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.
As outlined by Cobb (2007), most introductory statistics books teach classical hypothesis tests as
formulating null and alternative hypotheses,
calculating a test statistic from the observed data,
comparing the test statistic to a reference (null) distribution, and
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.
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.
The theme of this year’s United States Conference on Teaching Statistics (USCOTS) 2019, “Evaluating Evidence,” put an emphasis on the current discussion/debate on p-values and the use of the word “significant” when making statistical conclusions. Conference-wide presentations (1, 2, 3) offered updates to the ASA official statements on p-value based on the special issue of The American Statisticianand potential ways to move beyond significance.
Now that USCOTS is four months behind us, we thought it would be a good idea to reflect on how it has impacted each of our teaching plans for this year. Each member of our editing team has shared their thoughts below. What are yours? [Share your plans in the comments section.]
Our editorial team welcomes you to the Statistics Teaching and Learning Corner (StatTLC), a virtual place to chat about statistics education. While there are many opportunities for educators to interact and disseminate research at conferences and in academic journals, there are fewer opportunities to informally discuss and share ideas and experiences. We have decided to launch this blog in an effort to share our own ideas and experiences teaching statistics and biostatistics at the college-level, but to also provide a platform for the statistics education community to share their ideas and experiences.
You can expect to see relatively short, digestible posts about teaching and pedagogy resources for both face-to-face and online courses, research with a focus on how to implement the findings in the classroom, and teaching experiences from faculty instructors, researchers, and teaching assistants. Be on the lookout for questions prompts and thought provoking statements to inspire further discussion in the comments section of each post!