Abstract Number:
1577
Submission Type:
Topic-Contributed Paper Session
Participants:
Zachary del Rosario (1), Mine Dogucu (3), Kelly McConville (2), Federica Zoe Ricci (3), Zachary del Rosario (1), Samantha Seals (4), Kaitlyn Fitzgerald (5), Patricia Toledo (1)
Institutions:
(1) N/A, N/A, (2) Harvard University, N/A, (3) University of California Irvine, N/A, (4) University of West Florida, N/A, (5) Azusa Pacific University, N/A
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Discussant:
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Speaker(s):
Session Description:
Bayesian inference is a flourishing paradigm in statistics. Dramatic improvements in algorithms and methodology have led to extensive work in Bayesian research. However, Bayesian methods are not broadly used across science, technology, engineering, or mathematics (STEM). To fill this need, the NSF has sponsored a faculty development initiative to promote the teaching of Bayesian methods across all STEM disciplines-the BayesBATS program. This session will give an overview of the BATS project from the project organizers, and report out educational innovations developed by faculty participants in the program.
1 Federica Zoe Ricci: BayesBATS: a program for advancing Bayesian thinking in STEM education
Bayesian statistics has evolved from a small sub-field to a major area of statistics and Bayesian methods are increasingly adopted in both academia and industry. However, the exposure to Bayesian statistics of undergraduate students in many STEM fields is still very limited. In this talk, I give an overview of BayesBATS, an NSF-supported program aimed at training educators across a range of US institutions to enable them to introduce Bayesian methods in their curriculum. I will describe the program's activities and report on the experience of its first cohort.
2 Zach del Rosario: Surprise-they're different! An introductory activity on differences between Frequentist and Bayesian statistics
We present an activity for an introductory statistics course that illuminates the differences between Frequentist and Bayesian approaches. The activity has students analyze a dataset related to social inequality and discover interesting trends-with a surprise at the end. Different student groups will come to different conclusions, which will require them to grapple with the differences between Frequentist and Bayesian approaches. We briefly describe the intervention and report results from a pilot run of the activity.
3 Katie Fitzgerald: Inquiry Based Learning activities for teaching Bayesian statistics
We present a series of Inquiry Based Learning activities for teaching Bayesian statistics to undergraduate learners. We discuss our use of the POGIL (Process Oriented Guided Inquiry Learning) framework to develop and facilitate the activities and report initial feedback from a pilot run of the activities.
4 Samantha Seals: Embracing Bayesian Statistics in the Modern Classroom
A Bayesian statistical framework offers a versatile approach, allowing scientists to update their beliefs as new data emerges. Integrating Bayesian concepts into the STEM curriculum prior to graduate school will equip students with practical tools, applicable in diverse fields, encouraging adaptable statistical reasoning in the generations to come. To encourage science educators to infuse their courses with Bayesian reasoning, we provide access to an Open Educational Resource (OER) website that includes lesson plans, examples from various disciplines, and project-based learning activities.
5 Patricia Toledo: Introducing Frequentist and Bayesian Methods in Parallel in an Undergraduate Economics Statistics Course
This work adapts a traditional economics course to introduce frequentist and Bayesian approaches in parallel. This first course in statistics emphasizes intuition over formulas and proofs. By comparing approaches, students confront common misunderstandings, e.g. with p-values. I will describe lessons learned from a Fall 2023 course pilot.
Sponsors:
International Society for Bayesian Analysis (ISBA) 2
Section on Bayesian Statistical Science 3
Section on Statistics and Data Science Education 1
Theme:
Statistics and Data Science: Informing Policy and Countering Misinformation
Yes
Applied
Yes
Estimated Audience Size
Medium (80-150)
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I understand and have communicated to my proposed speakers that JSM participants must register and pay the appropriate registration fee by June 1, 2024. The registration fee is nonrefundable.
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