familiarized with the logic of multivariate statistical analyses and properly use multivariate statistical techniques in their own research. Some topics included in EDPY 605 are multiple regression, multivariate analysis of variance (MANOVA), multivariate analysis of covariance (MANCOVA), factorial design for research studies, principal component analysis, and exploratory factor analysis.

The capping project is the culminating activity for students in the course-based Master of Education program in Measurement, Evaluation, and Data Science.  The purpose of the course-based program is to provide students with a solid foundation in measurement, evaluation, and data science through extensive coursework.  The program also requires a capping project which is intended to help students integrate the knowledge and skills acquired through their coursework in a final project.  The capping project is not a thesis.  Hence, it does not require that students conduct independent research.  Rather, the capping project is intended to be a flexible exercise where students select a topic of interest and study that topic in-depth using the outcomes from their coursework as the foundation.  

MEDS students take a variety of online and in-person courses. Some courses may include:

• LING 513 - Speech Technology 

  • • Introduction to speech synthesis and speech recognition, with some time allotted to other speech and language technologies. 

• INT D 500 - An Introduction to Community-Based Participatory Research

  • • An introduction to conceptual and methodological foundations of community-based participatory research in the health and social sciences. 

• ECE 625 - Data Analysis and Knowledge

  • • Approaches, techniques and tools for data analysis and knowledge discovery. 

• HECOL 522 - Introduction to Structural Equation Modeling

  • • Provides an introduction to the theory and practice of structural equation modeling with social science data. Practical application in Mplus is emphasized by computing and interpreting statistical models within this framework, including path analysis, confirmatory factor analysis, and structural equation modeling.