2016-Statistics Education


Statistics Education 
Chair: Alison Gibbs (University of Toronto) 
[PDF]

JEREMY BALKA, University of Guelph
A Traditional Introductory Statistics Course with Extensive Video Support  [PDF]
 
In an introductory statistics course, I supply complete course notes, more than 100 accompanying videos, and exercise sets with full solutions. In a sense, the course has developed into an online course with lectures as a bonus. In this talk I will discuss my motivation for setting up the course in this way, some of the pros and cons of this approach, and some of my experiences teaching the course. 
 
DUNHAM BRUCE, University of British Columbia
Investigating How Students Interact with Simulation-based Applets  [PDF]
 
The use of applets in helping students grasp statistical concepts has attracted much interest in the statistical education community. There is evidence such tools may improve student learning, but there is little research on how students engage with applets or how they should be designed. As part of the development of a set of new applets, we have interviewed students and observed how they interact with the applets. Suggestions for how to conduct such research will be discussed, along with some provisional findings.
 
SOHEE KANG, University of Toronto Scarborough
Project-Based Statistics Learning Through Wikispace  [PDF]
 
``Learn by doing'' is the type of experience that educators strive to facilitate for students. Project based learning is an instructional strategy in which students work cooperatively to create a product. A final project in upper year statistics courses provides students with opportunities to build 21st century learning skills such as collaboration, communication, critical thinking, and the use of technology. In this talk, I share the classroom experience of using ``Wikispace'' to create a wiki-like page as a final product for my fourth year course, Multivariate Analysis. How to create and maintain projects will be demonstrated along with students' products. 
 
ANNE MICHELE MILLAR, Mount Saint Vincent University
P-values: Love them - Hate them - Leave them. Implications for Statistical Education.  [PDF]
 
The vast majority of our introductory statistics text books teach significance tests as the definitive method for statistical inference. In light of the ban on p-values by some journals, and the ASA's recent statement on p-values, should we be updating this approach? Great strides have been made in statistical education, stressing the teaching of concepts rather than calculations, introducing students to real world data from the beginning of the course. Many of our students still leave the first course with very fuzzy ideas about inference. We rarely see non-frequentist approaches taught in our introductory courses. Is it time for change? 
 
LORI MURRAY, University of Western Ontario
Five Years of Blending  [PDF]
 
A blended course format is a combination of in-class and online learning. Although the unique format has been in use for several years, for many it is still a new concept. We have been using blended learning in an introductory statistics course at the University of Western Ontario since 2012. In this talk, I will give my experience as the instructor, highlighting some of the advantages and pitfalls that I have encountered along the way. 
 
NATHAN TABACK, University of Toronto
Students' Attitudes Towards Statistics in Online versus Flipped Classrooms  [PDF]
 
Technology has allowed instructors to experiment with different course delivery methods including flipped and fully online courses. In the Fall of 2015 at the University of Toronto, our large (N=1,413) introductory course was taught in both flipped and fully online formats, with some discipline-specific sections. To investigate if students' attitudes towards statistics differ depending on course delivery method, students were asked to complete pre and post course surveys (SATS-36) measuring attitudes towards statistics. We'll report on a comparison of attitude components between flipped and online sections, and discipline and non-discipline specific sections of the course, adjusted for baseline student covariates.