USCOTS 2019 - Posters

Friday May 17th, 11:00 am – 12:00 pm

P1-01: Statistics in Works of Fiction – In Three Acts

By André Michelle Lubecke, Lander University

P1-02: Classroom Stats: Fun, Flexible, Free Mobile Data Collection and Web-Based Analysis

By Adam Childers and David Taylor, Roanoke College

P1-03: There is an App for That

By Bernhard Klingenberg, Williams College and New College of Florida

P1-04: R Shiny Apps for Teaching and Learning Statistics

By Dan Adrian, Grand Valley State University

P1-05: Pre-Conceptions of Statistical Inference in Biostatistics

By Aimee Schwab-McCoy, Creighton University

P1-06: Understanding Statistics for Good: Teaching Social Responsibility through Statistical Consulting in a Service Learning Course

By Judith E. Canner and Alana Unfried, California State University, Monterey Bay

P1-07: A Course in Biostatistical Literacy: Learning to Evaluate Evidence

By Laura Le and Ann Brearley, University of Minnesota

P1-08: Simulation-Based Inference and the Large-Enrollment Class: Assessing the Impact of a Curricular Shift

By Daisy Philtron and Patricia Buchanan, The Pennsylvania State University

P1-09: Interactive Class Examples with R Shiny to Build Student Intuition

By Ryne VanKrevelen, Elon University

P1-10: Developing a Web-based Dynamic Graphical Software, eStat, for All Levels of Statistics Education

By Jung Jin Lee, Soongsil University

P1-11: Real DataSETs: Student Engagement Techniques for the Advanced Data Analysis Course

By Christopher Kinson and David Unger, University of Illinois at Urbana-Champaign

P1-12: Bringing Visual Inference to the Classroom

By Adam Loy, Carleton College

P1-13: Characterizing Undergraduate Biology Students’ Graphing Practices

By Elizabeth Suazo-Flores, Stephanie M. Gardner, Joel K. Abraham, Anupriya Karippadath, Eli Meir, and Susan Maruca (Perdue University)

P1-14: The Use of a Data and Statistics Integrity Module in Introductory Courses

By Lisa W. Kay, Eastern Kentucky University

P1-15: External Representations in Introductory Statistics

By Sheri E. Johnson (University of Georgia); Dana Kirin (Portland State University); Samuel Cook (Boston University); Robert Sigley (Texas State University); Asli Mutlu (North Carolina State University)

P1-16: Supporting STEM Teachers’ Statistical Learning: A Modified “Choose Your Own Adventure”

By Sandra Madden, University of Massachusetts Amherst

P1-17: Statistical Consulting… Staffing With Undergraduates!

By Joe Nolan, Northern Kentucky University

P1-18: AP Statistics Teaching Survey

By Taylor Harrison and Heather Barker, North Carolina State University

P1-19: A Comparison of Teaching Methods in Introductory Statistics: Lecture-Based Teaching vs. Team-Based Learning

By Maryann S. Allen, Colby-Sawyer College

P1-20: Statistical Problem-Solving Cycles while Solving Simulation Tasks During Guided Interviews

By Jonathan Brown, University of Minnesota

P1-21: Developing Critical Statistical Literacy through the Use of Online Discussion Boards

By Nina G. Bailey, University of North Carolina at Charlotte

P1-22: A Slippery Slope for New Instructors: How Introductory Statistics Turns into Remedial Mathematics

By Kelly Findley, University of Illinois

P1-23: Computational Workshops to Facilitate Implementation of Statistics in Scientific Research

By Allison Theobold and Stacey Hancock, Montana State University

P1-24: Finding Meaning in a Multivariable World: A Conceptual Approach to an Algebra-Based Second Course in Statistics

By Beth Chance, Karen McGaughey, and Soma Roy (Cal Poly); Todd Swanson and Jill VanderStoep (Hope College); Nathan Tintle (Dordt College)

