Building Flexibility into your Course with “Oops tokens”

Before March 2020, I was not a very flexible teacher in terms of course policy. I was quite rigid on deadlines unless there were emergencies and exams were “one and done.” After frantically creating and teaching an online course for spring term 2020 where I tried to be very flexible to accommodate the wide range of home situations faced by my students, I was exhausted from managing the numerous emails and messages on Slack asking for extensions. I needed to find a better balance between increasing flexibility for students and the amount of time I spent negotiating logistical details. That summer, I read Small Teaching Online, which is where I first heard about the idea of using “Oops tokens.” I have now used tokens for three terms, and plan to continue using them for all of my courses.

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Digging into Data Science using the Garden Dataset

Contributing author Lisa Lendway has been teaching statistics and data science at Macalester College for the past 4 1/2 years. Before returning to Macalester College (she’s an alum!), Lisa worked as a statistician/data scientist in a variety of industries including retail, healthcare, and insurance. She strives to stay at the forefront of new R technology and to teach her students those skills so they can thrive in the workforce or graduate school. When not nerding out about R, you can probably find Lisa biking with her spouse, spending time with her 9 & 11 year old kids, or tending her large urban garden.

In the summer of 2020, I decided it was time to collect some data from my garden. I did this for two reasons: 

  1. I was curious about how much food I produced.
  2. I wanted to use the data in my Introductory Data Science course at Macalester College.

I knew the data would be fairly simple, and I liked that it would be a bit personal and give a way for me to connect with students. Although I didn’t know it when I started collecting the data, the personal aspect ended up being especially important during the 2020-21 school year when I was teaching remotely.

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Promoting Diversity, Equity, and Inclusion in the Statistics Classroom

Contributing author Heather Barker is a lecturer of Mathematics and Statistics in the Department of Mathematics and Statistics. She teaches statistics courses as well as courses for mathematics education students. Her research interests lie in statistics education, text mining, and diversity/equity/inclusion pedagogy.

Contributing author Kirsten Doehler is an Associate Professor of Statistics in the Department of Mathematics and Statistics at Elon University. Her research interests include statistics education, survival analysis, and diversity/equity/inclusion. For more information, visit her faculty page.

Many colleges and universities have made a strong effort to make their campuses more inclusive to all people, especially those from underrepresented and historically marginalized minority groups. These efforts include faculty incorporating instruction that focuses on Diversity, Equity, and Inclusion (DEI) issues in their classroom. Statistics instructors have a unique opportunity to engage students in work around DEI considering there is an abundance of data available today to explore these issues. In this blog post, we share some of the data sets and activities we have used in our undergraduate introductory statistics classes to engage students in conversations around big issues that persist in marginalized groups.

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“You mean we have to write in this class!?”

Contributing author Nicole Dalzell is an Assistant Teaching Professor of Statistics at Wake Forest University.

I often hear variations of this statement at the beginning of each semester. Writing is not something students tend to associate with statistics, nor is it something that most stats faculty members have been formally trained to teach. However, the ability to create and critique written communication involving data and statistics is becoming increasingly important. Students who will be using statistics and data in future careers need to be able to communicate their results and processes to get a job. Other students read statements involving statistics on social media or news sources, and then must decide whether that information is correct or reliable. In this post, I will share an activity that can be used to begin to teach statistical writing.

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Ooh, Shiny!: R Shiny apps as a teaching tool

Contributing author Dan Adrian is an Associate Professor of Statistics at Grand Valley State University.

Interactive web applications (or apps), such as the Rossman-Chance collection, are popular tools for teaching statistics because they help illustrate fundamental concepts such as randomness, sampling, and variability through dynamic visualizations. The StatKey collection of apps created for the Lock5 textbook series to demonstrate and perform simulation-based inference is another example1. Historically, despite the utility of the web apps and the ease of their use, it was difficult for most stat educators to create or modify them because of the requisite coding knowledge in HTML, CSS, and Java/Javascript. Thankfully, RStudio created the R package {shiny}, which allows web apps (i.e., Shiny apps) to be created using R code alone, and the HTML/CSS/Javascript work is done by the package “behind the scenes”.

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Tips and Tricks for Online Courses: A Q&A with Instructional Designers

In the School of Public Health at the University of Minnesota, we have a group called the Office of e-Learning Services (ELS) that works closely with instructors who teach fully online. The model we use here is working collaboratively and often with instructional designers in ELS to design our online courses. We (the instructors) are the content experts and they are the technology and learning management system (LMS) experts. They provide instructional support (e.g., activities, assessments), lecture development (e.g., production), and LMS course site development (to name a few; for more info, see this page). I value their insights in online learning and invited them to share their insights on StatTLC. In this post, you will meet two of the members from ELS: Katy Korchik and Kris Woll.

This post is in a question and answer format. Enjoy!

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Making Awesome Tables and Figures Using Gestalt Principles

Contributing author Silas Bergen is an associate professor of statistics and data science at Winona State University.

It was late October in Minnesota, a perfect time for a swim in a local lake! At least, so thought a biology professor at our university (I’ll call him Dr. Doe). This chilly excursion dislodged an idea in his mind: what is the relationship between the body temperature recovery at different areas of the body and water temperature? Dr. Doe collected data from six hard-core swimmers and asked me if my students in the statistical consulting class could help him answer his question “Sure”, I said.

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Reflections from using Collaborations in a Synchronous Virtual Format

I, maybe like many of you, have been reflecting on what teaching methods worked and what did not work during COVID teaching this last year. In particular, I have been thinking about what tools and strategies I learned/used that I would continue to use after we go back to in-person teaching. Many faculty had to learn very quickly how to create virtual lectures and online assignments. This took a lot of time meaning that there are some aspects, possibly collaborative work, that may not have received as much attention as they should moving forward.

In this post I will share how I taught virtual classes in fall 2020 and spring 2021, what I have learned regarding collaboration in a virtual statistics course which has synchronous labs, what I plan to continue to use in future virtual courses, and what I plan to transfer over to in-person teaching.

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Specifications-Grading: An Example

Contributing author Eric Reyes is an Associate Professor in the Department of Mathematics at Rose-Hulman Institute of Technology and has been tinkering with specifications grading in his statistics courses for the past five years.

Sometimes Specifications-Grading (Nilson, 2015) can feel like cooking – I may have all the ingredients, but it doesn’t mean I can turn it into an edible product.  Bouncing ideas off other colleagues has been extremely beneficial.  In this post, I will discuss an implementation I used for an intermediate statistics course.

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Specifications-Grading: An Overview

Contributing author Eric Reyes is an Associate Professor in the Department of Mathematics at Rose-Hulman Institute of Technology and has been tinkering with specifications grading in his statistics courses for the past five years.


What is the least enjoyable part about being a professor? For me, the answer is easily “grading.” For years I dreaded the whole process – determining whether a response was worth 4 points or 5, ensuring consistency across students, and arguing over partial credit instead of discussing course content. Opposite this dread was the knowledge that one of the most important roles we have as educators is providing feedback to students. Since reading Nilson’s (2015) book on Specifications-Grading, I have implemented some variation of the system in all my courses. While I don’t suddenly love grading, I have been convinced this is a better approach; I spend less time grading; and the quality of the student work has improved! In this post, we’ll discuss the key components of Specifications-Grading; in a follow-up post, we’ll discuss an implementation for an intermediate statistics course.

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