Monthly Archives: November 2014

Assessment Opportunities

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

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.

we worry that the motivation for conducting bootstrapping is less intuitive for students

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Different tools for different audiences

 

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). 

Simulation-based inference has different audiences, even in the introductory course

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

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. 

Yet I claim that making students responsible for the hard part is good for learning.

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Moving from learning statistics to discovering statistics

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. 

or I could make an applet specifically targeted towards this common question that will let her discover the answer for herself

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