Teaching and Mentoring
My goal in teaching and mentoring is to help students to develop as scientists and giving them the skills they need to continue learning independently.
In the classroom, I emphasize conceptual understanding of course material, using inquiry-based and open-ended activities to allow students to engage with and discover concepts on their own.
My courses also emphasize critical thinking, scientific writing, and analytical skills. I introduce students to primary literature, current research, and relevant real-world events, so they can see how scientists use the concepts they are learning in class.
East Stroudsburg University
BIOL 200 | General Ecology (lab) | Fall 2017, Spring 2018
BIOL 423/523 | Plant Ecology (undergraduate and graduate levels, lecture and lab) | Fall 2017
BIOL 496 | Seminar II | Spring 2018
BIOL 115 | Introductory Biology II (lab) | Spring 2018
BIOL 220 | Field Botany (lecture and lab) | Spring 2018
Stony Brook University
BIO 211 | Statistics and Data Analysis: A Conceptual Approach | Fall 2015
BIO 341 | Plant Diversity (lecture and lab) | Summer 2015
BIO 301/ECO 301/GEO 301 | Sustainability of the Long Island Pine Barrens | Summer 2011
BIO 341 | Plant Diversity (lecture and lab) | Spring 2013
BIO 356/BEE 587 | Applied Ecology and Conservation Biology (lab) | Spring 2010
BIO 204 | Fundamentals of Scientific Inquiry in the Biological Sciences I | Fall 2009
Plant Ecology (ESU)
As a plant ecologist, one of my primary motivations in teaching is to reduce “plant blindness” in my students. I aim to help students become more aware of plants as compelling, dynamic living organisms, rather than just a green backdrop to the rest of our lives.
Plant Ecology places the study of the form and function of plants in the context of their ecological interactions. This course incorporates class discussions, laboratory and field field observations, and original research projects carried out by the class. Students learn plant identification skills and methods of ecological research and vegetation analysis through hands-on practice in the classroom and outdoors.
This course is designed to instill knowledge of the principles of fundamentals of plant ecology and the methods of vegetation analysis.
Statistics and Data Analysis: A Conceptual Approach (Stony Brook)
Statistics and Data Analysis is an introduction to probability and data analysis for biology majors, which emphasizes statistical literacy, experimental design, and critical thinking. Students learn how to approach data analysis through in-class problem solving and rapid assessment (flash surveys) of their understanding of each new topic. Students in this course develop an independent research project over the course of the semester, receiving feedback from me throughout the process. This process includes a research proposal, a preliminary report, data analysis and final report of findings, and presentation of their results.
A conceptually focused introduction to probability and data analysis emphasizing statistical literacy, experimental design, and critical thinking. Topics include probability, t-tests, chi-squared tests, correlation, regression, and ANOVA, as well as special topics of interest to undergraduate biology majors such as case-control studies and meta-analysis. This course includes a one hour weekly recitation in which students do hands-on activities, discuss papers from the primary literature, and gain experience with data analysis.
1. Students will know the relationship between simple statistics and population parameters.
2. Students will understand the true meaning of a confidence interval.
3. Students will know when to summarize data in a table versus a graph.
4. Students will be able to critique statistical graphs for content and clarity of design.
5. Students will know the basic properties of the most commonly used statistical distributions (normal, lognormal, binomial).
6. Students will understand the difference between standard error and standard deviations, and know when to construct error bars using one or the other.
7. Students will understand what is meant by a p-value and how it can be used in hypothesis testing.
8. Students will understand the concepts of statistical significance and statistical power.
9. Students will understand how to interpret polling data, issues of sample size, and margin of error.
10. Students will be able to identify misinterpretations associated with multiple comparisons and know how to modify analyses accordingly.
11. Students will be able to apply a chi-squared test.
12. Students will be able to design and interpret case-control studies.
13. Students will be able to compare two groups using paired and unpaired t-tests.
14. Students will understand the principles behind correlation and linear regression.
15. Students will understand the process of model development and comparison.
16. Students will understand the principles behind ANOVA.
17. Students will understand the principles of and be able to critically evaluate meta-analyses in the biological or medical literature.
18. Students will develop experience posing testable hypotheses, gathering data, and performing appropriate analyses; in other words, students will gain familiarity with the scientific method.
19. Students will show competence in data analysis and statistical literacy.
Bootstrapping activity - through resampling one draw, students estimate the proportion of blue fuzzies in the jar
Concept mapping activity