I believe that a good teacher needs to demonstrate the mastery of three general skills, which are preparing learning-centered syllabus, creating effective learning experience, and assessing student learning progress. It is an iterative to master these skills. The courses I teach at Cornell are listed below. Feel free to email me for a copy of syllabus if interested.
Environment and Development (GDEV 2065), Fall 2023
Course description: This course examines the relationship between development and environmental change across local to global scales. Students explore how economic, social, institutional, and ecological processes shape development trajectories, sustainability challenges, and their distributional consequences. The course emphasizes trade-offs and synergies among development goals and environmental outcomes, using real-world cases to connect theory with practice.
Learning objectives: Students learn to analyze environmental and development challenges from multiple perspectives, identify trade-offs and synergies, evaluate proposed solutions, and apply systems thinking and basic research skills to real-world sustainability problems.
Introduction to Data Science in Global Development with R (GDEV 3295), Fall 2023
Course description: This course introduces data science tools for studying development and environmental change using R. Students work with real-world datasets to learn data cleaning, visualization, statistical analysis, and reproducible research. The course emphasizes using quantitative evidence to investigate substantive questions in global development rather than treating programming as an end in itself.
Learning objectives: Students learn to manage and visualize data in R, apply basic statistical and spatial methods, build reproducible analytical workflows, and critically interpret and communicate quantitative findings.
Introduction to Data Science with R (GDEV 4290/5290), Spring 2023
Course description: This workshop-style course introduces R for quantitative analysis using students’ own datasets. Students learn practical skills in data import and management, visualization, basic statistical analysis, functions, loops, and linear models. The course is hands-on and project-based, emphasizing how to choose appropriate methods, develop efficient workflows, and use R to answer substantive research questions.
Learning objectives: Students learn to manage and manipulate data, compute summary statistics, create effective visualizations, write basic functions and loops, conduct introductory statistical analyses, and apply R to their own research datasets.