Students at Cornell who are interested in learning about research, theories, and methods involved in the work done at the Future of Learning Lab should consider taking the following courses.

INFO 4100/5101: Learning Analytics
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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.

INFO/COMM 4800/5800: Behavioral Science Interventions

“There is nothing as practical as a good theory,” Kurt Lewin wrote. Today behavioral scientists build on social scientific theories about human behavior to develop new intervention approaches that address major challenges facing our society: poverty, poor health, educational inequalities, and many more. This course is designed as a senior capstone seminar that equips students with the knowledge and skills to analyze social problems, consider the ethical implications of intervention, design and pilot appropriate interventions, implement and test them online, analyze and interpret the results, and present policy-relevant findings. The course combines applied quantitative research methods and applied social behavioral science theories to prepare students for careers in research, data science, consulting, and policy evaluation.

Student reviews: Annie (Sp’20), Tanmay (Sp’20).

INFO 6750: Causal Inference and Design of Experiments

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.