arXiv subjects

Maria Tackett

Publications and source records attributed to Maria Tackett.

4 recordsLinked to original sources

arXiv2023

Classroom Community amid Covid-19: A Mixed-Methods Study of Undergraduate Students in Introductory Mathematics and Statistics

A strong sense of classroom community is associated with many positive learning outcomes and is a critical contributor to undergraduate students' persistence in STEM, particularly for women and students of color. This chapter describes a mixed-methods investigation into the relationship between classroom community and course attributes in introductory undergraduate mathematics and statistics courses, mediated by student demographics. The project was motivated by and conducted amid the Covid-19 pandemic: data were collected from online courses in the 2021-21 academic year and from hybrid and in-person courses in the 2021-22 academic year. Quantitative data was gathered from both students and instructors and analyzed using structural equation modeling. The primary instrument was the validated Classroom Community Scale - Short Form. These quantitative results are complemented and contextualized by thematic and textual analyses of focus group data, gathered using a newly developed protocol piloted during the 2021-22 academic year. All data comes from a highly selective private university in the United States. Preliminary practical implications of the study include the value of synchronous participation in fostering connectedness and the importance of attending to students' personal identities in understanding their experiences of belonging.

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arXiv2022

A validation of the short-form classroom community scale for undergraduate mathematics and statistics students

This study examines Cho and Demmans Epp's short-form adaptation of Rovai's well-known Classroom Community Scale (CCS-SF) as a measure of classroom community among introductory undergraduate math and statistics students. A series of statistical analyses were conducted to investigate the validity of the CCS-SF for this new population. Data were collected from 351 students enrolled in 21 online classes, offered for credit in Fall 2020 and Spring 2021 at a private university in the United States. Further confirmatory analysis was conducted with data from 128 undergraduates enrolled in 13 in-person and hybrid classes, offered for credit in Fall 2021 at the same institution. Following Rovai's original 20-item CCS, the 8-item CCS-SF yields two interpretable factors, connectedness and learning. This study confirms the two-factor structure of the CCS-SF, and concludes that it is a valid measure of classroom community among undergraduate students enrolled in remote, hybrid, and in-person introductory mathematics and statistics courses.

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arXiv2022

Three principles for modernizing an undergraduate regression analysis course

As data have become more prevalent in academia, industry, and daily life, it is imperative that undergraduate students are equipped with the skills needed to analyze data in the modern environment. In recent years there has been a lot of work innovating introductory statistics courses and developing introductory data science courses; however, there has been less work beyond the first course. This paper describes innovations to Regression Analysis taught at Duke University, a course focused on application that serves a diverse undergraduate student population of statistics and data science majors along with non-majors. Three principles guiding the modernization of the course are presented with details about how these principles align with the necessary skills of practice outlined in recent statistics and data science curriculum guidelines. The paper includes pedagogical strategies, motivated by the innovations in introductory courses, that make it feasible to implement skills for the practice of modern statistics and data science alongside fundamental statistical concepts. The paper concludes with the impact of these changes, challenges, and next steps for the course. Portions of in-class activities and assignments are included in the paper, with full sample assignments and resources for finding data in the supplemental materials.

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arXiv2020

Implementing version control with Git and GitHub as a learning objective in statistics and data science courses

A version control system records changes to a file or set of files over time so that changes can be tracked and specific versions of a file can be recalled later. As such, it is an essential element of a reproducible workflow that deserves due consideration among the learning objectives of statistics and data science courses. This paper describes experiences and implementation decisions of four contributing faculty who are teaching different courses at a variety of institutions. Each of these faculty have set version control as a learning objective and successfully integrated one such system (Git) into one or more statistics courses. The various approaches described in the paper span different implementation strategies to suit student background, course type, software choices, and assessment practices. By presenting a wide range of approaches to teaching Git, the paper aims to serve as a resource for statistics and data science instructors teaching courses at any level within an undergraduate or graduate curriculum.

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