Note the change of title for this seminar.
As data have become more prevalent in many fields, it is imperative that undergraduate students are equipped with the skills necessary for working with data in this modern environment. There has been significant innovation in introductory statistics and data science courses; however, there has not been as much focus on innovating subsequent courses. In this talk I will share innovations to an undergraduate regression analysis course, the second statistics course taken by many students from a variety of disciplines. I will discuss three principles that have guided the modernization of the course, along with how these principles align with the necessary skills of practice outlined in recent statistics and data science curriculum guidelines. I will present pedagogical strategies and examples from in-class activities and assignments. I will conclude with a discussion about some challenges I’ve faced, the impact of the innovations, and next steps for the course. Though the talk will focus on the undergraduate regression course, the principles and pedagogical strategies are applicable for courses throughout the undergraduate curriculum.
Maria Tackett (Duke University, USA)
Maria Tackett is an Assistant Professor of the Practice in the Department of Statistical Science at Duke University. Her research focuses on examining factors that impact students’ sense of belonging in introductory math and statistics courses, along with identifying pedagogies that foster community and supportive learning environments. Maria has worked with the Bill & Melinda Gates Foundation and the Charles A. Dana Center on projects to develop courseware and instructional materials for undergraduate introductory statistics courses. She is an associate editor for the Journal of Statistics and Data Science Education, an editor for the “Taking a Chance in the Classroom” column in CHANCE magazine, and a member of the steering committee for the current GAISE College Report revision.
Session chair: Nicola Rennie
RSS Teaching Statistics Section