The Current State of Undergraduate Bayesian Education and Recommendations for the Future

TL;DR

This paper analyzes the current state of undergraduate Bayesian education across top research universities and liberal arts colleges, highlighting teaching practices, course content, and offering recommendations for future curriculum development.

Contribution

It provides a comprehensive analysis of existing Bayesian courses at the undergraduate level and offers evidence-based recommendations for expanding Bayesian education.

Findings

Bayesian courses are increasingly offered at undergraduate institutions.

Courses vary widely in content and teaching tools used.

Recommendations for curriculum development are provided.

Abstract

As a result of the increased emphasis on mis- and over-use of $p$-values in scientific research and the rise in popularity of Bayesian statistics, Bayesian education is becoming more important at the undergraduate level. With the advances in computing tools, Bayesian statistics is also becoming more accessible for the undergraduates. This study focuses on analyzing Bayesian courses for the undergraduates. We explored whether an undergraduate Bayesian course is offered in our sample of 152 high-ranking research universities and liberal arts colleges. For each identified Bayesian course, we examined how it fits into the institution's undergraduate curricula, such as majors and prerequisites. Through a series of course syllabi analyses, we explored the topics covered and their popularity in these courses, and the adopted teaching and learning tools, such as software. This paper presents our findings on the current practices of teaching full Bayesian courses at the undergraduate level. Based on our findings, we provide recommendations for programs that may consider offering Bayesian courses to their students.