(This article was originally published at Statistical Modeling, Causal Inference, and Social Science, and syndicated at StatsBlogs.)
Changing everything at once: Student-centered Learning, computerized practice exercises, evaluation of student progress, and a modern syllabus to create a completely new introductory statistics course
Andrew Gelman, Department of Statistics, Columbia University
It should be possible to improve the much-despised introductory statistics course in several ways: (1) altering the classroom experience toward active learning, (2) using adaptive software to drill students with questions at their level, repeating until students attain proficiency in key skills, and (3) standardized pre-tests and post-tests, both for measuring individual students’ progress and for comparing the effectiveness of different instructors and different teaching strategies. All these ideas are well established in the education literature but do not seem to be part of the usual statistics class. We would like to implement all these changes in the context of (4) a restructuring of the course content, replacing hypothesis testing, p-values, and the notorious “sampling distribution of the sample mean” with ideas closer to what we see as good statistical practice. We will discuss our struggles in this endeavor. This work is joint with Eric Loken.
The talk’s not actually happening until 16 May. But I thought, this time why not post something five months early instead of three months late (as I’m actually writing this in mid-Sept).
Anyway, if you happen to be at this conference (which, as the name suggests, will be entirely online), I hope you like my talk. I’d link to the conference webpage but it’s so horribly ugly (ironic given that it’s for an electronic conference) that I’ll spare you.
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