Teaching
As a lead instructor and teaching fellow at Harvard, I have led and supported courses on introductory statistics, applied quantitative methods, the use of data and evidence in education, and teacher workforce management. I’ve taught both master’s and doctoral students, virtually and in person. I enjoy learning with and from students, and am proud to have overwhelmingly positive student evaluations.
My full list of teaching experience is on my CV. Below, I've listed my most recent positions and student evaluations.
HGSE EVI101: Evidence
Lead Instructor, August 2022 and 2023
This is a required course for all incoming master's students at HGSE. I co-teach this course with Joseph McIntyre.
Course description: The dilemmas we face as education professionals seeking to advance equity and opportunity require us to make sense of, evaluate, and prioritize different kinds of evidence. This course equips students with the foundational skills and knowledge they’ll need to interpret the most common forms of evidence—both qualitative and quantitative—and apply them to their practice. We ground our exploration of these issues in a persistent, pervasive, and provocative challenge: improving equity in literacy outcomes for grade 3 to 5 students in Charlotte-Mecklenburg, North Carolina. We will use evidence to frame the problem of educational inequity, evaluate the quality and relevance of the evidence about possible solutions, and consider what additional evidence we would need to strengthen our conclusions. The course uses an innovative team-based learning pedagogy, including “flipped” lectures, whole class discussion, and small-group activities. By the end of the course, students will be able to weigh the unique affordances of different types of evidence in making decisions about complex educational dilemmas and will acquire a powerful set of tools for analyzing and applying evidence to improve education systems.
HGSE S040: Introductory and Intermediate Statistics for Educational Research
Teaching Fellow for Joseph McIntyre, Fall 2020, 2021, and 2022
My role involved designing and teaching weekly review sections and grading weekly individual assignments and multiple group projects.
Course description: Often when quantitative evidence is being used to answer questions, scholars and decision-makers must either analyze empirical data themselves or evaluate the analyses of others. This course will cover the basic principles of quantitative data analysis... Students will examine real data gathered to address questions in educational, psychological, and social research settings, becoming acquainted with basic descriptive statistics, tabular and graphical methods for displaying data, the notion of statistical inference, and analytic methods for exploring relationships with both categorical and continuous measures. These topics will provide students with a solid foundation for addressing research questions through statistical modeling using simple and multiple linear regression. There will be an emphasis on applying the statistical concepts learned in this course--in particular, how to: (1) select the appropriate statistical techniques; (2) properly execute those techniques; (3) examine the assumptions necessary for the techniques to work appropriately; (4) interpret analytic results; (5) summarize the findings effectively; and (6) produce publication-style visual displays of results. Because quantitative skills are best learned through practice, computer-based statistical analyses will be an integral part of the course. There will be several problem sets involving the core concepts covered in class as well as several take-home assignments and a final project involving data analysis and the interpretation and reporting of research results.
HGSE S057: Making Data Count (formerly: Using Data in Organizations)
Teaching Fellow for Carrie Conaway, Spring 2021 and 2022
My role involved teaching weekly review sections, grading individual assignments and multiple group projects, and general course management and logistics.
Course description: Data can be a compelling inspiration and guide for action in education. But too often the power of data is lost because we ask the wrong questions, share facts instead of stories, ignore or misuse existing research, or work within an organizational culture that doesn’t promote learning. In this course, students will learn how to address all of these challenges. They will learn how to use theories of action to develop strong research questions about diagnosis, implementation, and impact; how to tell a verbal and visual story with data; how to frame numbers to inspire change; and how to strengthen the conditions for learning in organizations. This course complements introductory and advanced statistics courses by building skills in communicating with data and applying it to social challenges. It is appropriate for students who anticipate doing analytical or organizational improvement work in future roles or who will manage or lead such staff.
HGSE S011E: Understanding Today's Educational Testing
Teaching Fellow for Ann Mantil, January 2022
My role involved designing and teaching daily review sections, grading regular individual assignments, and providing support and feedback for a summative final project.
Course description: Achievement testing is a cornerstone of education policy and practice, but it is complex and is routinely misunderstood by educators, policymakers, and the media. How much confidence should we have that high-stakes tests capture student learning? How does pressure to raise test scores affect educational practices and students’ achievement? What can test scores tell us about differences in performance over time and across socioeconomic and racial/ethnic groups? This module is an intensive dive into testing and its role in today’s K-12 classrooms and accountability systems. Through interactive lecturing, small-group discussion, and analyses of test items and score reports, students will learn how to understand test results and use testing appropriately in their later work. While concepts such as reliability, validity, and bias will be introduced, the course is designed for students with little or no prior statistical training and is relevant for prospective teachers, school and district leaders, and policy analysts.
HGSE A035M: The Teacher Workforce: Management and Policy
Teaching Fellow for Eric Taylor, Spring 2021
My role involved grading policy memos, offering regular individual feedback and support to students on their work, and supporting course design and logistics.
Course description: This course is about the challenges of managing human resources, with a focus on teachers and the education sector. The course is motivated by practical questions: whom to hire, fire, or promote, when to provide on-the-job training, how to evaluate job performance, whether to tie pay to performance, how to design jobs, how to structure retirement benefits, and others. We will examine these questions using tools from economics, especially labor and personnel economics. Underlying these questions are several essential economic concepts that this course will introduce: opportunity costs, asymmetric information, decisions under uncertainty, investments in human capital, principal-agent problem, and incentives, among others. To illustrate these questions and concepts we will use examples drawn from recent empirical research on teachers. As those examples will demonstrate, the topics of this course are directly relevant to several current education policy debates that we will discuss in class: for example, tenure rules, accountability and evaluation, pay for performance or credentials.