By Nathan Taback, and Alison L. Gibbs

Journal of Statistics and Data Science Education | 2022 | 31 (2), 134-14

Can a “nudge” toward engaging, fun, and useful material improve student attitudes toward statistics? We report on the results of a randomized study to assess the effect of a “nudge” delivered via a weekly E-mail digest on the attitudes of students enrolled in a large introductory statistics course taught in both flipped and fully online formats. Students were randomized to receive either a personalized weekly E-mail digest with course information and a “nudge” to read and explore interesting applications of statistics relevant to the weekly course material, or a generic course E-mail digest with the same course information, and no “nudge.” Our study found no evidence that “nudging” students to read and explore interesting applications of statistics resulted in better attitudes toward statistics. Supplementary materials for this article are available online.

Read more

Location-Agnostic Professional Experience Course in Actuarial Science

by Vicki Zhang

Expanding Horizons (Society of Actuaries) | 2022 | June 2022

Read more

The Fundamental Role of Computation in Teaching Statistical Theory

by Alison L Gibbs, Alex Stringer

Proceedings of the Satellite conference of the International Association for Statistical Education (IASE) | 2021 | Aug-Sept 2021

Short Summary: What skills, knowledge and habits of mind does a statistician require in order to contribute effectively as an inhabitant of the data science ecosystem? We describe a new course in statistical theory that was developed as part of our consideration of this question. The course is a core requirement in a new curriculum for undergraduate students enrolled in statistics programs of study. Problem solving and critical thinking are developed through both mathematical and computational thinking and all ideas are motivated through questions to be answered from large, open and messy data. Central to the development of the course is the tenet that the use of computation is as fundamental to statistical thinking as the use of mathematics. We describe the course, including its connection to the learning outcomes of our new statistics program of study, and the multiple ways we use and integrate computation

Read more

Training Official Statisticians for Adaptive Statistical Practice

by Sotirios Damouras, Alison Gibbs, Steve MacFeely

Statistical Journal of the IAOS | 2021 | vol. 37, no. 3, pp. 887-898

Short Summary: Statistics is undergoing what feels like an evolutionary jump, i.e., a period of rapid and sweeping developments brought about by dramatic shifts in its environment. The repercussions are felt strongly by official statistics, which operates at the forefront of societal and economic change. In this paper, we look at the implications of the recent developments for the training of official statisticians and highlight key knowledge areas for successfully navigating the emerging landscape. In addition, we employ the concept of adaptive expertise to help us identify three qualities that support the independent and lifelong development of practicing statisticians, and propose five teaching strategies for fostering these qualities in the classroom.

Read more

Computational Skills by Stealth in Introductory Data Science Teaching

by Wesley Burr, Fanny Chevalier, Christopher Collins, Alison L Gibbs, Raymond Ng, and Chris Wild

Teaching Statistics | 2021 (accepted) | DOI: 10.1111/test.12277

Short Summary: In 2010 the Nolan and Temple Lang-proposed “integration of computing concepts into statistics curricula at all levels.” The unprecedented growth in data and emphasis on data science has provided an impetus to finally realizing full implementations of this in new statistics and data science programs and courses. We discuss a proposal for the stealth development of computational skills in students’ exposure to introductory data science through careful, scaffolded exposure to computation and its power. Our intent is to support students, regardless of interest and self-efficacy in coding, in becoming data-driven learners, who are capable of asking complex questions about the world around them, and then answering those questions through the use of data- driven inquiry. Reference is made to the computer science and statistics consensus curriculum frameworks the International Data Science in Schools Project (IDSSP) recently published for secondary school data science or introductory tertiary programs, designed to optimize data- science accessibility.

Read more

The Building Blocks of Statistical Education in the Data Science Ecosystem

by Alison L. Gibbs, and Nathan Taback

Harvard Data Science Review | 2021 (accepted)

Read more