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.
Contributing author Jonathan Duggins is a Teaching Assistant Professor in the Department of Statistics at North Carolina State University.
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.
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.
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?
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.
Contributing author Catherine Case is a lecturer at the University of Georgia and the lesson plan editor for Statistics Teacher.
This post is really inspired by a plenary talk given by Jim Stigler at USCOTS 2015. He’s a psychologist at UCLA, and in his USCOTS talk, he emphasized the idea of productive struggle. He talked about different teaching cultures around the world, and how American classrooms often feature “quick and snappy” lessons as opposed to “slow and sticky” lessons, despite the fact that making the process of learning harder can actually lead to deeper, longer-lasting understanding.
His ideas really challenged me, because I often teach fairly large classes (120 – 140 students per section), and nowhere is “quick and snappy” more highly valued than in a large lecture. There’s definitely tension in large classes between efficiency and productive struggle.