I Hear, I Forget. I Do, I Understand: A Modified Moore-Method Mathematical Statistics Course

Abstract

Moore introduced a method for graduate mathematics instruction that consisted primarily of individual student work on challenging proofs (Jones 1977). Cohen (1992) described an adaptation with less explicit competition suitable for undergraduate students at a liberal arts college. This paper details an adaptation of this modified Moore-method to teach mathematical statistics, and describes ways that such an approach helps engage students and foster the teaching of statistics.Groups of students worked a set of 3 difficult problems (some theoretical, some applied) every two weeks.Class time was devoted to coaching sessions with the instructor, group meeting time, and class presentations. R was used to estimate solutions empirically where analytic results were intractable, as well as to provide an environment to undertake simulation studies with the aim of deepening understanding and complementing analytic solutions. Each group presented comprehensive solutions to complement oral presentations. Development of parallel techniques for empirical and analytic problem solving was an explicit goal of the course, which also attempted to communicate ways that statistics can be used to tackle interesting problems. The group problem solving component and use of technology allowed students to attempt much more challenging questions than they could otherwise solve.

Flipping an Introductory Biostatistics Course: A Case Study of Student Attitudes and Confidence

“…The most common suggestions for improvement were more thorough feedback on the homework assignments and, not surprisingly, more real-life examples. Similar to other literature (Horton 2013;Wilson 2013), students noticed this course took more individual effort than the traditional courses. For the most part, students saw benefit in the trade-offs, as there was only one negative comment specifically about the flipped format.…”

“…Appropriately designed group work allows students to learn from each other and develop communication skills around the statistical process (Horton 2013;Hund 2015). Curran et al (2013) described a peer-led team learning model.…”

Challenges and Opportunities for Statistics and Statistical Education: Looking Back, Looking Forward

“…So how do we train students to be able to be life-long learners? Besides providing a useful check on analytic answers, simulations may provide insights into how to solve a problem (Horton 2013). I believe that use of empirical problem solving, using computational tools and simulation, may free up aspects of our curriculum and allow students to be nimbler and better able to find answers to problems that didn't exist when they were trained.…”

Fostering Conceptual Understanding in Mathematical Statistics

“…Such efforts include the use of in-depth case studies and statistical software to integrate statistical theory and practice Speed, 1999, 2000); the use of statistical software, such as R, to enhance student understanding of concepts (Buttrey et al, 2001;Horton et al, 2004;Nolan and Temple Lang, 2003); and the use of student-centered problem-based learning approaches to develop students' statistical thinking and problem-solving skills and foster collaborative learning (Bates Prins, 2009;Horton, 2013). To date, these efforts primarily focus on the second semester of the mathematical statistics sequence and often require substantial changes to curriculum (e.g., Horton, 2013;Nolan and Speed, 1999).…”