“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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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.

Motivation

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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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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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.

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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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