Transcript Making Statistics Learning Fun! (that’s not a factorial sign)

‘til the webinar starts, here are some
points to ponder….
“Using our discipline to enhance human welfare”
-- 2005 JSM theme & one of the six ASA Mission Statement parts
founders have “demonstrated early a commitment to statistical science in
service to public welfare” and “the examination of social issues such as the
homeless and the poor” is one vital area to which members now apply their
expertise – ASA’s description of its Vision, Mission, History
“[Statistics’] real contribution to society is primarily moral, not technical” –
Vardeman & Morris, Feb. 2003 The American Statistician
“Just as the teacher of history or literature would not avoid moral issues
in her lessons, the statistics teacher likewise should not avoid them.”
-- Mary Rouncefield, July 1995 J. of Statistics Education
“It no longer suffices to know how things are constituted: we need to seek
how things should be constituted so that this world of ours may present less
suffering and destitution.” –19th century French statistician Eugene Burét
“Statistics are human beings with the tears wiped off.” –
Paul Brodeur, Outrageous Misconduct
“Justice, justice shall you pursue” – Deuteronomy 16:20
“I arise in the morning torn between a desire to improve the world
and a desire to enjoy the world. This makes it hard to plan the day.”
– 20th century American author E. B. White
Logistics Reminder
Until the “discussion” part, you’ll be “muted”
(for optimal audio quality), but you can
relay questions via your “chat window.”
This webinar will be archived
(www.causeweb.org/webinar/) & its main
associated article is also accessible online
(www.amstat.org/publications/jse/v15n1/).
Welcome to our July 10, 2007 CAUSE Webinar:
“Teaching Statistics
Using Social Justice Examples”
Drawing from (and expanding upon)
my article in J. of Statistics Education
(March 2007), we’ll discuss examples,
resources and pedagogy associated
with this meaningful way of engaging
students in the statistics classroom.
Dr. Larry Lesser, Assoc. Professor
University of Texas at El Paso
www.math.utep.edu/Faculty/lesser/
TSSJ = “Teaching Statistics
with Social Justice”
the teaching of statistics
with nontrivial inclusion
of examples related to SJ,
offering chances for students to reflect
upon the context of these examples
as they learn or apply
associated statistical content
(Lesser 2007)
OUTLINE
“Presentation” portion
• Background & Terms
• Examples & Resources
• Pedagogy & Implementation
“Discussion” portion
Feedback -- when prompted with a
feedback window at the end of the webinar
Quick Feedback #1
Click “RAISE HAND” if:
you read the JSE article
If you did read it,
this webinar will give you
some things beyond it.
If you haven’t read it,
it’s a recommended place to start:
it includes definitional issues, content examples,
some 150 references/resources,
and concise literature review of
the broader pedagogical and philosophical realms.
Quick Feedback #2
Now click “RAISE HAND” if:
you have classroom (or research)
experience with the topic of this webinar,
whether or not you read the JSE article
My Background/Trajectory
• 2000-01: created math/stat/ethics module
at AASU (published in Nov. 2004 JSE)
• 2002-04: full-time teaching at a HS where
students had 18-hr/year service requirement
and did many projects to support its mission to
“provide a lifelong foundation for…improving the
world in which we live”
• 2004: began teaching at UT-El Paso
Background (continued)
2005: JSM paper on SJ/ethics/service learning;
Gutstein’s work inspires me to write SJ paper for JSE
2006: chapter in critical pedagogy book;
a major speaker at 1st national college-level course
development workshop on Math and SJ;
2007: SJ paper appears in JSE (March);
1st national conference on Math & SJ (May);
MSJ2 workshop (June); CAUSE webinar (July)
a progression towards TSSJ
• “social justice” (SJ)
• “teaching for SJ”
• “teaching mathematics for SJ”
Searching
• Google: “SJ” – millions
“T for SJ” – tens of thousands
“TM for SJ” – hundreds
TSSJ – 0 (excluding this year’s
webinar & JSE paper)
• CAUSEWeb: “SJ” – 0
Viewing statistics as
“the grammar of SJ”?
(Lesser 2007)
Tools to identify group differences or patterns can
help people recognize, analyze or address
social inequalities
Calculating expected value of a “fair share” and
how much deviation might be viewed as
innocuous offers a benchmark to discussions
about what is “fair.”
An awareness of statistical pitfalls helps people
interpret or make appropriate depictions of
quantitative information.
a progression towards TSSJ
• “social justice” (SJ)
• “teaching for SJ”
• “teaching mathematics for SJ”
----------------------------------
• “teaching statistics for SJ”
• “teaching statistics with SJ”
SJ & stats: goodness of fit
“Using our discipline to enhance human welfare”
-- theme of 2005 JSM
and one of the six parts of the ASA Mission Statement
founders have “demonstrated early a commitment to
statistical science in service to public welfare” and “the
examination of social issues such as the homeless and
the poor” is one vital area to which members now
apply their expertise
– ASA’s description of its Vision, Mission, History
Another reason: motivation!
“few are drawn to statistics by immediate practical need....
today's teachers face challenges of motivation…
substantially greater than those of a half-century ago.”
(GAISE; ASA 2005)
Emerging evidence suggests that TSSJ examples support
student engagement (Gutstein 2003, Makar 2004, Lesser 2006,
Weaver 2007).
“[S]tudents can ask real questions about real-life situations.
