Program structure (MA):
- Total credits: 30 graduate credits.
- Delivery: Fully online with a mix of synchronous and asynchronous learning.
Students complete ten 3 credit courses:
- CEP 834 – Inference in Educational Statistics (3 credits) – Foundations of statistical inference with applications to educational research questions.
- CEP 835 – Artificial Intelligence (AI) and Data Science in Education (3 credits) – Survey of AI, data science workflows, and large scale datasets in education.
- CEP 808 – Introduction to Educational Measurement (3 credits) – Principles of assessment, reliability, validity, and score interpretation.
- CEP 819 – Experimental Design and Causal Inference (3 credits) – Experimental and quasi-experimental designs for estimating causal effects in education.
- CEP 821 – Sampling within Educational Contexts: Multilevel Analyses (3 credits) – Modeling data with nested structures such as students in classrooms and schools.
- CEP 823 – AI for Data Collection and Analysis in Education (3 credits) – Applied machine learning methods for prediction and classification using educational data.
- CEP 826 – Linear Statistical Models in Education (3 credits) – Communicating findings through dashboards, graphics, and visual storytelling for decisionmakers.
- CEP 863 – Path Analytic Models in Education (3 credits) – Advanced modeling techniques that extend prior statistics coursework.
- CEP 867 – Ethics of AI in Education (3 credits) – Ethical and regulatory issues including privacy, bias, FERPA, COPPA, and responsible AI practice.
- CEP 898 – Educational Statistics and AI Capstone (3 credits) – Culminating project where students design and carry out an applied study using statistics and AI to address an educational problem.
Students must successfully complete all ten courses listed below to earn the 30 credit MA in Educational Statistics and AI. For the most current official requirements, see the Academic Programs catalog.