Tools and Recommendations for Reproducible Teaching

It is recommended that teacher-scholars of data science adopt reproducible workflows in their research as scholars and teach reproducible workflows to their students. In this article, we propose a third dimension to reproducibility practices and recommend that regardless of whether they teach reproducibility in their courses or not, data science instructors adopt reproducible workflows for their own teaching. We consider computational reproducibility, documentation, and openness as three pillars of reproducible teaching framework. We share tools, examples, and recommendations for the three pillars

Teaching Visual Accessibility in Introductory Data Science Classes with Multi-Modal Data Representations

Although there are various ways to represent data patterns and models, visualization has been primarily taught in many data science courses for its efficiency. Such vision-dependent output may cause critical barriers against those who are blind and visually impaired and people with learning disabilities. We argue that instructors need to teach multiple data representation methods so that all students can produce data products that are more accessible. In this paper, we argue that accessibility should be taught as early as the introductory course as part of the data science curriculum so that regardless of whether learners major in data science or not, they can have foundational exposure to accessibility. As data science educators who teach accessibility as part of our lower-division courses in two different institutions, we share specific examples that can be utilized by other data science instructors.Comment: 17 pages, 6 figure

Training graduate students to teach statistics and data science from a distance

Enrollment in undergraduate statistics and data science courses has rapidly increased in just the last decade, resulting in an increased reliance on graduate teaching assistants (GTAs) and graduate instructors of record (GRIs). In the age of the COVID-19 pandemic, teaching from a distance has become a necessity. Many instructors, including GTAs and GRIs, need to adapt to new technologies and reconsider pedagogical decisions. This paper presents our experiences from a graduate teaching fellowship program created because of the pandemic. The program had two major components: 1) pedagogical workshops attended by teaching fellows from multiple disciplines across the university and 2) one-on-one mentoring by a faculty member from the fellow’s primary discipline. Here, we provide a unique look at graduate training from both the perspective of the mentor and the mentee. We share a sample training curriculum and propose recommendations for those interested in implementing teaching training opportunities for graduate students

Designing and implementing an automated grading workflow for providing personalized feedback to open-ended data science assignments

Open-ended assignments -- such as lab reports and semester-long projects -- provide data science and statistics students with opportunities for developing communication, critical thinking, and creativity skills. However, providing grades and qualitative feedback to open-ended assignments can be very time consuming and difficult to do consistently across students. In this paper, we discuss the steps of a typical grading workflow and highlight which steps can be automated in an approach that we define as an automated grading workflow. We illustrate how gradetools, a new R package, implements this approach within RStudio to facilitate efficient and consistent grading while providing individualized feedback. We hope that this work will help the community of data science and statistics educators use gradetools as their grading workflow assistant or develop their own tools for assisting their grading workflow.Comment: 24 pages, 3 figure

Supporting Bayesian Modeling With Visualizations

With computational advances, Bayesian modeling is becoming more accessible. But because Bayesian thinking often differs from learners’ previous statistics training, it can be challenging for novice Bayesian learners to conceptualize and interpret the three major components of a Bayesian analysis: the prior, likelihood, and posterior. To this end, we developed an R package, bayesrules, which provides tools for exploring common introductory Bayesian models: beta-binomial, gamma-Poisson, and normal-normal. Specifically, within these model settings, the bayesrules functions provide an active learning opportunity to interact with the three Bayesian model components, as well as the effects of different model settings on the model results. We present here the package’s visualization functions and how they can be utilized in a statistics classroom

Framework for Accessible and Inclusive Teaching Materials for Statistics and Data Science Courses

AbstractDespite rapid growth in the data science workforce, people of color, women, those with disabilities, and others remain underrepresented in, underserved by, and sometimes excluded from the field. This pattern prevents equal opportunities for individuals, while also creating products and policies that perpetuate inequality. Thus, it is critical that, as statistics and data science educators of the next generation, we center accessibility and inclusion throughout our curriculum, classroom environment, modes of assessment, course materials, and more. Though some common strategies apply across these areas, this article focuses on providing a framework for developing accessible and inclusive course materials (e.g., in-class activities, course manuals, lecture slides, etc.), with examples drawn from our experience co-writing a statistics textbook. In turn, this framework establishes a structure for holding ourselves accountable to these principles

Bayes BATS

NSF grant proposal for the Bayes BATS project. More details about the project are available at https://www.stat.uci.edu/bayes-bats

The Learning Difficulties Experienced By Introductory Data Science Students

Introduction to Data Science (IDS) courses are being offered by many different departments either as a mandatory or an elective course. Because of the foundational nature of IDS courses to develop students’ understanding of data science, it is important to be aware of students’ potential learning difficulties. To that end, we conducted semi-structured interviews with 14 IDS instructors to study students’ difficulties. Qualitative content analysis was used to analyze the data. IDS instructors reported that students without prior coding experience encountered more syntactic difficulties than their peers. In terms of conceptual and strategic knowledge, students experienced difficulties in understanding principles of data visualization, the basics of coding, joining data sets, debugging and data wrangling. These findings suggest that IDS courses could be improved by addressing student difficulties and invite conducting future research with students to understand the dimensionality of student learning to improve capacity in data science (education)