Due to high failure rates and student dissatisfaction with university Calculus courses across the United States, wide-ranging efforts are being made to transform such courses to include active learning pedagogies, which have been documented to support student success. Graduate Teaching Assistants (GTAs) and undergraduate Learning Assistants (LAs) (i.e., near- peer tutors who aid instruction of collegiate courses in which they have previously been successful as students) often play key roles in facilitating active learning environments in university STEM courses; however, little is known about their classroom roles and professional development within these environments. This talk explores GTAs’ and LAs’ enactment of and growth within active learning classrooms through three distinct studies.

The first study is motivated by the consistently documented positive content-related outcomes for students in LA-supported classrooms, examining how LAs and students interact around the specific content of implicit differentiation. The second study examines LAs’ pedagogical growth over their first semester as LAs, using an innovative Large Language Model (LLM) assisted analytic approach to uncovering changes in LAs’ perceptions and interpretations of their teaching as they gain experience as LAs. The third study examines how GTAs enact active learning principles within their classroom, focusing on how one GTA’s actions shifted when teaching with LAs of varying experience.

When considered together, these three studies generate a larger picture of life in an active learning Calculus I classroom, depicting how content and communities come together to support student learning in this environment. Motivated by the outcomes of these studies, future pursuit of a large-scale qualitative investigation of active learning Calculus classrooms is suggested.

The first two years of college play a key role in retaining STEM majors. This becomes considerably difficult when students lack the background knowledge needed to begin in Calculus and instead take College Algebra or Precalculus as a first mathematics course - courses associated with high failure rates. Given the poor success rates often attributed to these courses (Wakefield et al., 2018), researchers have been looking for ways in which to improve these courses, such as examining the impact of enhancing study habits and skills and metacognitive knowledge (e.g., Credé & Kuncel, 2008; Donker et al., 2014; Ohtani & Hisasaka, 2018; Schneider & Artelt, 2010). These efforts typically require students to reflect on the mistakes they made on exams. As part of a study on the impact of metacognitive instruction for College Algebra students (Pilgrim et al., 2020a), we found that students often attributed their exam errors to “simple mistakes” (Ryals et al., 2020b, p.494). However, we identified many of these as “not simple”. To understand students’ perceptions of their mistakes within the context of problem-solving, we adapted the Carlson and Bloom (2005) problem-solving framework as an analytical tool. We found that students’ and researchers’ classifications of errors were not aligned across the problem-solving phases. In this talk I will present findings from this work, sharing the adapted problem-solving framework, students’ perceptions of their exam mistakes, and the relationship between students’ categorizations of their errors and the problem-solving phase in which the errors occurred.

In this conversation, I will present the preliminary results of ongoing research on the professional development of beginning mathematics teachers in culturally diverse school contexts in the Chilean region of La Araucania. A narrative analysis of interviews and focus groups with 10 preservice teachers reveals the diverse interpretive frameworks employed to examine the presence of indigenous and immigrant students in mathematics classes. The present study employs the concept of disposition to facilitate an analysis of these narratives, with the objective of elucidating the impact of initial teacher education in shaping dispositions toward ethnic and racial diversity within the context of mathematics education.

Since its earliest formulations in Henry Pollak’s work in the 1970s, modelling in mathematics education has been interpreted in multiple ways that persist to the present. The lack of consensus regarding its definition influences how it is taught in classrooms. In this lecture, we will consider several definitions and tasks that could potentially develop mathematical modelling competences. We will also briefly discuss the types of contexts involved and the role of digital technologies in these processes.

CISTEME is a unique Center focused on enhancing student ability and on empowering STEM faculty members. Mathematics faculty are increasingly interested in helping students see the relevance of mathematics beyond the classroom, yet many face structural, pedagogical, and logistical barriers to engaging in this work. This presentation examines how a STEM center can support mathematics faculty in developing and sustaining community-engaged teaching practices that align with disciplinary goals and course learning outcomes. Through faculty learning communities, and partnerships with local organizations, the STEM center serves as a bridge between mathematics educators, students, and community partners. We share examples of community-engaged projects that connect core mathematical concepts to authentic community questions, while supporting inclusive teaching and meaningful student engagement. The presentation also seeks to learn more about how the Center can more effectively support faculty researchers and instructors, as they create practical strategies and resources that lower barriers to participation. Participants will leave with concrete understanding of CISTEME’s vision, the community based practices that have been effective in increasing students interests in STEM, and a clearer understanding of how STEM centers can support faculty in this work.

This presentation will discuss past research projects aimed at improving problem-solving abilities and metacognition in middle school students. We will discuss findings and ongoing efforts related to metacognitive training utilizing AI agents. 

For several semesters, I've taught a large-lecture probability and statistics course. This course is primarily for engineering students, and has a number of unique challenges including its size and its position as a terminal math course for many majors. It also differs substantially from more procedural calculus courses that students are accustomed to; students face challenges learning and working with the language of statistics while connecting the mathematics to real-world problems. 

In this talk, I will discuss  a system of exam revisions, metacognitive surveys, and connections to the course material (via statistical analyses taught in the class!) that promote study skills and self-analysis of students' habits. I'll also talk about how this Freire-inspired system can materially improve the dialectic of the course, and allow for deeper conversations with students even in the context of a large class.