Recent Perspectives on Mathematical Education

Kara Allum

(Oxford University, United Kingdom)

"Maths is for everyone: why interdisciplinary and DEI focused approaches should be the basis of high school outreach"

Mathematics is one of the most abstract topics we learn at high school and whilst the joy of problem-solving appeals to some of us, questions like “why do I need to know this?” or “how is this useful?” are typically asked by everyone else. Mathematics is inherently interdisciplinary within academia, a fact that is often not communicated within high schools or outreach projects, and leads to misinformation around the idea of what a mathematician is and where their work applies. This disconnect continues when we try to ask the question of who can be a mathematician? The primary and high schools we work with and the researchers we send both have big impacts on future engagement, and, when combined with the academic stereotype (and often reality) that every mathematician is a cisgender straight white man, we can disenfranchise a lot of young people before they get to make up their own minds. In this talk, I will describe my experiences working on different types of outreach programs, mathematical or otherwise, and put forward ideas I have learned from these projects that I believe should form the basis of mathematical outreach. I will discuss the power of storytelling, the need to protect curiosity and why we must strive to be more proactive participants within our local communities. Interdisciplinary topics and DEI work are integral factors to outreach and are key to moving the current narrative away from mathematics being exclusive to one where mathematics belongs to everyone.

Kathleen Hoffman

(University of Maryland, Baltimore County, USA)

"Extending Quantitative Reasoning in the Biological Sciences"

About a decade ago, a call to transform the curriculum in the biological sciences along with the change in the MCAT focus from courses to competencies spurred a flurry of activity surrounding interdisciplinary education, particularly quantitative reasoning in the biological sciences. Funded by HHMI, UMBC, in a joint project with three other universities, set out to develop validated competency-based modules to facilitate quantitative reasoning in the first two biology courses. Results showed a modest increase in quantitative competencies, but a discrepancy in achievement gains between direct entry and transfer students. To mediate this effect, UMBC, along with four community college partners, developed a consortium of faculty and administrators to facilitate quantitative module development in four core biology courses and to facilitate large-scale implementation. Funded by an NSF IUSE grant, the consortium will track student achievement with the intention of both mitigating the achievement gap between direct entry and transfer students, as well as understanding the effect of increased exposure to quantitative modules.

Shelby Scott

(University of Tennessee at Knoxville, USA)

"Things I learned as an interdisciplinary graduate student"

Graduate school is a confusing and difficult experience for all students, but there are particular challenges that come from being an interdisciplinary early career researcher. In this talk, I will share some of my struggles faced as a biomathematician/ecologist/statistician/data scientist and give insight to some of the positive and negative feelings many of us seem to have during our time as interdisciplinary graduate students. The goal is to open up an honest conversation about the difficulties of wearing multiple hats as an academic and to de-stigmatize some of these experiences.

Glenn Ledder

(University of Nebraska, Lincoln, USA)

"A Teaching Module for Mathematical Epidemiology Using Matlab or R"

With the enormous impact COVID-19 is having on our students’ lives, there is no better subject to motivate mathematics students than mathematical epidemiology. Add to that the significant amount of misinformation promulgated on the internet and by political actors, and it is clear we have a moral duty, as well as a mathematical one, to teach this subject. The main pedagogical problem we face is that the standard teaching materials on mathematical epidemiology focus on the theory of simple endemic disease models. This is fine as far as it goes, but the crisis we face concerns a complicated disease on an epidemic time scale. Instead of equilibrium analysis of the simplest models, we need a focus on modeling, simulation, and experiment on more realistic models as well as the simplest ones. In particular, we need to address questions about how public health measures impact the progress of an epidemic and “call bullshit” on false or misleading public statements. Our pedagogical challenges are to create materials that allow a student with minimal programming experience to set up virtual experiments in a program-based model implementation and to create meaningful modeling questions that use those experiments. To that end, I have created educational modules for the SIR, SEIR, and SEAIHRD (COVID-19) models, each of which is centered on a suite of programs that encode the model and are carefully designed to have a minimal “model-user interface”, so that students with the barest minimum of programming experience can modify the programs to address specific questions. This presentation will focus on the Matlab version of the SEIR and COVID-19 modules. I will show the programs and how easy they are for novices to use, and I will highlight a few of the experiments and accompanying questions.