eCOTS 2018 - Posters

Monday, May 21st

P1-01: R Shiny apps for introductory statistics

By Daniel Adrian (Grand Valley State University)

P1-02: Using Clickers to Address Misconceptions and Promote Discussions in Statistics

By Kelly Cline (Carroll College)

P1-03: Writing with Statistics: The Development and Implementation of Writing Workshops to Improve Written Communication Skills in Statistics Graduate Students

By Laura Hildreth, Erin Strickland, Jacob Swisher, & Michelle Miley (Montana State University)

P1-04: Testing The Effectiveness of Student Self-Grading in an Upper Level Statistics Course

By Wilmina Marget & Caroline Bland (John Carroll University)

P1-05: Teaching Statistical Concepts using PechaKucha Presentations

By Jennifer Ann Morrow, Lisa Shiley (University of Tennessee); and Stephanie Kelly (North Carolina A&T State University)

P1-06: Using classroom response systems to foster students' active engagement with statistics

By Damien Raftery (Institute of Technology Carlow)

P1-07: Effects of a Summer Statistics Refresher Boot Camp

By Derek Sauder, Brian C. Leventhal, & Jeanne Horst (James Madison University)

P1-08: Utilizing Free Resources to Advance Students Programming for Data Science Skills: Data Camp

By Adam Sullivan (Brown University)

P1-09: Fantasy Basketball for Statistical Learning

By Karsten Maurer (Miami University)

Tuesday, May 22nd

P2-01: Bayesian Courses for Data Scientists and Cross-Campus Share

By Jim Albert, Jingchen (Monika) Hu (Bowling Green State University)

P2-02: Teaching Version Control in Introductory and Advanced Courses With GitHub Classroom: Experiences and Resources

By Jacob Fiksel, Leah Jager, Margaret Taub, and Johanna Hardin (Johns Hopkins University)

P2-03: Probability of Thrones: Using Battle Strategy to Teach Probability

By Brianna Kurtz, Trenton Schacht (University of Central Florida)

P2-04: Graduate Teaching Assistants' Evolving Conceptualizations of Active Learning

By Elijah S. Meyer, Jennifer L. Green, & Elizabeth G. Arnold (Montana State University)

P2-05: How Adapting the M&M Experiment to the Online Classroom Yielded a Useful Messy Data Set

By Jane Oppenlander (Clarkson University)

P2-06: Efficacy of 'The Islands'-based Projects Compared to Student-Collected Data Projects in Introductory Statistics Courses

By Lisa Rosenberg, Ryne Vankrevelen, Laura Taylor, & Kirstie Doehler (Elon University)

P2-07: Quick Assessment of Open ended Activities in R Commander

By Serrano, V. Cuadros, J., Martori, F., Calvo, M., Miñarro, T., & Gorina, V. (IQS Universitat Ramon Llull)

P2-08: The Power of Technology: A Shiny Applet Assisted Approach to Teaching Statistical Power

By Christian Stratton & Jennifer Green (Montana State University)

P2-09: Designing Full Video Lectures for an Online Introductory Statistics Course

By Whitney Zimmerman (Pennsylvania State University)

Wednesday, May 23rd

P3-01: Learning Data Science with the Help of a Data Exploration Tool

By Philipp Burckhardt, Christopher Genovese, & Rebecca Nugent (Carnegie Mellon University)

P3-02: Building early data science tools for a diverse audience

By Kayla Frisoli, Gordon Weinberg, & Rebecca Nugent (Carnegie Mellon University)

P3-03: Mixing Active Learning and Lecturing: Using Interactive Visualization as a Teaching Tool

By Ted Laderas & Jessica Minnier (Oregon Health & Science University)

P3-04: Online, Hybrid and Flipped Classrooms: Results from a Survey of Statistics and Biostatistics Instructors

By Jane Monaco, Todd Schwartz, & Elizabeth Ajazi (UNC-CH Gillings School of Global Public Health)

P3-05: Peer development of Apps for Teaching: benefits to undergraduate research and to active learning

By Dennis Pearl (Penn State University)

P3-06: Introduction to Data Science with No Prerequisites

By Kimberly Roth & Loren Rhodes (Juniata College)

P3-07: Lessons Learned: Designing Asynchronous Graduate Statistics Courses

By Lisa A. Shipley & Jennifer Ann Morrow (University of Tennessee)

P3-08: Using text analysis to characterize student learning in an introductory statistics & data science course

By Ronald Yurko & Rebecca Nugent (Carnegie Mellon University)

P3-09: Understanding dynamic, multivariate data about society: How can we teach civic statistics?

By Iddo Gal (University of Haifa, Israel)

P3-10: Identifying misconceptions of introductory data science using a think-aloud protocol

By Sangwon Hyun, P Burckhardt, P Elliott, C Evans, K Lin, A Luby, C P Makris, J Orellana, A Reinhart, J Wieczorek, R Yurko, G Weinberg, & R Nugent (Carnegie Mellon University)

Thursday, May 24th

P4-01: Smaller investments and bigger payoffs of using R in intro courses via "tame data"

By Albert Y. Kim (Amherst College)

P4-02: Preparing Underrepresented and First-Generation Students for Careers in Biomedical Big Data Science

By Judith E Canner (California State University, Monterey Bay)

P4-03: Students' conceptual understanding of the distinction between random sampling and random assignment

By Elizabeth Brondos Fry (University of Minnesota)

P4-04: Student-Created Songs in Statistics Class

By Lawrence M. Lesser (The University of Texas at El Paso)

P4-05: Statistical Consulting Experiences at Primarily Undergraduate Institutions

By Tracy Morris, Cynthia Murray, & Tyler Cook (University of Central Oklahoma)

P4-06: Case-Study Based Redesign of a Large Introductory Course for Non-Majors

By Justin Post & Jonathan Duggins (North Carolina State University)

P4-07: Data Analytics in the Introductory Business Statistics Classroom: Which Airline Will be the Official Airline of JSM 2020?

By Deborah Rumsey & Danielle Hoffman (The Ohio State University)

P4-08: STATS4STEM.ORG - An Integrated Learning Platform For Statistics and Data Science Education

By Eric Simoneau (STATS4STEM.ORG)

P4-09: Real Data Are Messy! Cleaning, Organizing, and Drawing Meaning from Raw Data on Maple Trees

By Nicole Williams & Jeffrey McLean (St. Lawrence University)

P4-10: Using FRED Data to teach statistics

By Kim McHale & Heather Chapman (Heartland Community College)

Legend

Birds of a Feather
Breakout
Keynote / Panel
Workshop
Poster Session
Tech Talk
Sponsored
Other

※ Denotes tentative session.

* Denotes repeated session.

Note: All times are listed in Eastern Time.