Teaching

Training Courses

• • Analyzing Survey Data with R (Pluralsight)

  • Reshaping Data with R (Pluralsight)

  • Statistical Analysis and Visualizations Using R (U of A Technology Training Centre)

  • Statistical Analysis with Microsoft Excel (U of A Technology Training Centre)

Workshops

• • R: Introduction Continued (U of A 2020) 

  • Conducting Monte Carlo Simulations in R (U of A 2020)

  • Educational Data Mining with R (EPODDER 2019)

  • Exploring, Visualizing, and Modeling Big Data with R (NCME 2019)

  • Evaluating the Quality of Assessment and Survey Items Using Visualizations (CSSE 2019)

  • R You Ready to Learn R? (U of A 2018)

Graduate & Undergraduate Courses

EDPY 507: Measurement Theory I (Course Website)

EDPY 507 is designed for researchers and practitioners who will develop, evaluate, and select measurement instruments in their professional roles. It introduces the measurement concepts and models related to both classical test theory (CTT) and item response theory (IRT). The foundational concepts necessary to understand both theoretical positions will be presented. Then, we will apply these two test theories to solve important contemporary problems in educational and psychological measurement.

EDPY 506: Machine Learning: Theory and Applications

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 programming language for different data sources and visualize, evaluate, and interpret results from data analysis. 

EPDY 607: Measurement Theory II (Course Website)

EDPY 607 is an advanced graduate-level course about educational measurement and psychometrics, focusing on test construction, unidimensional, multidimensional, and explanatory item response modeling, computerized adaptive testing, differential item functioning, automated test assembly, and other relevant topics. The purpose of this course is to present advanced measurement techniques and to demonstrate computer applications for psychometric data analysis using R. 

EDPY 504: Survey Design and Implementation

EDPY 504 is a graduate-level course for students, researchers, and practitioners who intend to use survey methods to collect data. This course aims to

• 1. 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,

  2. develop a practical understanding of the principles of survey data analysis and reporting, and

  3. help students apply basic survey research methods to the design of survey instruments, as well as the collection, analysis, and interpretation of responses.

EDPY 504 involves several hands-on projects that are designed to help students create a complete survey proposal at the end of the course.

EDPY 500: Introduction to Data Analysis in Educational Research

EDPY 500 is a graduate-level introductory course about statistical data analysis techniques in educational research. This course serves as the first step in the graduate‐level statistics sequence in our Faculty—EDPY 505 (Advanced Univariate Statistics in Educational Research) and EDPY 605 (Multivariate Statistical Methods in Educational Research). 

The main purpose of this course is to provide an introduction to descriptive and inferential univariate statistics commonly used in educational and social science research. In EDPY 500, three different aspects of statistical reasoning are emphasized: 

• 1. Computational formulas and assumptions

  2. Computer applications

  3. Appropriate uses of univariate statistics in educational research. 

A thorough understanding of the topics covered in this course will prepare students for more advanced graduate work in educational statistics and ensure that students can conduct their own data analyses.

EDPY 605: Quantitative Methods II

EDPY 605 is a doctoral level statistics course about multivariate statistical procedures. This course aims to help students become familiarized with the logic of multivariate statistical analyses and properly use these statistical techniques in their own research. A laboratory component of the course will help students learn how to use the computer software to conduct multivariate analyses. The topics that this course covers include multiple regression, multivariate analysis of variance (MANOVA), multivariate analysis of covariance (MANCOVA), factorial design for research studies, principal component analysis, and exploratory factor analysis.

EDPY 303: Educational Assessment

EDPY 303 is an undergraduate-level course required for all undergraduate students enrolled in Elementary Education and Secondary Education programs. EDPY 303 aims to introduce students to the complexity of classroom assessment as a means of supporting and measuring student learning. As such, this course is designed to facilitate students' growth as an assessor, evaluator and communicator of student learning.

Upon completion of EDPY 303, students are expected to be able to:

• 1. create high-quality assessments for classroom use

  2. increase student motivation and achievement by providing opportunities for assessment to support student learning;

  3. discuss their approach to a balanced assessment system and defend their choices;

  4. communicate valid and reliable results of a student's demonstration of the learner outcomes stated in the program of studies.