Truth bombs being dropped left and right. — *Raphael. “The school of athens”. Circa 1510. Fresca. Stanze di Raffaello, in the Apostolic Palace in the Vatican.*

My experience as a teacher has been varied, and ranges from mentorship toward individual students to classroom lectureship. I have included here an overview of my experience along with interesting background information on the position, where relevant. You can view my student and faculty-peer evaluations where they available. I am still trying to figure out what exactly what I want this page to be but I hope that you find this information useful and/or interesting!

Note: positions are listed in reverse-chronological order.

Faculty and Student Feedback and Evaluations

Mentorship Beyond the Classroom

Fall 2017 - Spring 2023

Select student feedback from students whom I have mentored outside of the classroom. Original name- and address-redacted emails available upon request. Slightly edited for grammar.
Source	Feedback
Direct Email Feedback	“I had a lot of fun learning these past few weeks.”
Direct Email Feedback	“I hope you are doing well and having an amazing week!!! I just wanted to say thank you sooooo much for everything you have done for me! Especially the letter of recommendation that you wrote for me on short notice! I’m truly grateful and if you ever need anything, please let me know!! As for my future plans, I have decided to attend [PhD program in Clinical Psychology]!”
Direct Email Feedback	“It's been quite a while and I hope that you are doing well...This is [student name] from the SURGE program at Brooklyn College. You wrote me a recommendation letter earlier this year...I must say that you are a great mentor and you've given me excellent advice for approaching recommendations, etc. I really appreciate all that you've done for me and I wish you all the best.”'
Direct Email Feedback	“AHHH JUST GOT MY ACCEPTANCE !!!!! [to a PhD program in developmental psychology] Thank you so much! I truly appreciate all the help and guidance you’ve given me!”
Direct Email Feedback	“Thanks for getting back to me, and thanks for being so generous with your time and knowledge! Your guide to applying to grad schools is impressive. It’s so thorough and comprehensive.”
Direct Email Feedback (From a Faculty Supervisor)	“Matt, this is a wonderful letter [of recommendation]. Much more descriptive and complete than many written by experienced (jaded?) senior faculty...What is terrific about your letter is that you give specific examples of behavior that justify your generalizations about [the student], which is not always something people do because it takes more time to think about these. But your letter is full of specifics which makes it very useful to the people reading it. This shows real consideration of [student]’s positive characteristics, and again, I underscore how much better it is that you illustrated these generalizations with specific examples. Many letters are mostly fluff, full of positive adjectives without much substance. Yours is excellent in that regard. This is your letter (and I see no reason for me to counter-sign it, given what a good job you have done), and feel free to modify any of my edits, but I thank you very for doing this so conscientiously.”

Quantitative Reasoning Fellowship (Fall 2021 - Present)

At the CUNY School for Labor and Urban Studies Learning Center.

Select Student Feedback, Quantitative Reasoning Consultation
Source	Feedback
Direct Email Feedback	“Thank you for your tutoring this semester. It was very extremely helpful. You helped me to understand this new language of statistics better than the book and the class. Honestly, I don’t think I would have gotten this far without your insights and knowledge...Thanks again!!!”
Direct Email Feedback	"Thank you! I appreciate your help. You were very helpful."
Direct Email Feedback	“Just saying thank you very much for your kind help with my Statistics learning. I got a grade A and I am very happy with the result. It was due to your kind help and teaching. I am grateful to you. I was very nervous about it, but [statistics] turned out to be my favorite subject now. I am learning all over again since I want to have a crystal clear understanding of the subject. Once again thank you so much.”
Direct Email Feedback	“I am learning more about Statistics now after I got good marks. It gave me great inspiration and I like the subject very much.”
Direct Email Feedback	“I just wanted to thank you for helping me with my assignments for [my] class. I got a B as my grade for the class and I wouldn't have earned it without your help. I really appreciate it. Have a nice summer. “
Forwarded Email Feedback	"Matthew was great!"
Forwarded Email Feedback	"I was able to attend the workshop on Surveys this past Saturday and it was very informative and helpful. Matthew did an amazing job presenting. Glad I was able to take advantage of this session."
Forwarded Email Feedback	"Matthew was very knowledgeable and helpful. I would recommend him to anyone involved in QR research."
Anonymous Survey Comment	"I want the other students to know that my experience at SLU [Learning] Hub is always exceptional, Matthew is very knowledgeable and we have been working together the entire semester. I have learned a lot from Matthew and I am grateful for Matthew’s dedication."
Anonymous Survey Comment	"The meeting with Matthew was very helpful and informative. He was very knowledgeable regarding the subject matter. He also reinforced techniques that were taught in class."
Anonymous Survey Comment	“This was a very productive session that gave me insight into how to code and analyze Data input into Dedoose and coding it.”
Anonymous Survey Comment	“This [workshop was] an eye-opener and detailed way of knowing the best approach to Quantitative reasoning.”
Anonymous Survey Comment	"[Matthew] is a great resource for students that need help with their class assignments and final project. And also students that need that extra boost.”
Anonymous Survey Comment	“[Matthew’s] as good as advertised.”

