Journal of Statistics and Data Science Education: Promoting Innovative and Inclusive Approaches in Statistics and Data Science Education

630 

Topic-Contributed Paper Session 

Juana Sanchez (1), Prince Allotey (3), Anarina Murillo (2), Mine Dogucu (4), Ann Brearley (5), Nathan Quarderer (6), Victoria Woodard (7)

(1) University of California, Los Angeles (Department of Statistics and Data Science), N/A, (2) Brown University, N/A, (3) University of South Carolina, N/A, (4) University of California, Irvine, N/A, (5) University of Minnesota, N/A, (6) University of Colorado, N/A, (7) University of Notre Dame, N/A

Anarina Murillo  
Brown University

Prince Allotey  
University of South Carolina

Juana Sanchez  
University of California, Los Angeles (Department of Statistics and Data Science)

Mine Dogucu  
University of California, Irvine

Ann Brearley  
University of Minnesota

Nathan Quarderer  
University of Colorado

Victoria Woodard  
University of Notre Dame

For statistics, data science and AI to truly benefit society, it is not enough to simply develop more effective technologies and methodologies; we must also ensure that educators are equipped to teach these subjects using innovative teaching strategies and engaging resources that support effective learning and cater to a wide range of learning styles. This is especially important as data science curricula rapidly expand across various disciplines. In this session, statistics and data science educators who published their educational resources and teaching experiences in the Journal of Statistics and Data Science Education (JSDSE), will showcase their effective teaching approaches and resources. The speakers will discuss how they use the foundational principles of statistics education to support the implementation of their accessible teaching innovations in a wide range of courses, including experimental design, Bayesian statistics, biostatistics literacy, and earth and environmental data science. By aligning the educational resources used in their courses with best practices in statistics and data science education, the presentations will show that effective pedagogy extends beyond introductory college statistics courses and statistics departments. In the presentations, the authors will guide us through some of these innovative resources, demonstrating examples of how data literacy competencies can be developed across the statistics curriculum and across many disciplines.

Titles of the presentations, speakers and brief abstract of the presentations.

(1) The Design and Implementation of a Bayesian Data Analysis Lesson for Pre-Service Mathematics and Science Teachers.
Mine Dogucu (speaker), Sibel Kazak and Joshua M. Rosenberg mdogucu@gmail.com
address the challenge of teaching uncertainty in K-12 education. They provide resources aimed at helping pre-collegiate learners understand and think about uncertainty using an accessible yet rigorous Bayesian approach.

(2) Five Hands-on Experiments for a Design of Experiments Course.
Victoria Woodward presents innovative statistical experiments used in a course specifically developed to help students design experiments, implement their designs, collect data, and analyze the results. This active learning approach taught students the challenges of data collection and ways to ensure data cleanliness post-collection.

(3) A Biostatistics Literacy Course: Teaching Medical and Public Health Professionals to Read and Interpret Statistics in the Published Literature.
Ann M. Brearley (speaker), Kollin W. Rott and Laura Le . address the challenge of helping public health graduate students and health sciences professionals develop the skills to read and interpret statistical results in contemporary and methodologically evolving medical and public health literature.

(4) Fostering the Development of Earth Data Science Skills in a Diverse Community of Online Learners: A Case Study of the Earth Data Science Corps.
Nathan Quarderer (speaker), Elsa Star Culler, et al. highlight the issue that training in Earth and Environmental Science skills is not equally accessible, contributing to a lack of diversity in the field. To address this gap, they developed earth and environmental data science training opportunities for faculty and undergraduate students at Minority Institutions. Named Earth Data Science Corps. Projects and culturally relevant learning experiences will be discussed.

Section on Statistics and Data Science Education ¹

Section on Statistics and the Environment ³

Section on Teaching of Statistics in the Health Sciences ²