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.
In Fall 2018, I teach Sustainable Food and Farms (SOS 327) and Introduction to Quantitative Research in R (SOS 494/598). Please email me for a copy of syllabus if interested.
Sustainable Food and Farms (SOS 327)
Course description: Food is fundamental for human survival. The ways we organize our culture, our economies, our social interactions and the natural environment to meet our food needs has a profound impact on our ability to sustain ourselves, society, and the planet. In this course, we will take a broad view of food systems and the sustainability of such systems. You will be exposed to theories and examples of agricultural intensification, smallholder farming, and food system sustainability. You will explore the many sustainability challenges associated with food system activities and critically examine the social, ecological, economic and institutional dimensions of these challenges and proposed solutions.
Learning objectives: 1) to identify, discuss, critique, and analyze the different dimensions of a food system in terms of environmental, social, and economic aspects of sustainability; 2) to understand the evolutionary trajectory of food systems and challenges to sustainable growth; 3) to explore and evaluate distinct food system outcomes for society, the environment, and economies from the household to global scale; 4)to develop a personal understanding of the structure of the food system and your own place within a sustainable food system; 5) to learn and practice basic research and system thinking skills; and 6) to practice interpersonal communication skills and team work.
Introduction to Quantitative Research in R (SOS 494/598)
Course description: This course gives students an opportunity to use the public domain and free software R to perform basic quantitative analysis. The R language provides a rich environment for working with data, especially for statistical modeling and graphics. In this course we will cover data import and management, basic functions, plotting tools, loops and functions, basic linear models, and various graphing tools. Students will identify appropriate methods for data manipulation, visualization, and analysis using the R environment. This is a hands-on, project-based course to enable students to develop skills and to solve statistical problems using R.
Learning objectives: 1) to manage datasets and manipulate variables; 2) to apply basic functions for summary statistics and other computation; 3) to use various plotting and graphing tools, as well as more advanced packages, for data visualization and analysis; 4) to develop basic functions and loops to improve computing efficiency; 5) to perform basic statistical analysis; and 6) to visualize data elegantly.