My experience as a teacher ranges from mentorship toward individual students to classroom lectureship. I have provided descriptions of what the courses covered. Some of positions may not be immediately recognizable as traditional teaching or assistantship courses but still were still essentially lectureships or mentorships. I have provided descriptions of all of my positions for visitors looking for evidence of teaching experience.
Faculty and Student Feedback and Evaluations
| Position | Place | Date | Links |
|---|---|---|---|
| Mentorship Beyond the Classroom | Lab, office hours, etc. | Fall 2017 - Spring 2023 | Student Feedback |
| Quantitative Reasoning Fellowship | CUNY School of Labor and Urban Studies Learning Hub | Fall 2020-Spring 2023 | Student Feedback 2022-2023 Supervisor Evaluation 2021-2022 Supervisor Evaluation 2020-2021 Supervisor Evaluation |
| Center for Academic and Scientific Excellence | CUNY Brooklyn College | Summer 2021 & 20221 | Student Feedback Student Evaluations |
| Graduate Course: Statistical Programming with R | CUNY Brooklyn College | Spring 2020 | Student Evaluations |
| Statistical Methods in Psychological Research (Lab) | CUNY Brooklyn College | Fall 2018-Summer 20192 | Spring 2019 Student Evaluations Spring 2019 Faculty Evaluation Fall 2018 Student Evaluations Fall 2018 Faculty Evaluation |
Description of Teaching
Mentorship Beyond the Classroom
My role: provide mentorship outside of the classroom including training and supervising research assistants, conducting weekly or bi-weekly journal clubs with research assistants, meeting with students during office hours or after class, advising students in one-on-one meetings, or responding to emails seeking advice. About 50% of my student mentorship has been advising students on applying to graduate school (four so far have been accepted into graduate programs in psychology and a related field).
Quantitative Support at the SLU Learning Center
My role: build and sustain an academic support system for students and faculty, specifically with the aim of facilitating quantitative pedagogy through one-on-one student support, the design and administration of quantitative support workshops, and in-class visitations and tutorials.
Since Summer 2020, I have been carrying out an appointment as a Quantitative Reasoning Fellow at the CUNY School of Labor and Urban Studies Learning Center.
In terms of what I actually do, this position is a lot like supervising a research methods or senior project course, only less grading! Most of the degrees at SLU require a senior capstone project, and about 50-60% of the visits are with respect the student’s capstone project. During our sessions, we work together to devise concrete strategies for acquiring data, analyzing it, and reporting it. Students usually consult with me several times throughout the semester across various stages of their projects.
Since my first year to date, I have provided over 100 hours of one-on-one consultation. The projects students come to me span a surprisingly wide range of disciplines and methods, ranging from economics to sociology to psychology to public health. I’ve consulted on quantitative analyses of economic and other public data; survey and prevalence data; and on analysis of qualitative data (e.g., case studies, thematic content analysis).
I have also developed and administered several workshops designed to guide students on different aspects of the empirical research process. So far, I have designed and conducted four 75-minute workshops designed to help students refine and concretize different stages of the research process: refining research questions into testable quantitative hypotheses, operationalizing variables and ensuring sound measurement and assessment; constructing a data analysis plan, and analyzing data with jamovi using a codebook. At the end of each workshop, students received fillable PDF documents that can be used on their own projects. These guides are designed to systematize those “soft”-skill aspects of the research process that are often mysterious and intractable for new researchers. They are meant to be self-contained versions of my workshops and are works in progress. They are slightly geared toward the content area of labor and urban studies, but anyone who may find them useful can access them here:
Brooklyn College Center for Achievement in Science Education (CASE)
My role: lead 45 hours of instruction in statistical programming in R, which provides core knowledge of the R programming language, practice in the a tidy approach to data manipulation, and application of the statistical knowledge that students gained through other aspects of the workshop within R.
In early Spring of 2021, I reached out to the leadership at the Brooklyn College Center for Achievement in Science Education (CASE) to ask how I might contribute to Brooklyn College’s diversity initiatives. Later in the year, I was invited to join as a workshop leader for the SURGE program, an NIH-funded academic support program designed to equip Brooklyn College freshman from underrepresented backgrounds with the kinds of scientific skills that make for strong PhD candidates.
The general idea is that if you want to diversify the PhD pool, you have to catch students early, give them a sense of belongingness in the scientific community, spur their curiosity toward the science, and most importantly provide the opportunity to experience life as a scientist. Students should get these kinds of exposure early enough to either a) run for the hills before they spend more time than they need to on something they don’t like, or b) get started on the preliminaries of pursuing graduate school early to maximize preparedness. The idea is to diversify through recruitment and to provide access to important skills that more advantaged students tend to already have easier access to (on average).
While there is a lot that a student can doon their own to prepare themselves - indeed, I have compiled quite a large list myself - the fact of the matter is that many students simply do not have equal opportunity. This program addresses that.
As a guest speaker in the workshop once put it:
Coding is freedom, independence, and power.
Lecturer On Record, Graduate Course, Special Topics in Experimental Psychology (Statistical Coding with R)
My role: instruct students in the Master’s in Experimental Psychology program at Brooklyn College in the use of R, covering base R to project management using RStudio projects, tidyverse principles including dplyr and ggplot2, using R Markdown to render attractive summary reports, and communicating statistical results to both experts and (hypothetical) non-expert stakeholders in an accessible and responsible way.
I was invited to teach this course literally one week before the first class after the sudden departure of the previous instructor. I created the coursework and in-class group projects on the fly.
Lab Instructor, Statistical Methods in Psychological Research, Brooklyn College
Fall 2018, Spring 2019, Summer 2019
My role: instruct two statistics lab sections per semester. Topics covered included introductions to descriptive statistics, probability theory, prediction, ordinary least squares, between- and within-subject ANOVA models, statistical inference, consumption of statistical information, plotting, using Excel and SPSS statistical software, and principles of plotting.