An International Journal on the Teaching and Learning of
JSE Volume 21, Number
The 1993 inaugural issue of the Journal of Statistics Education (JSE)
published an article about a small conference for Principal Investigators
(PIs) and co-PIs of twelve projects in statistics education funded by the National
Science Foundation (NSF). This twenty-year retrospective (1) offers
some personal memories related to the founding of JSE, (2) offers some
thoughts about the legacies of the twelve funded projects, (3) sets out a
sense of how the conference themes have fared over the last twenty years, and
(4) indicates what this might suggest about the future of our profession. In conclusion, I argue (briefly) that at
this moment in its history, statistics education faces the biggest
opportunity and challenge of its last 40 years.
Key Words: Statistics education; NSF; Assessment;
Activities; Simulation; TQM; Randomization; Bayes.
As I look back over 20 years, I see how collaboration has become the
cornerstone of my beliefs about teaching and learning and about my own
professional development and productivity. While I once viewed cooperative
learning as solely a pedagogical method, I now view collaborative learning as
my ongoing way of life. It is a defining characteristic of all my work, whether
teaching, creating curriculum and assessment, conducting research, and
writing. Not only does collaboration make my work more enjoyable and
sociable, it makes me think harder, defend or revise my ideas, and become
more creative in brainstorming or problem solving. As a result, collaboration
greatly improves the quality of every product I help to create. Standing
firmly on my soapbox, I begin this paper with a brief look backwards at my
first exposure to cooperative learning and my early use of this method as a
teacher. Then, I describe my journey as a
practitioner of cooperative learning, teaching, and scholarship.
Key Words: Cooperative learning; Statistics Education;
In this article, we present a study to test whether neutral observers
perceive a resemblance between a parent and a child. We demonstrate the
general approach for two separate parent/child pairs using survey data
collected from introductory statistics students serving as neutral observers.
We then present ideas for incorporating the study design process, data
collection, and analysis into different statistics courses from introductory
to graduate level.
Key Words: Classroom example; Survey data; Tests for
binomial probabilities; Tests for multinomial probabilities.
This paper describes a flexible paradigm for creating an electronic “Core
Concepts Plus” textbook (CCP-text) for a course in Introductory Business and
Economic Statistics (IBES). In general terms, “core concepts” constitute the
intersection of IBES course material taught by all IBES professors at the
author’s university. The “Plus” component of the paradigm is embodied in
self-written, professor-specific sections that are combined with the
core-concepts material to produce professor-specific versions of the IBES
CCP-text. The paradigm entails a vertically integrated text creation process
with two primary aspects: first, non-IBES faculty members that ultimately
receive former IBES students are included in the text-writing process;
second, some former IBES students (e.g., tutors) are included in the
text-writing process. Student learning experiences with the CCP-text are
summarized with survey results; the learning outcomes are assessed using
three semesters of pre- and post-test data; and a textbook cost study is used
to contextualize the savings to students. The CCP-text appears to be
efficacious in all three of these areas. Recommendations concerning how and
where the paradigm might be replicated are also presented.
Key Words: Textbook Costs; Textbook Rental Programs;
with Statistics Educators
David Moore is Professor Emeritus of Statistics at Purdue University. He
served as the first President of the International Association for
Statistical Education (IASE) from 1993-1995 and as President of the American
Statistical Association (ASA) in 1998. He is a Fellow of the ASA and of the
IMS and was awarded the ASA’s Founders Award in 2001. He has written several
influential, widely used textbooks for introductory statistics. This
interview took place via email on May 3, 2013 through June 12, 2013.
I located 27 articles that have been published from January through July
2013 that pertained to statistics education. In this column, I highlight a
few of these articles that represent a variety of different journals that
include statistics education in their focus. I also provide information about
the journal and a link to the journal's website so that abstracts of
additional articles may be
accessed and viewed.
Many new things are going on with CAUSEweb and
MERLOT, and the purpose of this short article is to share some important
updates with the greater statistics education community.
Data Sets and Stories
This dataset contains the results of a quasi-experiment, testing Karl
Pearson's "drunkard's walk" analogy for an abstract random walk.
Inspired by the alternate hypothesis that drunkards stumble to the side of
their dominant hand, it includes data on intoxicated test subjects walking a
10' line. Variables include: the direction to which subjects first
stumbled, the side of the line on which they ended up, and a record of
subjects' dominant hand. In addition to enhancing a discussion of the
random walk itself, instructors may find this dataset useful for teaching
tests of sample proportions.
Key Words: Data Set and Story; Random
Walk; Probability; Test of Sample Proportion.
As a part of an opening course survey, data on eye color and gender were
collected from students enrolled in an introductory statistics course at a
large university over a recent four year period. Biologically, eye color and
gender are independent traits. However, in the data collected from our
students, there is a statistically significant dependence between the two
variables. In this article, we present two ideas for using this data set in
the classroom, and explore the potential reasons for the dependence between
the two variables in the population of our students.
Key Words: Student data; Contingency table; Independence;