INFO 6750: Causal Inference and Design of Experiments (Fall 2018)

This PhD-level research methods course provides a hands-on introduction to topics in causal inference, design of experiments, and open science. Topics covered: potential outcomes framework for causal inference, sampling distributions and randomization inference, types of random assignment, uses of covariate measures in inference, statistical power, approaches to non-compliance, quasi-experimental designs, replication, replicability, and open science practices. Short lectures, seminar discussion, and hands-on practice are interwoven on a weekly basis to develop applied analytic skills. The course is targeted at quantitative social scientists who strive to draw causal inferences in controlled lab or field settings and uncontrolled real-world settings. This course should be taken after completing an introductory course in statistics and an introductory quantitative research methods course. Ideally, students taking this course are already conducting (quasi-) experimental research and are looking for ways to improve the precision and power of their inferences.

INFO 5200: Introduction to Learning Analytics (Spring 2019)

How is technology transforming the ways we learn and teach? How can all of the interaction and performance data that are generated from online courses, learning management systems, and student discussion forums be used effectively? This introductory course on Learning Analytics provides a survey of learning science theories (active learning, modalities, Bloom’s taxonomy, metacognition, self-regulated learning) and educational data science methods (predictive modeling, classification, regression, natural language processing, causal inference). Students will collect and analyze their own learning trace data as part of the course. Learning outcomes: Students will learn to articulate key ideas in the learning sciences; articulate the potential benefits and dangers of learning analytics for students, teachers, and institutions; choose and apply appropriate methods for analyzing different kinds of educational data and be able to articulate why; and interpret the results of basic learning analytics.