P1-25: Multi-Institutional Assessment of Changes in Student Attitudes for Introductory College Courses with and without a Focus on Simulation Based Inference

By Shea Reynolds and Beth Chance, Cal Poly

P1-26: Exploring and Addressing Sampling Misconceptions in Statistical Reasoning Using Simulations

By Jessie Store and Davie Store, Alma College

P1-27: The Evolution of AP Statistics: How Big Data and Machine Learning are Changing the Course

By Kyle Barriger, Castilleja School

P1-28: Introducing Individualized Pathways and Resources to Adaptive Control Theory-Inspired Scientific Education (iPRACTISE) System

By Jungmin Lee, Jonathan Park, Sy-Miin Chow, and Dennis Pearl, The Pennsylvania State University

P1-29: Assessing Student Conceptual Competencies using Bayesian Networks

By Wentao Yan, Dennis Pearl, and Matt Beckman, The Pennsylvania State University

P1-30: Student Developed Shiny Apps for Teaching Statistics

By Samuel Messer and Yinqi Zhang, The Pennsylvania State University

P1-31: Changing Paradigms: Faculty Moving to Data Science from Other Disciplines

By Bernard Ricca, Bruce Evan Blaine, Kathleen Donovan, and Anne Geraci, St. John Fisher College

P1-32: What Makes a Good Statistical Task?

By Catherine Case, Christine Franklin, and Kaycie Maddox, University of Georgia

P1-33: Visualizing Students’ Learning Process in Statistics Using Traces from R Commander

By Vanessa Serrano, Jordi Cuadros, Francesc Martori, Antoni Miñarro, Miquel Calvo, Pablo Díez, Cristina Montañola, Victor León, IQS Universitat Ramon Llull

P1-34: Establishing a Foundational Understanding of Model Construction in an Intermediate Statistics Course

By Julie Garai (The University of the South); Marina Ptukhina (Whitman College); Walt Stroup (University of Nebraska–Lincoln)

Saturday May 18th, 11:00 am – 12:00 pm

P2-01: Stats and Beyond: Using Song in STEM

By Dennis Pearl (The Pennsylvania State University); Larry Lesser (The University of Texas at El Paso); John Weber (Georgia State University)

P2-02: Customizable, Open Source Shiny Apps for Randomization-Based and Traditional Intro Stat Courses

By Scott Manski, Michigan State University

P2-03: Oranges Are the New Statistics

By Kathrine Frey Frøslie, Norwegian University of Life Sciences

P2-04: The Water Contamination Crisis in Flint, Michigan: Elementary Statistics through Case Study

By Kendra Burbank, University of Chicago

P2-05: Teaching Meta-analysis: Getting Statistics Students to Play in Other Backyards

By Bruce Evan Blaine, St. John Fisher College

P2-06: Design and Analysis of Experiments for an Interdisciplinary Undergraduate Research Experience

By Jennifer Broatch, Pamela Marshall, Jennifer Hackney-Price, and Kimberly Kobojek, Arizona State University West Campus

P2-07: Using the Historical Development of Statistical Techniques to Teach the Value of Evidence from Data

By Charlotte A. Bolch, University of Florida Beverly L. Wood, Embry-Riddle Aeronautical University

P2-08: Teaching Fundamentals of Data Science to a Varied Audience

By Tracy Morris, University of Central Oklahoma

P2-09: It Really Starts Here: Developing Statisticians in an Introductory Regression Course

By Krista Varanyak, The University of Virginia

P2-10: A Longitudinal Study of GTAs’ Experiences with Active Learning

By Elijah S. Meyer, Elizabeth G. Arnold, and Jennifer L. Green, Montana State University

P2-11: Using GitHub and RStudio to Facilitate Authentic Learning Experiences in a Regression Analysis Course

By Maria Tackett, Duke University

P2-12: Evaluating the Impact of a Statistics Refresher Course on Statistics Knowledge, Statistics Self-Efficacy, and Statistics Anxiety

