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.

Continue reading “Adapting Statistics Instruction for an Online Environment in the Wake of COVID-19”

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?

Continue reading “Hello, is anyone there? Instructor presence in an online statistics course”

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.

Continue reading “Visual Inference: Using Sesame Street Logic to Introduce Key Statistical Ideas”