Brooklyn College

CASE (Center for Academic and Scientific Excellence; Summer 2021-2022)

Select Student Feedback, CASE
Question	Response
What was your favorite thing about the coding class?	"My favorite thing was that Matthew (our instructer) explained everything well. He always made us practice our coding and he was always patient with our questions."
	"I liked how we learned about each command in-depth rather than just learning to copy and paste the lines of code an exact way, because it gave us a deeper understanding of exactly what we were doing and why."
What is one thing that could be improved about the coding class?	"To be honest, the coding class was great."
Overall, what was your favorite part of the program?	"Coding was my favorite part because it was really engaging."
	"I think my favourite part of the program was the coding class, because I learned a lot about coding and it helped remind me why I found coding enjoyable when I first started."
Other comments about the program	"It was a learning experience and I enjoyed every second."
	"It was very fun and all the instructors and staff were welcoming and accommodating to our needs. Thank you!"
	"I absolutely loved the program. It really gave me a sense of security because I know that I’ll have amazing people helping me throughout my years at Brooklyn College. Thank you!"

Graduate Course in R Statistical Programming (Spring 2020)

Students are numerically coded to protect their confidentiality. There were 7 students in the course; the one student missing from this table later emailed me to ask for mentorship beyond the course.
Student (Coded)	Feedback
1	"Thank you Mathew for being patient and encouraging this semester!"
2	"Also, thanks I definitely learned a lot from this class this semester and definitely will continue working with R!"
3	"I've learned a lot this semester. It was pleasure being in your class. [It] was very [helpful] information, interesting and fun. Thank you very much.
4	"Thank you for a great semester! You are truly informative and very helpful about this stuff and I feel like I actually learned something...I feel proud of myself and I thank you for that!"
5	"I ... want to say thank you so much for all the help this semester. You taught us so much and so well, I really enjoyed your class and your teaching style! I hope you don't mind if I stay in touch with you!"
6	"Thank you again for being a great teacher this semester, I have learned a lot and feel more confident in analyzing data."

Statistical Methods in Psychological Research Lab (Summer 2019)

Brooklyn College does not collect student evaluations or conduct faculty evaluations for Summer courses.

Statistical Methods in Psychological Research Lab (Spring 2019)

Question	Yes	No	Responses
1. Did you receive a written syllabus during the first week of class?	93%	7%	15

Question	Excellent	Good	Fair	Poor	Unacceptable	Not applicable	Responses
2. The instructor's ability to organize ideas and materials for class	31%	27%	23%	15%	4%	0%	26
3. The instructor's ability to stimulate interest in the subject	24%	36%	24%	12%	4%	0%	25
4. The instructor's ability to encourage independent thinking	28%	32%	28%	8%	4%	0%	25
5. The instructor's ability to generate effective class discussion	28%	36%	20%	12%	4%	0%	25
6. The instructor's ability to communicate clearly	28%	32%	24%	8%	8%	0%	25
7. The instructor's openness to students' comments, questions and viewpoints concerning class topics	44%	36%	12%	4%	4%	0%	25
8. The instructor's knowledge of the subject matter of the class	24%	36%	20%	16%	4%	0%	25
9. The instructor's ability to keep to the time and schedule requirements for the class	48%	32%	12%	4%	4%	0%	25
10. The instructor's availability to students outside of class	32%	36%	24%	4%	4%	0%	25
11. The clarity of information provided about the course requirements and assignments	36%	28%	24%	0%	8%	4%	25
12. The promptness with which tests and assignments are graded and returned	20%	36%	20%	20%	4%	0%	25

Question	Too Many	Just Right	Too few	Not Applicable	Responses
13. The number of assignments/projects/creative works in this class	16%	72%	4%	8%	25

