Student projects: A reflection of how the course is taught

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Todd Swanson – Hope College

SWANSON_TODD

I’ve had students complete projects in my introductory statistics course for at least 20 years. My expectations, requirements, and outcomes have evolved much over this time including how the data are gathered, what type of data I allow to be gathered, and how the project is presented. Things that were acceptable years ago are not acceptable now. The projects have become more and more like real research and the move in this direction has certainly been aided by using a simulation-based curriculum. [pullquote]Projects now reflect the real research studies that we explore in class every day. [/pullquote] Continue reading →

New Listserv

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For more immediate conversations about these issues, also consider joining the Simulation-Based Inference Listserv. The SBI mailing list  is intended for individuals interested in discussing pedagogical issues related to using simulation-based inference techniques (e.g., randomization tests) in introductory statistics courses as the primary introduction to statistical inference. For more information or to subscribe, go to https://www.causeweb.org/mailman/listinfo/sbi.

How Technology Transformed My Intro Stats Class

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Valorie Val Zonnefeld 2013

Valorie Zonnefeld, Dordt College

Have you ever thought about the reaction to the first slide rule in the 1620s?  Do you think some people pulled up their noses, questioning whether students would forget their “calculations”?  Did others dive in whole-heartedly, touting the advantages of teaching with their fancy new “slipsticks”?  Regardless of the initial reaction, the slide rule has left its mark on the story line of mathematics.  There is no question that technological developments continue to change how mathematics and statistics are taught. [pullquote]The ability to use technology to simulate random phenonema has made statistics more approachable to many students. [/pullquote]

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Assessment Opportunities

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We are looking for individuals/institutions willing to give common assessment items during Winter/Spring 2015.

  • Pre/Post attitude surveys (SATS)
  • Pre/Post concept inventory (modeled after CAOS and GOALS)
  • Embedded multiple choice exam questions (Unit 1: One Proportion, Unit 2: Two Proportions and/or Two Means)

We will send you names of students who participated (if you want to give course credit) as well as a report at the end of the term with your student results and comparison results. For more information, please contact Cindy Nederhoff <Cindy.Nederhoff@dordt.edu>.

Why we aren’t bootstrapping yet

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Beth Chance, Nathan Tintle, and the ISI team

BethHeadntintle

Although we strongly agree that we must do more to help students understand the role of sampling variability in inferential decisions, we have not yet been convinced that a formal treatment of bootstrapping (having students sample with replacement) is the only path to get them there.[pullquote]we worry that the motivation for conducting bootstrapping is less intuitive for students[/pullquote] Continue reading →

Different tools for different audiences

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The Catalyst Group, University of Minnesota
Matt Beckman, Ethan Brown, Bob delMas, Elizabeth Brondos Fry, Nicola Justice, Anelise Sabbag

The Catalyst group at the University of Minnesota has developed two introductory statistics courses that employ simulation-based inference methods: an undergraduate course (EPSY 3264) and a graduate course (EPSY 5261). [pullquote]Simulation-based inference has different audiences, even in the introductory course[/pullquote] Continue reading →

Students building their own simulations: How hard can it be?

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Tim Erickson, Mills College

Joan Garfield tells us that approaching inference using simulation is like teaching students to cook rather than simply to follow recipes.  I’m totally on board with that. In this post, I want to reflect about students can also grow the vegetables—that is, become farmers as well as cooks—and build the simulations themselves. [pullquote]Yet I claim that making students responsible for the hard part is good for learning.[/pullquote] Continue reading →

Moving from learning statistics to discovering statistics

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Scott Rifkin

Scott Rifkin, UCSD

I have tried several different approaches to using technology to help students get a better intuitive understanding of statistical concepts. Although statistical software has been used in introductory statistics classes for quite some time, interfaces that facilitate discovery-based learning rather than calculation are much newer. [pullquote]or I could make an applet specifically targeted towards this common question that will let her discover the answer for herself[/pullquote] Continue reading →

Overview

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Teachers of introductory statistics are increasingly using simulation-based methods to help students learn concepts and methods of statistical inference. Two quick anecdotes:
• The theme of the 2011 U.S. Conference on Teaching Statistics was “The Next BIG Thing,” and the consensus emerging from the conference was that the BIG thing is teaching introductory statistics with simulation-based methods.
• The recently conducted International Conference on Teaching Statistics in Flagstaff (July 2014) included a large number of sessions on this topic, featuring presenters from around the world, often with standing-room-only crowds of attendees. Continue reading →

How did I get started on teaching simulation-based inference?

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Robin Lock, St Lawrence University

Around 1998, Allan Rossman and Beth Chance asked me to help out with a new edition of their popular Workshop Statistics book that would be adapted to use a new software package called Fathom that was being developed by Bill Finzer, then at KCP Technologies. [pullquote]But I could detect light bulbs going on with students thinking, “Oh, that’s what he means by seeing what would happen if the null hypothesis is true!'[/pullquote] Fathom has a lot of neat tools designed to allow students to explore statistical concepts, including a facility to allow students to easily select a sample from a dataset, define any statistic for that sample, and then quickly generate a new dataset with values of that statistic for many new samples. Continue reading →