By Beth A. Perkins, S. Jeanne Horst, Brian C. Leventhal, and Nicole M. Zapparrata, James Madison University

P2-13: Math Diagnostics and Relationship to Course Grades

By Adam Molnar and Kesa McDonald, Oklahoma State University

P2-14: Environment Matters: Institution and Course Characteristics and Pedagogy

By Marjorie Bond (Monmouth College); Leyla Batakci (Elizabethtown College); Wendine Bolon (Monmouth College); Douglas Whitaker (Mount Saint Vincent University)

P2-15: Evaluating the Effectiveness of Using Simulations to Teach the Central Limit Theorem and the Sampling Distribution of a Mean

By Veronica Hupper, University of New Hampshire

P2-16: Transformation of Large Lecture Class with the Aid of Technology

By Amanda Ellis, Eastern Kentucky University

P2-17: P-Values Mean What? Conceptions Students are Demonstrating on Open-Ended Responses

By Kristen E. Roland and Jennifer J. Kaplan, University of Georgia

P2-18: Using Real Data in the Classroom: Public Use Data Files from the National Center for Health Statistics

By Michael R. Jiroutek (Campbell University); Matthew J. Hayat (Georgia State University); MyoungJin Kim (Illinois State University); Todd A. Schwartz (The University of North Carolina at Chapel Hill)

P2-19: The Scaffolding of Inferential Reasoning: Informal Analysis of Variance

By Nicolas Robinson and David Trumpower, University of Ottawa

P2-20: Project TIER: Incorporating Reproducibility in the Quantitative Methods Curriculum

By Richard Ball, Haverford College

P2-21: Challenges to Using and Interpreting the SATS-36 Instrument: Do you like statistics? Do your students like statistics? How do you know?

By Douglas Whitaker (Mount Saint Vincent University); Alana Unfried (California State University, Monterey Bay); Marjorie Bond (Monmouth College)

P2-22: Utilizing Distributed Practice Assignments to Develop Students’ Statistical Literacy in an Online Class

By Vimal Rao, University of Minnesota

P2-23: Fostering Engagement and Meaningful Interactions in a Large, Online Intro Course

By Ella Burnham, University of Nebraska–Lincoln

P2-25: Connecting Bayes Factor and the Region of Practical Equivalence (ROPE) Procedure for Testing Interval Null Hypothesis

By Arthur Berg and Vishal Midya (Penn State College of Medicine); Jason Liao (Penn State Cancer Institute)

P2-26: Integrating Statistics with Geometry and Measurement

By Sheri E. Johnson, University of Georgia

P2-27: The Daily Question: Overcoming Math Anxiety, Building Trust through Creating a Classroom Community

By Matthew Hawks, United States Naval Academy

P2-28: Promoting Student Learning and Engagement in Introductory Statistics Labs Using Problem Based Activities

By Jeffrey Woo, The University of Virginia

P2-29: Data Quality’s Role in Sound Inference: Critical, Yet Commonly Ignored in Teaching and Practice, and Due for Increased Emphasis

By Sylvia Kuzmak, Rise Coaching and Consulting LLC, NJ

P2-30: Use of R Markdown in a Graduate Biostatistics Classroom

By Steven Foti, University of Florida

P2-31: Comparing Instructor Created vs. Externally Created Homework Assignments and Their Effects on Exam Scores

By David Swart, Miami University

P2-32: Using Medicare Data and Hospital Compare for Projects in a Partially Flipped Statistics Course for Health Science Students

By Kim Druschel, Sadita Salihovic, Mike May, and Katie Radler, Saint Louis University

P2-33: Students’ Images of Randomness Post-Instruction

By Neil J. Hatfield, University of Northern Colorado

Legend

Birds of a Feather
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※ Denotes tentative session.

* Denotes repeated session.

Note: All times are listed in Eastern Time.