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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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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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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