Undergraduate research experiences (UREs), whether within the context of a mentor-mentee experience or a classroom framework, represent an excellent opportunity to expose students to the independent scholarship model. The high impact of undergraduate research has received recent attention in the context of STEM disciplines. Reflecting a 2017 survey of statistics faculty, this article examines the perceived benefits of UREs, as well as barriers to the incorporation of UREs, specifically within the field of statistics. Viewpoints of students, faculty mentors, and institutions are investigated. Further, the article offers several strategies for leveraging characteristics unique to the field of statistics to overcome barriers and thereby provide greater opportunity for undergraduate statistics students to gain research experience.
One of the first simulation-based introductory statistics curricula to be developed was the NSF-funded Change Agents for Teaching and Learning Statistics curriculum. True to its name, this curriculum is constantly undergoing change. This article describes the story of the curriculum as it has evolved at the University of Minnesota and offers insight into promising new future avenues for the curriculum to continue to affect radical, substantive change in the teaching and learning of statistics. Supplementary materials for this article are available online.
Over the last two decades, statistics educators have made important changes to introductory courses. Current guidelines emphasize developing statistical thinking in students and exposing them to the entire investigative process in the context of interesting research questions and real data. As a result, many concepts (confounding, multivariable models, study design, etc.) previously reserved only for higher-level courses now appear in introductory courses. Despite these changes, causality is rarely discussed in introductory courses, except for warning students “correlation does not imply causation” or covering the special case of randomized controlled experiments. In this article, we argue causal inference concepts align well with statistics education guidelines for introductory courses by developing statistical and multivariable thinking, exposing students to many aspects of the investigative process, and fostering active learning. We discuss how to integrate causal inference concepts into introductory courses using causal diagrams and provide an illustrative example with youth smoking data. Through our website, we also provide a guided student activity and instructor resources. Supplementary materials for this article are available online.
While computing has become an important part of the statistics field, course offerings are still influenced by a legacy of mathematically centric thinking. Due to this legacy, Bayesian ideas are not required for undergraduate degrees and have largely been taught at the graduate level; however, with recent advances in software and emphasis on computational thinking, Bayesian ideas are more accessible. Statistics curricula need to continue to evolve and students at all levels should be taught Bayesian thinking. This article advocates for adding Bayesian ideas for three groups of students: intro-statistics students, undergraduate statistics majors, and graduate student scientists; and furthermore, provides guidance and materials for creating Bayesian-themed courses for these audiences. Supplementary files for this article are available on line.
One of the main goals of statistics is to use data to provide evidence in support of an argument. This article will discuss some popular forms of writing assessments currently in use, to demonstrate the differences between the methods for structuring the students' learning to support their arguments with evidence. We share a model, which was originally created to assess students in introductory statistics and has been adapted for the second course in statistics, which takes a unique approach toward assessing the students' understanding of statistical concepts through writing. In this model, students are expected to answer prompts that required them to (1) take a stance on an argument, (2) defend their position with facts given in the prompt, (3) discern the implications that those facts implied, and (4) give a proper conclusion to their argument. We provide examples of a few of the writing assignment prompts used in the course, their intended assessment purpose, and common answers that students gave to these assignments. Supplementary materials for this article are available online.