Question	Very Challenging	Challenging	Somewhat Challenging	Not Challenging	Not Applicable	Responses
14. How challenging the class assignments/projects/creative works were	16%	44%	28%	4%	8%	25

Question	Very useful	Useful	Somewhat Useful	Not Useful	Not Applicable	Responses
15. The usefulness of assignments/projects/creative works in this class	24%	48%	16%	8%	4%	25

Question	Very Difficult	Difficult	Somewhat Difficult	Not Difficult	Not Applicable	Responses
16. The difficulty of examinations in this class	4%	44%	8%	0%	44%	25

Question	Very Fair	Fair	Somewhat Unfair	Unfair	Not Applicable	Responses
17. The fairness of examinations in this class	24%	28%	8%	0%	40%	25

Question	A Lot	A Fair amount	Some	A Little	Hardly Anything	Not Applicable	Responses
19. How much general knowledge about the subject have you gained?	36%	20%	20%	20%	4%	0%	25
20. How much ability to analyze and solve problems have you gained?	36%	16%	24%	20%	4%	0%	25
21. How much ability to find and use information on your own have you gained?	32%	28%	12%	24%	4%	0%	25
22. How much ability to express your ideas verbally have you gained from this class?	28%	20%	20%	20%	4%	8%	25
23. How much ability to develop and express your ideas through artistic/creative means have you gained from this class?	24%	20%	12%	24%	4%	16%	25

Student Evaluations, Statistics Lab (Spring 2019)

Statistical Methods in Psychological Research Lab (Fall 2018)

Question	Yes	No	Responses
1. Did you receive a written syllabus during the first week of class?	88%	12%	16

Question	Excellent	Good	Fair	Poor	Unacceptable	Not applicable	Responses
2. The instructor's ability to organize ideas and materials for class	30%	35%	26%	9%	0%	0%	23
3. The instructor's ability to stimulate interest in the subject	35%	26%	26%	13%	0%	0%	23
4. The instructor's ability to encourage independent thinking	35%	30%	22%	9%	0%	4%	23
5. The instructor's ability to generate effective class discussion	39%	22%	17%	17%	0%	4%	23
6. The instructor's ability to communicate clearly	43%	30%	22%	4%	0%	0%	23
7. The instructor's openness to students' comments, questions and viewpoints concerning class topics	57%	22%	17%	4%	0%	0%	23
8. The instructor's knowledge of the subject matter of the class	39%	30%	26%	4%	0%	0%	23
9. The instructor's ability to keep to the time and schedule requirements for the class	55%	23%	14%	5%	0%	5%	22
10. The instructor's availability to students outside of class	61%	22%	13%	4%	0%	0%	23
11. The clarity of information provided about the course requirements and assignments	57%	17%	17%	4%	0%	4%	23
12. The promptness with which tests and assignments are graded and returned	57%	17%	17%	4%	0%	4%	23

Question	Too Many	Just Right	Too few	Not Applicable	Responses
13. The number of assignments/projects/creative works in this class	13%	48%	0%	39%	23

Question	Very Challenging	Challenging	Somewhat Challenging	Not Challenging	Not Applicable	Responses
14. How challenging the class assignments/projects/creative works were	13%	30%	17%	0%	39%	23

Question	Very useful	Useful	Somewhat Useful	Not Useful	Not Applicable	Responses
15. The usefulness of assignments/projects/creative works in this class	13%	43%	9%	0%	35%	23

Question	Very Difficult	Difficult	Somewhat Difficult	Not Difficult	Not Applicable	Responses
16. The difficulty of examinations in this class	0%	26%	17%	4%	52%	23

Question	Very Fair	Fair	Somewhat Unfair	Unfair	Not Applicable	Responses
17. The fairness of examinations in this class	0%	35%	13%	0%	52%	23

Question	A Lot	A Fair amount	Some	A Little	Hardly Anything	Not Applicable	Responses
19. How much general knowledge about the subject have you gained?	22%	35%	17%	17%	9%	0%	23
20. How much ability to analyze and solve problems have you gained?	17%	43%	13%	13%	13%	0%	23
21. How much ability to find and use information on your own have you gained?	17%	43%	13%	13%	9%	4%	23
22. How much ability to express your ideas verbally have you gained from this class?	13%	39%	17%	13%	13%	4%	23
23. How much ability to develop and express your ideas through artistic/creative means have you gained from this class?	4%	35%	9%	17%	9%	26%	23

Student Evaluations, Statistics Lab (Fall 2018)

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.