Developing an Introductory Statistics Course for Pre‐service Elementary Teachers

Introductory statistics instruction is evolving to help create a statistically literate society. However, sound statistical reasoning skills are not obtained in one general education statistics course, but instead need to be nurtured and developed over the course of one’s entire K‐16 educational experience (Franklin & Kader, 2010). It is therefore imperative that elementary teachers be adequately prepared to foster statistical thinking in their students. While introductory statistics courses typically serve the need to create statistically literate citizens, they do not necessarily provide the opportunity to develop pedagogical content knowledge that goes beyond a basic understanding of the content teachers will be teaching. Erin Blankenship and I recently developed an introductory statistics course for pre‐service elementary teachers. The curriculum, focusing on modeling and simulation, coupled with additional activities, helps pre‐service teachers gain a deeper understanding of statistical concepts as well as transfer those ideas to their future elementary classrooms. This talk will describe the course and provide example course work that captures the progression from student to novice teacher. Overall, the course helps pre‐service teachers recognize the integral role they play in a student’s entire education, and we hope it can serve as a model/resource for others interested in pre‐service teacher development

Student Performance in Curricula Centered on Simulation-Based Inference: A Preliminary Report

Simulation-based inference (e.g., bootstrapping and randomization tests) has been advocated recently with the goal of improving student understanding of statistical inference, as well as the statistical investigative process as a whole. Preliminary assessment data have been largely positive. This article describes the analysis of the first year of data from a multi-institution assessment effort by instructors using such an approach in a college-level introductory statistics course, some for the first time. We examine several pre-/post-measures of student attitudes and conceptual understanding of several topics in the introductory course. We highlight some patterns in the data, focusing on student level and instructor level variables and the application of hierarchical modeling to these data. One observation of interest is that the newer instructors see very similar gains to more experienced instructors, but we also look to how the data collection and analysis can be improved for future years, especially the need for more data on nonusers

Pedagogical Possibilities for the 2048 Puzzle Game

In this paper, we describe an engaging puzzle game called 2048 and outline a variety of exercises that can leverage the game’s popularity to engage student interest, reinforce core CS concepts, and excite student curiosity towards undergraduate research. Exercises range in difficulty from CS1-level exercises suitable for exercising and assessing 1D and 2D array skills to empirical undergraduate research in Monte Carlo Tree Search methods and skilled heuristic evaluation design

Simulation modelling: Educational development roles for learning technologists

Simulation modelling was in the mainstream of CAL development in the 1980s when the late David Squires introduced this author to the Dynamic Modelling System. Since those early days, it seems that simulation modelling has drifted into a learning technology backwater to become a member of Laurillard's underutilized, ‘adaptive and productive’ media. Referring to her Conversational Framework, Laurillard constructs a pedagogic case for modelling as a productive student activity but provides few references to current practice and available resources. This paper seeks to complement her account by highlighting the pioneering initiatives of the Computers in the Curriculum Project and more recent developments in systems modelling within geographic and business education. The latter include improvements to system dynamics modelling programs such as STELLA®, the publication of introductory textbooks, and the emergence of online resources. The paper indicates several ways in which modelling activities may be approached and identifies some educational development roles for learning technologists. The paper concludes by advocating simulation modelling as an exemplary use of learning technologies ‐ one that realizes their creative‐transformative potential

Curriculum Guidelines for Undergraduate Programs in Data Science

The Park City Math Institute (PCMI) 2016 Summer Undergraduate Faculty Program met for the purpose of composing guidelines for undergraduate programs in Data Science. The group consisted of 25 undergraduate faculty from a variety of institutions in the U.S., primarily from the disciplines of mathematics, statistics and computer science. These guidelines are meant to provide some structure for institutions planning for or revising a major in Data Science