About

Peter Garik is an Affiliate Faculty (Clinical Associate Professor – SED/Curriculum & Teaching) at Boston University. His research primarily focuses on improving science instruction through visualization of abstract scientific models using computer tools. He has contributed to curriculum projects that integrate computer modeling into the classroom, participated in in-service teacher training projects, and co-directed the development of a science museum exhibit incorporating hands-on experiments and multimedia displays. Currently, his research includes methods for teaching quantum concepts in general chemistry using computer simulations, a study of expert versus novice understanding of quantum concepts, and research on engineering instruction. He works closely with faculty members from CAS and SED, as well as those affiliated with the Science and Mathematics Education Center. Dr. Garik holds a Ph.D. in Theoretical Physics from Cornell University, an M.Sc. in Physics from Cornell University, and a B.Sc. in Physics and Mathematics from SUNY at Stony Brook.

Research topics

• Mathematics education
• Computer Science
• Political Science
• Psychology
• Mathematics
• Pedagogy
• Astronomy
• Engineering
• Physics
• Quantum mechanics
• Engineering ethics
• Statistics

Selected publications

• Why Teachers (Do Not) Integrate History of Science into Physics Courses

  Science & Education · 2024-10-21

  articleSenior author
  PublisherDOI

• Context Matters: Influence of Undergraduates’ Approaches, Experiences, and Expectations on the LA Model in Large Enrollment Science Courses

  Educational Innovations and Emerging Technologies · 2022-01-01

  articleOpen access

  The Learning Assistant (LA) model was designed to facilitate the innovative use of active learning in large lecture courses by training and integrating undergraduate near-peer instructors called Learning Assistants (LAs). We analyze the LA model in the context of the course learning activities, their influence on student outcomes, and students’ expectations for the course. We used results from a large-scale survey to model expectations, course activities, and outputs in the form of course satisfaction and final grades, and drew from interviews, classroom observations, and student focus groups to make sense of the model results. Quantitative results revealed a small positive effect of LAs on final grade when only LAs were input into the statistical model. However, when the influence of students’ perceptions of their professor, their teaching fellow (TF), and their own study habits were included, the effect of LAs was mitigated. Qualitative results showed that while undergraduates reported that they felt more comfortable asking LAs questions about course material, many still focused on grades over conceptual understanding. LAs championed small group work more than their teaching partners, but the effectiveness of the LAs to encourage group work was linked to the TF’s approach to teaching. Results suggest that the expectations of the students, teaching staff, and the course activities and assessments can impact the effectiveness of the LA model.

  PublisherDOI

• Integrating History of Science in In‑service Physics Teacher Education: Impact on Teachers’ Practice

  Science & Education · 2021 · 10 citations

  Senior authorCorresponding
  • Mathematics education
  • Physics
  • Psychology
  PublisherDOI

• GLOBE Mission Earth: Student Projects Activating Research and Knowledge

  AGU Fall Meeting Abstracts · 2020-12-01

  article
  Publisher

• Supporting Map Literacy for Grades K-12 in a High Need School District

  AGU Fall Meeting Abstracts · 2020-12-11

  articleSenior author
  Publisher

• Engaging undergraduate students in project-based science through citizen science: The GLOBE Program

  AGU Fall Meeting Abstracts · 2020

  • Political Science
  • Mathematics education
  • Engineering ethics
  Publisher

• Student Understanding In Signals And Systems: The Role Of Interval Matching In Student Reasoning

  2020 · 1 citations

  Senior authorCorresponding
  • Computer Science
  • Computer Science
  • Mathematics education

  This study was designed to investigate student understanding in signals and systems, particularly the study of continuous-time linear, time-invariant systems. In this paper, we report on a principal finding of this investigation, namely, the importance of the interval matching reasoning resource in accounting for the faulty reasonings that students invoke in reasoning about central topics in signals and systems. The qualitative method of clinical interviewing was employed for probing into student understanding. Fifty-one undergraduate students majoring in aerospace engineering at the Massachusetts Institute of Technology volunteered to participate in this study. Data was analyzed with the aim of identifying the faulty reasonings that participants invoked in their response to different signals and systems problems, and the cognitive structures of reasoning resources that describe and explain the origin of these faulty reasonings. Results indicate that there is a consistency across student faulty reasonings related to three different signals and systems topics -superposition, convolution, and the Laplace transform. This consistency is ascribed to the systematicity in student invocation of the reasoning resource of the interval matching readout strategy.

  PublisherOA PDFDOI

• Using GLOBE Hydrology Protocols for Culturally Responsive Teaching and Socioscientific Issues in a High Need Urban District

  AGU Fall Meeting Abstracts · 2019-12-01

  article
  Publisher

• Building Bundles of GLOBE Activities: Facilitating Urban Teachers Implementation of Project Based Learning

  AGU Fall Meeting Abstracts · 2018-12-01

  article
  Publisher

• GLOBE Mission EARTH: Fusing GLOBE with NASA Assets to Build Systemic Innovation in STEM Education

  AGU Fall Meeting Abstracts · 2016-12-01

  article
  Publisher

Recent grants

• Conference Meeting: How Can the History and Philosophy of Science Contribute to Contemporary Science Teaching?

  NSF · $82k · 2012–2016

Frequent coauthors

• Eshel Ben‐Jacob

  55 shared

• Bradford G. Orr

  University of Michigan–Ann Arbor

  35 shared

• Dale P. Barkey

  University of New Hampshire

  34 shared

• Barbara Stoler Miller

  25 shared

• E. Bochner

  Tel Aviv University

  9 shared

• R. Zamir

  Tel Aviv University

  9 shared

• N. Broxholm

  Tel Aviv University

  9 shared

• Joel Hetrick

  9 shared