STA 771S: Teaching statistics

This course is designed to help students become better teachers and communicators of statistics. Students will be introduced to innovative pedagogical approaches for teaching college level statistics and data science, with an emphasis on introductory level education.


Week 1 - A fresh look at intro stats

Aug 28

Slides:

Slides are made in Keynote. Source file can be found here.

Discussion:

  • What do you know about a "traditional" intro statistics curriculum? What topics / contents from this curriculum should we hold on to? What can go, and why?
  • What are biggest challenges to moving to a more computational intro statistics curriculum?
  • What do you want to get out of this course? Add notes to course Etherpad.

Readings:


Week 2 - Pedagogy

Sep 4

Slides:

Discussion:

  • What are advantages / disadvantages for lecturing for an entire class period?
  • What are your thoughts on the following aspects of team creation? What are pros and cons that you can think of for each option?
    • Formation: Student choice vs. assigned
    • Consistency: Same team throughout the semester vs. changing
    • Makeup: Homogenous vs. heterogenous with respect to background in course material
  • What are advantages / disadvantages for using team based learning pedagogy in your class?

Readings:

Assignments:

  • Visit a class:

    • Visit a class (not lab session) between now and November 6.
    • You're welcomed to visit my class: STA 112FS - TuTh 10:05 - 11:20 at Link Classroom 1.
    • Reach out to any other faculty members to ask for their permission for a visit and whether there is a particular day they prefer you attend.
  • Post on Piazza by September 11 under the relevant thread:

    • Find a teaching statement online, preferably from a statistician or an academic in a related field and post a link to it. If you can find multiple examples, you can post all, but make sure to note which one you think is the best.
    • Find a syllabus that you think is "good" and post a link to it / upload it.
    • Post which class you're visiting and when.

Week 3 - Philosophy and design

Sep 11

Slides:

Teaching statement slides are made in LaTeX Beamer and course design slides are made with the xaringan package. Source files for both can be found here.

Discussion:

  • What is your teaching philosophy? Think, pair, share, and summarize in 1-2 sentences.
  • How do you tailor your content for your audience: undergrads / grads / your boss?

Readings:

Assignment:

Write your teaching statement

  • Assume this is for hiring (1-2 pages)
  • First draft of your teaching statement is due Fri, Sep 28 at 3pm on Sakai
  • Between Fri, Sep 28 and Tue, Oct 2 you will review one other person's statement
  • On Tue, Oct 2 we will workshop teaching statements
  • Final draft of teaching statement is due Tue, Oct 16 by class time on Sakai + printed in class (your printout should look nice printed in black and white!) -- If you need feedback earlier for a job application deadline, submit it earlier and let me know your deadline

Week 4 - Computational infrastructures

Sep 18

Slides: Computational infrastructures for teaching with R

Slides are made in Keynote. Source files for both can be found here.

Discussion:

  • How important a skill is being able to install and run a new piece of software? When should students learn this skill?
  • How do these software solutions interplay with FERPA?
  • How do we achieve similar goals if not working with R?

Readings:

Çetinkaya-Rundel, M., & Rundel, C. (2018). Infrastructure and tools for teaching computing throughout the statistical curriculum. The American Statistician, 72(1), 58-65.


Week 5 - Mini-teaching #1

Sep 25

  • Teach a topic in intro stat or intro data science
  • 10 minute presentation
  • Recorded
  • Meet with Mine to review over the next two weeks

Week 6 - Policies

Oct 2

Slides:

Discussion: What are some robust and successful course policies you encountered in the sample syllabi you collected?


Week 7 - No class: Fall break

Oct 9


Week 8 - Mini-teaching #2

Oct 16

  • Re-do your teaching of the same topic, with suggested improvements
  • 10 minute presentation
  • Reflect on what changed

Week 9 - Teaching data science, reproducibly - Pt 1

Oct 23

Slides: Running your course on GitHub


Week 10 - Assignments and assessments

Oct 30

Slides: Active learning

Slides are made in Keynote. Source file can be found here.

Workshop: Designing active learning components


Week 11 - Mini-teaching #3

Nov 6

  • Run an active learning session on a topic of your own choice
  • 15 minute presentation
  • Presenters: Abbas, Liz, Kelly

Week 12 - Mini-teaching #3

Nov 13

  • Run an active learning session on a topic of your own choice
  • 15 minute presentation
  • Presenters: Alex, Jake, Lindsey, Tori

Assignment: Due Tuesday, Nov 27 on Sakai

  • What was the active learning component of your presentation? What is/are the learning goal(s) for the activiti?
  • Watch over your own active learning teaching session and answer the following questions:
    1. Highlight one or more aspects of the presentation that you think "worked", i.e. went according to plan and you thought was successful in achieving the learning goals.
    2. Highlight one or more aspects of the presentation that you think didn't work. If you had to do this over, what would you do differently?
    3. What were the students doing when you were talking/lecturing? (You won't be able to see this in the video, but you might remember from your session.)
    4. What were you doing when the students were actively working? How did that feel at the time, how does it look on the video? If you were to do it again, what would you do during that time -- do the same thing or do something different? If different, what?
  • Watch over a classmate's active learning teaching session.
    • Tori to watch Lindsey's presentation
    • Lindsey -> Kelly
    • Kelly -> Abbas
    • Abbas -> Liz
    • Liz -> Alex
    • Alex -> Jake
    • Jake -> Tori
  • Answer the following questions for your classmate's presentation:
    1. What was the active learning component of their presentation? What do you think the learning goal(s) of this component is/are?
    2. Highlight one or more aspects of the presentation that you think "worked", i.e. went according to plan and you thought was successful in achieving the learning goals.
    3. Highlight one or more aspects of the presentation that you think could be improved and explain why and provide suggestions for how to improve.

Week 13 - No class: Thanksgiving

Nov 20


Week 14 - Teaching data science, reproducibly - Pt 2

Nov 27

  • Reproducible workflows for teaching and learning R
  • Teaching R to new useRs with the tidyverse