JSM 2020 Takeaways

With the 2020 Joint Statistical Meetings in the rear view mirror and the fall semester under way, we wanted to take some time to reflect on our first virtual conference of this scale. While we were pleased to see the progress in the field of statistics education, we left feeling a little disappointed in the experience of the virtual conference overall. Below Adam and guest contributor Ann Brearley (University of Minnesota) share some of their favorite takeaways from the conference, which include topics like social justice, consulting and collaborating, teaching math stats courses, and approaches for talking about more difficult concepts…

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Icebreakers! (not the gum)

To start off this post, it’s probably fitting to quote a Duran Duran song (1990): “The lasting first impression is what you’re looking for.”

Besides starting with the usual housekeeping on the first day of class, why not set the tone for the course by providing students with a glimpse into the classroom environment as a community of learners, get students to connect with one another, AND do statistics? Look no further than an Icebreaker activity! We present two Icebreakers that can get your class (either in-person or online) off to a great start: Questions on the Back (a classic) and How Old? Visualization

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Slack for (A)synchronous Course Communication

Contributing author Albert Y. Kim is an assistant professor of statistical & data sciences. He is a co-author of the fivethirtyeight R package and ModernDive, an online textbook for introductory data science and statistics. His research interests include spatial epidemiology and model assessment and selection methods for forest ecology. Previously, Albert worked in the Search Ads Metrics Team at Google Inc. as well as at Reed, Middlebury and Amherst colleges. You can follow him on Twitter @rudeboybert.

Contributing author R. Jordan Crouser is an Assistant Professor of Computer Science at Smith College. He is published in the areas of visualization theory, human-computer interaction, educational technology, visual analytics systems and human computation. For more information, visit his faculty page.

Contributing author Benjamin S. Baumer is an assistant professor in the Statistical & Data Sciences program at Smith College. His research interests include sports analytics, data science, statistics and data science education, statistical computing, and network science. For more information, visit his faculty page.

You might have heard of Slack before. But what is it? Is it email? Is it a chat room? Slack describes their flagship product as a “collaboration hub that can replace email to help you and your team work together seamlessly.” In this blogpost, we’ll describe how we’ve been using Slack for asynchronous course communication, as opposed to the synchronous course communications afforded by Zoom and other remote conferencing platforms.

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Teaching Programming vs. Training Programmers: Where the Means Justify the Means

Contributing author Jonathan Duggins is a Teaching Assistant Professor in the Department of Statistics at North Carolina State University.

Introduction

Most of us statistics (and data science!) educators understand that knowing how to use statistical software is integral to student successes, both in their coursework and in their careers, for our statistics and data science majors. However, in many degree programs, software usage is seen as a means to an end – getting an analysis – rather than an end goal in its own right. How did this come about, why does it matter, and what can we do to change our software-related instruction? These are the questions I discuss below, first by looking at some history of programming in these contexts, then by presenting two current philosophies on how to incorporate programming.

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Online Strategies due to COVID-19, Part 2

In this series of posts, the StatTLC blog team describes how we are managing with the abrupt changes to our courses. In this, we share some of our decisions (and the thinking that went into them), the tools we are using, and tips. We are teaching a diverse set of classes this semester at institutions with many different technology tools. We hope that you find this useful as you make some decisions for your classes moving forward in the time of COVID-19.

Continue reading “Online Strategies due to COVID-19, Part 2”

Online Strategies due to COVID-19, Part 1

In this series of posts, the StatTLC blog team describes how we are managing with the abrupt changes to our courses. In this, we share some of our decisions (and the thinking that went into them), the tools we are using, and tips. We are teaching a diverse set of classes this semester at institutions with many different technology tools. We hope that you find this useful as you make some decisions for your classes moving forward in the time of COVID-19.

Continue reading “Online Strategies due to COVID-19, Part 1”

Adapting Statistics Instruction for an Online Environment in the Wake of COVID-19

Contributing author Christopher Engledowl is an Assistant Professor of Mathematics Education and Quantitative Research Methods at New Mexico State University.

The world is currently experiencing unprecedented forced movement from face-to-face interaction to a completely virtual form of interaction. Higher education institutions have quickly made sweeping policy decisions that have, overnight, overhauled the classroom learning environment. These decisions have resulted in many people questioning the kinds of quality that can be expected—especially from instructors who have never taught an online course. Simultaneously, many organizations have expanded the capacity of their digital platforms to accommodate the insurgence of people making use of their products for teaching and learning.

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Hello, is anyone there? Instructor presence in an online statistics course

Contributing author John Haubrick is an instructional designer and assistant teaching professor for the Penn State Department of Statistics where he supports the teaching and design of the online statistics courses.

With the prevalence of online chat bots and robocalls, we sometimes find ourselves asking: “Are you a machine or a real person?” Students can also experience this when taking an online course with an “absent” instructor. Instructor presence in an online course has been cited in research as a major influence of student satisfaction and engagement, which may impact their ability to learn the course content (e.g., Ladyshewsky, 2013; Gray and DiLoreto, 2016). So what can we do to “show up” to class as an online statistics instructor?

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Developing Statisticians in Intermediate Statistics Courses Through an Applied Project

Contributing author Krista Varanyak is a lecturer at the University of Virginia and an Ignite Scholar.

The field of statistics education tends to focus heavily on introductory courses: How can we engage students who typically struggle in math-based courses? How can we develop statistical consumers? How can we prepare students to be successful beyond introductory courses? However, there is not much literature or resources shared about the teaching of intermediate courses. In many cases, the intermediate courses are designed for students working towards a statistics degree who are learning to be statistical producers. Overall, the goal of these courses, and the statistics major as a whole, is to produce students who will enter the workforce as statisticians. Therefore, it is imperative that students in these intermediate courses develop fundamental practical and interpersonal skills that are required to be a working statistician. Some of these skills include: comparing various analysis techniques to select the appropriate procedure, learning a new concept independently, applying the technique on data using a statistical software, and communicating findings in a formal report either written or orally.

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Visual Inference: Using Sesame Street Logic to Introduce Key Statistical Ideas

As outlined by Cobb (2007), most introductory statistics books teach classical hypothesis tests as

  1. formulating null and alternative hypotheses, 
  2. calculating a test statistic from the observed data, 
  3. comparing the test statistic to a reference (null) distribution, and 
  4. deriving a p-value on which a conclusion is based.

This is still true for the first course, even after the 2016 GAISE guidelines were adapted to include normal- and simulation-based methods. Further, most textbooks attempt to carefully talk through the logic of hypothesis testing, perhaps showing a static example of hypothetical samples that go into the reference distribution. Applets, such as StatKey and the Rossman Chance ISI applets, take this a step further, allowing students to gradually create these simulated reference distributions in an effort to build student intuition and understanding. While these are fantastic tools, I have found that many students still struggle to understand what the purpose of a reference distribution is and the overarching logic of testing. To remedy this, I have been using visual inference to introduce statistical testing, where “plots take on the role of test statistics, and human cognition the role of statistical tests” (Buja et al., 2009). In this process, I continually encourage students to apply Sesame Street logic: which one of these is not like the other? By using this alternative approach that focuses on visual displays over numerical summaries, I have been pleased with the improvement in student understanding, so I thought I would share the idea with the community.

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