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 – Statistical Inference in Education (3 credits) – Foundations of statistical inference with applications to educational research questions.
- CEP 835 – Introduction to AI, Data Science, and Large Scale Educational Data (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 845 – Causal Inference (3 credits) – Experimental and quasi-experimental designs for estimating causal effects in education.
- CEP 846 – Multilevel Analysis (3 credits) – Modeling data with nested structures such as students in classrooms and schools.
- CEP 867 – Ethics of AI in Education (3 credits) – Ethical and regulatory issues including privacy, bias, FERPA, COPPA, and responsible AI practice.
- CEP 848 – Machine Learning Applications in Education (3 credits) – Applied machine learning methods for prediction and classification using educational data.
- CEP 849 – Data Visualization for Educational Data (3 credits) – Communicating findings through dashboards, graphics, and visual storytelling for decisionmakers.
- CEP 850 – Advanced Statistical Models for Education (3 credits) – Advanced modeling techniques that extend prior statistics coursework.
- CEP 898 – Applied Research Project (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.