arXiv subjects

Mine Dogucu

Publications and source records attributed to Mine Dogucu.

9 recordsLinked to original sources

arXiv2026

Engaging students with statistics through choice of real data context on homework

Statistics educators recommend teaching with real data with relevant contexts, but defining relevancy is challenging and varies by student. We investigated whether providing student choice of data context increases engagement through a quasi-experiment in two sections of an introductory probability and statistics course at a large public university (n=65 consenting students). Sections alternated as treatment and control: during their treatment, students chose weekly homework from three similar instructor-provided options varying by data context; during control weeks, they received randomly assigned contexts. We found no significant difference in homework grades between treatment and control conditions. However, thematic analysis revealed students with choice reported enhanced engagement and motivation, greater appreciation for statistics' real-world value, and increased autonomy. Students overwhelmingly preferred contexts relevant to their interests, experiences, daily lives, and career paths-though preferences varied considerably across individuals. Based on these findings, we provide four recommendations for statistics educators: (1) use real data with authentic contexts, (2) select contexts students care about, (3) incorporate variety across data contexts, and (4) consider choice as a pedagogical tool.

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arXiv2025

Statisticians Training STEM Educators in Statistics Methods and Pedagogy: A Case Study of Instructor Training in Bayesian Methods

Educating the next generation of scientists in statistical methodology is an important task. Educating their instructors in statistical content knowledge and pedagogical knowledge is as important and provides an indirect impact of students' learning. Statisticians are in a place to lead train-the-trainer (TTT) programs in different methods. We present our instructor training program in Bayesian methods as an effective case study of a TTT model. In addition to describing the details of the structure of our training program, we share our experience in designing and implementing our program including the challenges we face, the opportunities created, and our recommendations for TTT programs led by statisticians.

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arXiv2024

A Systematic Literature Review of Undergraduate Data Science Education Research

The presence of data science has been profound in the scientific community in almost every discipline. An important part of the data science education expansion has been at the undergraduate level. We conducted a systematic literature review to (1) portray current evidence and knowledge gaps in self-proclaimed undergraduate data science education research and (2) inform policymakers and the data science education community about what educators may encounter when searching for literature using the general keyword 'data science education.' While open-access publications that target a broader audience of data science educators and include multiple examples of data science programs and courses are a strength, significant knowledge gaps remain. The undergraduate data science literature that we identified often lacks empirical data, research questions and reproducibility. Certain disciplines are less visible. We recommend that we should (1) cherish data science as an interdisciplinary field; (2) adopt a consistent set of keywords/terminology to ensure data science education literature is easily identifiable; (3) prioritize investments in empirical studies.

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arXiv2023

Automated grading workflows 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 formative 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 call 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. By outlining the motivations behind the development of this package and the considerations underlying its design, we hope this article will provide data science and statistics educators with ideas for improving their grading workflows, possibly developing new grading tools or considering use gradetools as their grading workflow assistant.

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arXiv2023

The Design and Implementation of a Bayesian Data Analysis Lesson for Pre-Service Mathematics and Science Teachers

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 manuscript, 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.

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arXiv2022

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.

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arXiv2022

The Current State of Undergraduate Bayesian Education and Recommendations for the Future

As a result of the increased emphasis on mis- and over-use of $p$-values in scientific research and the rise in popularity of Bayesian statistics, Bayesian education is becoming more important at the undergraduate level. With the advances in computing tools, Bayesian statistics is also becoming more accessible for the undergraduates. This study focuses on analyzing Bayesian courses for the undergraduates. We explored whether an undergraduate Bayesian course is offered in our sample of 152 high-ranking research universities and liberal arts colleges. For each identified Bayesian course, we examined how it fits into the institution's undergraduate curricula, such as majors and prerequisites. Through a series of course syllabi analyses, we explored the topics covered and their popularity in these courses, and the adopted teaching and learning tools, such as software. This paper presents our findings on the current practices of teaching full Bayesian courses at the undergraduate level. Based on our findings, we provide recommendations for programs that may consider offering Bayesian courses to their students.

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arXiv2022

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 paper, 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.

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arXiv2021

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

Despite 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 opportunity for individuals, while also creating products and policies that perpetuate inequality. Thus, for statistics and data science educators of the next generation, accessibility and inclusion should be of utmost importance in our programs and courses. In this paper, we discuss how we developed an accessibility and inclusion framework, hence a structure for holding ourselves accountable to these principles, for the writing of a statistics textbook. We share our experiences in setting accessibility and inclusion goals, the tools we used to achieve these goals, and recommendations for other educators. We provide examples for instructors that can be implemented in their own courses.

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