Being a passionate learner myself, I am always impressed by how knowledge transforms and is multiplied through the exchanges between teachers and students. My overarching goal in teaching is to inspire and motivate; I want my students to absorb the knowledge, internalize and make connections.
When I taught Social Statistics II, I adopt the three strategies: be basic, systematic, and specific.
To help students build a solid understanding of statistical theories, I find it most effective by going back to the basics, back and forth, and in various ways. Good knowledge of social statistics cannot be achieved with either mathematical derivations or working on examples alone； they have to come hand in hand. When learning bivariate regression, students found it difficult to understand how the OLS estimate changes when either the independent variable or dependent variable is rescaled. Therefore, in my lab session, I discussed a series of visual examples based on very simple models and artificial data sets to help them contextualize the concept. After the students built an intuitive impression, I wrote down the equation of the OLS estimator, explained each component of the formula, and showed them how they can actually derive the right answers to various questions from this one basic equation. Students were excited to realize they did not have to memorize any specific examples but only needed to understand the basics well.
I try to help grow a knowledge tree in the students’ minds so that they can better connect and internalize the knowledge. At the beginning of each session, I always motivated the topic by illustrating where the new materials were on the tree, how they were related to what we had covered, and where they might lead us. After students learnt bivariate and multiple regression, I explained that such progression from bivariate to multiple is meaningful as social scientists usually try to identify treatment effects using observational data. I followed up by introducing the goals and general pictures of causal inference. Approaching the end of the semester, students realized that the overview of causal inference was helpful in understanding the concepts of mediators, moderators and confounders. In this way, these materials were no longer isolated points; they became connected systematically, making the students’ learning process more efficient and interesting.
To help students achieve their learning objectives, I adopted objective-based teaching to help them gain hands-on experience. My students had different needs, strengths and challenges in learning social statistics. To support them in ways they need, I conducted frequent surveys and quizzes throughout the semester to learn about their progress. To assist them in their course projects, I conducted consulting sessions to lead them through choosing topics, finding data, and choosing methods to conduct analysis with personalized suggestions. In the end, most of them gained a solid grasp of statistical skills and a good knowledge of how these methods are to be applied in their fields.
At the end of the semester, my students rated my service as 4.7 out of 5.0, among the highest ratings in the department (Yay!). I received reviews such as “Jingying was incredibly responsive and adjusted to meet student’s needs in the lab sessions, as well as responding very quickly to questions outside of the lab”. “Jingying was an effective TA who was genuinely interested in helping students and advocating for them throughout the course of the semester. She was incredibly knowledgeable and went above and beyond for students.”
|Overall performance||TA’s knowledge of the course material||Responsiveness and helpfulness||Fairness|