These in turn raise ethical and moral questions, which
motivate students’ learning, making the subject matter
more relevant and interesting.” (Rouncefield 1995)
F. De Maio (2007), p. 34:
“In the minds of many students, statistical
analysis bears little relevance to the
important issues of the day….[s]tatistical
tools which can be used to examine the
distribution of income (e.g., the Gini
coefficient), the progressivity of tax
structures (e.g., the Kakwani index), the
nature of poverty (e.g., the Sen index), or
health inequities (e.g., illness
concentration curves) receive little, if any,
attention in most introductory courses...”
FYI: Gini coefficient
(Williams & Joseph 1993, p.191)
Lesser (2007): TSSJ supports
GAISE, with one addition:
The “Goals” section in College GAISE (ASA 2005)
describes statistics audiences as some blend of
producers and consumers.
A TSSJ orientation would include viewing
audiences as democratic citizens/participants,
and include the additional goals of increasing
student awareness of SJ issues (e.g., through
datasets examined) and increasing ability to
evaluate or even promote social change.
in terms of questions asked….
(Lesser 2007)
critical components from Utts (2005): research/funding source,
researchers who had contact with the participants,
individuals studied and how selected,
nature of measurements made and the setting,
other differences in groups being compared,
size of any claimed effects
The TSSJ list, however, might also include questions such as:
•
From this particular collection, representation, or publication of
data, who appears to benefit and who appears to suffer?
Whose values may be implicitly represented or excluded?
•
Does this data or exploration offer a vehicle or tool that could be
used to help understand or improve social conditions in our
present world?
Areas of potential discrimination
identified by Pollack & Wunderlich
(table in June 2005 Amstat News is reproduced in Lesser 2007)
Labor markets: hiring, interviewing, wages, evaluation,
promotion, layoffs, rehiring
Education: college acceptance, financial aid, track
placement, evaluation, special ed. placement, promotion
Housing: steering, mortgage redlining, loan pricing, resale
value; wealth accumulation
Criminal justice: police behaviors, arrests, police
treatment, legal representation, parole, sentencing
Health care: access, insurance, quality, price, referrals
also, bring in mass media articles!
examples in JSE paper include:
• Operational definitions (unemployment,
poverty, homelessness, ethnicity, diversity,
economic progress, etc.)
• Relation of $ to education, equity
• Expected value (insurance, lottery)
• Inference (racial profiling, death penalty,
drug testing, jury discrimination, etc.)
vignette (not in JSE paper)
from a recent textbook
Statistics Concepts and Controversies, 6th ed.
Moore & Notz (2006, p. 232)
“Income inequality in the US is
greater than in other
developed nations and has
been increasing. Are these
numbers cause for concern?
And do they accurately reflect
the disparity between the
wealthy and the poor? For
example, as people get older
their income increases.
Perhaps these numbers only
reflect the disparity between
younger and older wage
earners. What do you think?”
importance of discussing the ways
that statistics are socially constructed
Milo Schield (2007)
Marilyn Frankenstein (2005)
Joel Best (2001, 2002)
a Lesser (2007) recommendation for TSSJ:
“…awareness that a comparison
can be an artifact of how data is aggregated
is listed by the National Council on
Education and the Disciplines (2001)
as essential for democracy,
and would therefore be important to include”
example from Lesser(2001):
some average SJ ideas
• average class size (Lesser 2007)
• Gau (2005): HS teachers use averages to
find: ‘living wage’ > minimum wage
• contextualized “leveling” interpretation:
another kind of connection to SJ:
students’ sense of fairness
Shaughnessy (2003) & Jacobs (1997) found
HS/MS and ES students, respectively,
do not value randomization in surveys and
wanted a survey to be “fair” by having:
(1) all students be able to choose whether to
be in the survey
OR
(2) all subgroups (e.g., a boy and a girl from
each class) represented
Pedagogy & Implementation
(Lesser 2007)
There are many levels of TSSJ -- analogous to the 6
pedagogical levels Anderson & Sungur (2002) give for a
“community awareness component.”
At the most basic level, students can be given
predetermined datasets and asked to use predetermined
statistical methods to analyze them.
At further levels, students have more and more opportunity
to discuss the context, choose the SJ topic(s), and find
(or even collect) the data.
Pick your level based on backgrounds, interests, available
time, and the balance of goals for the course.
Learn from instructors incorporating service learning, etc.
more tips on easing into this...
(Lesser 2007)
• Make TSSJ one of many project options
• Note precedent & examples in mainstream
sources & in mission statements, and
(emerging) supporting evidence
• Use variety of data from mainstream sources
• Use understated terms (e.g., inequality vs.
inequity; underrepresented vs. oppressed)
• Consider how SJ can be as “neutral”
as a “traditional” curriculum
A TSSJ ‘Wish List’
• More research on benefits and
overcoming implementation challenges
• More resources and examples compiled
on a webpage...
or into a TSSJ intro course
textbook or supplement?
Time for DISCUSSION!
We now “un-mute” you
& invite you to:
• Discuss what I’ve shared
• Share your own ideas
and/or experiences of TSSJ
WRAPPING UP
JSE: www.amstat.org/publications/jse/
Webinar: www.causeweb.org/webinar/
(thanks to CAUSE for supporting this cause!)
Me: www.math.utep.edu/Faculty/lesser/
(please relay SJ data/ideas you have!)
------------------------------------------------------give feedback on this webinar using the online
window that opens when this webinar ends