Wednesday, Aug 6: 10:30 AM - 12:20 PM
0630
Topic-Contributed Paper Session
Music City Center
Room: CC-101C
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.
Teaching experimental design
Inclusive and innovative teaching approaches
Teaching of Bayesian statistics
Biostatistics literacy
Mathematics and Science Teachers
Earth and environmental data science
Applied
Yes
Main Sponsor
Section on Statistics and Data Science Education
Co Sponsors
Section on Statistics and the Environment
Section on Teaching of Statistics in the Health Sciences
Presentations
We present a unique and innovative course, Biostatistical Literacy, developed at the University of Minnesota. The course is aimed at public health graduate students and health sciences professionals. Its goal is to develop students' ability to read and interpret statistical results in the medical and public health literature. The content spans the typical first-semester introductory material, including data summaries, hypothesis tests and interval estimation, and simple linear regression, as well as material typically presented in a second introductory course, including multiple linear regression, logistic regression, and time-to-event methods. The focus is on when to use a method and how to interpret the results; no statistical software computing is taught. A flipped classroom approach is used, where students are first exposed to the material outside of class, and class time is devoted to actively exploring and applying the concepts in greater depth. The course structure, the class activities, and feedback from students will be shared. Supplementary materials for this article are available online.
Keywords
Active Learning
Flipped Classroom
Introductory Biostatistics
Statistical Literacy
In many collegiate level statistics courses, the focus of the learning outcomes is often on the analysis of data after it has been collected. Students are provided with clean data sets from previous studies to practice statistical analysis, but receive little to no application as to the amount of time and effort that goes in to collecting good data. To account for these deficits at the author's institution, a design of experiments course was created that provided students with a more hands-on learning experience to the statistical process, especially as pertains to data collection. This paper focuses on five of the experiments that students designed and implemented during the course, and some suggestions to instructors that may wish to use these experiments in their own courses.
Keywords
Matched Pairs
Completely Randomized
Factorial
Nested
Split Plot
Today's data-driven world requires earth and environmental scientists to have skills at the intersection of domain and data science. These skills are imperative to harness information contained in a growing volume of complex data to solve the world's most pressing environmental challenges. Despite the importance of these skills, Earth and Environmental Data Science (EDS) training is not equally accessible, contributing to a lack of diversity in the field. This creates a critical need for EDS training opportunities designed specifically for underrepresented groups. In response, we developed the Earth Data Science Corps (EDSC) which couples a paid internship for undergraduate students with faculty training to build capacity to teach and learn EDS using Python at smaller Minority Serving Institutions. EDSC faculty participants are further empowered to teach these skills at their home institutions which scales the program beyond the training lead by our team. Using a Rasch modeling approach, we found that participating in the EDSC program had a significant impact on undergraduate learners' comfort and confidence with technical and nontechnical data science skills, as well as their science identity and sense of belonging in science, two critical aspects of recruiting and retaining members of underrepresented groups in STEM. Supplementary materials for this article are available online.
Keywords
Earth Science
Environmental Science
GIS
Python
Rasch Modeling
With the rise of the popularity of Bayesian methods and accessible computer software, teaching and learning about Bayesian methods are expanding. However, most educational opportunities are geared toward statistics and data science students and are less available in the broader STEM fields. In addition, there are fewer opportunities at the K-12 level. With the indirect aim of introducing Bayesian methods at the K-12 level, we have developed a Bayesian data analysis activity and implemented it with 35 mathematics and science pre-service teachers. In this article, we describe the activity, the web app supporting the activity, and pre-service teachers' perceptions of the activity. Lastly, we discuss future directions for preparing K-12 teachers in teaching and learning about Bayesian methods.
Keywords
Bayesian Methods
Grades K-12
Mathematics Education
Science Education
Teacher Education