INSTITUTE FOR RESEARCH IN MATHEMATICAL SCIENCES
MATHED FORUM MEETING AGENDA
Mathematics and Equity
FEBRUARY 28 ,
2015 at 10 am-2 pm
Institute, 222 College Street, Toronto
10:00 AM - 10:15 AM Reports: OAME, OMCA, OCMA, CMESG, CMS, and other.
10:15 AM - 10:45 AM Iain Brodie (Toronto District School Board): REALLY
Big Ideas in Data Management - Quantitative and Qualitative Research Done
Abstract: Most big ideas in mathematics education revolve around
the doing of math. Nowhere is this more apparent than in the data management
and probability strand(s) where the big ideas are:
1. That students need to collect, organize, and display data.
2. That students should be able to analyze data using mean, median and mode.
3. That students should treat probability as being independent from data
until they are too old to change.
What if we developed really big ideas? Ones that centred around understanding
the relationships between mathematics and everything else in the world:
1. Data can be used to predict and inform.
2. Data can be used to reveal underlying patterns in the world.
3. If you really want to know the answer to something, collect a LOT of
4. The analysis of data absolutely involves probabilistic thinking.
This is the story of a class of young students who pursued the idea that
there are answers to real questions to be had by understanding and using
mathematics to discover underlying trends and patterns in our world.
Bio: Iain is a teacher. When asked what he teaches, he always responds
with who he teaches. "I don't teach math, I teach children. They need
to learn math." Treating his classroom as a laboratory, he and his
students try to probe and discover the limits of what children are able
to learn. They have not yet found that upper limit. Having spent the majority
of his career teaching in the primary grades, Iain is finally teaching intermediate
students where he has a wonderful group of teenagers who share his passion
for good writing, integrating the arts, and indulging their collective curiosity
about the patterns found in mathematics and science.
10:45 AM - 11:15 AM Egan Chernoff (University of Saskatchewan): Grand
Abstract: It is a popular notion that probability is counterintuitive.
Sir David Spiegelhalter perhaps said it best: "I often get asked why
people find probability so unintuitive and difficult. After years of research,
I have concluded it's because probability really is unintuitive and difficult."
As one might expect, this notion has been fully embraced by a small band
of researchers in the field of mathematics education; however, said research
(and results) are not well known. As such, the purpose of this session is
to provide a historical overview of the last 60 years of research investigating
our grand probabilistic delusions. We will, of course, address and (not
without debate) answer some of the perplexing probability problems found
in the research literature.
Bio: Egan's research, in general, is focused on creating, developing
and utilizing a variety of theories, frameworks, methods and models to investigate
the teaching and learning of probability and probabilistic thinking. In
particular, his research, currently, utilizes logical fallacies (e.g., fallacy
of composition, appeal to ignorance and others) and particular models from
the field of cognitive psychology (e.g., attribute substitution) to account
for prospective elementary, middle and high school math teachers' normatively
incorrect, inconsistent and sometimes inexplicable responses to a variety
of probabilistic tasks.
11:15 AM - 11:45 AM Georges Monette (York University): Bayesians vs?
Abstract: The famous statistician John Tukey (1915 - 2000) said
that he had despaired of seeing the controversy resolved in his lifetime.
Will it be in ours? Maybe we will come to see it as a fundamental duality
whose resolution is in its acceptance. This talk will explore the roots
of the controversy and some attempts to resolve it. Can we "bake the
Bayesian omelette without breaking the Bayesian egg"? The moral is
that you can't understand either approach without also understanding the
other. The implication for education is that teaching traditional hypothesis
testing and p-values without presenting their connections with Bayesian
inference can lead to serious errors in statistical thinking.
Bio: I completed my Ph.D. under the supervision of Donald A.S. Fraser
at the University of Toronto, who is one of the world's leading pathbreakers
exploring alternatives and resolutions to the controversy between Bayesian
and Frequentist inference. Although I've spent most of my career applying
statistics and exploring the visualization of statistical concepts, I enjoy
periodically revisiting the state of affairs between Bayesians and Frequentists.
11:45 AM - 12:15 PM Ayse Basar Bener (Ryerson University): Connecting
Big Data and Data Science to the Statistics Curriculum.
Abstract: Data science incorporates varying elements and builds
on techniques and theories from many fields, including math, statistics,
pattern recognition, uncertainty modeling, data warehousing, and high performance
computing with a goal of extracting meaning from data. It is a paradigm
shift around the data, and the scope of analytics is descriptive, predictive
and prescriptive. It is an interdisciplinary field that requires skills
in math, statistics, machine learning, and computer science, software engineering
and domain expertise. Statistics curriculum should focus on building fundamental
knowledge in statistics and probability and also expand into building computational
skills to derive models from data.
Bio: Dr. Ayse Basar Bener is a professor and the director of Data
Science Laboratory (DSL) in the Department of Mechanical and Industrial
Engineering, and director of Big Data in the Office of Provost and Vice
President Academic at Ryerson University. She is a faculty research fellow
of IBM Toronto Labs Centre for Advance Studies, and affiliate research scientist
in St. Michael's Hospital in Toronto. Her current research focus is big
data applications to tackle the problem of decision-making under uncertainty
by using machine learning methods and graph theory to analyze complex structures
in big data to build recommender systems and predictive models in health
care, software engineering, smart energy grid, and green software. She has
published more than 130 articles in journals and conferences. She is a member
of AAAI, INFORMS, AIS, and a senior member of IEEE.
12:15 PM - 1:00 PM LUNCH BREAK (Light refreshments provided)
1:00 PM - 2:00 PM Panel Discussion on big ideas in probability and
statistics education. After-lunch panel of morning speakers, Iain Brodie,
Egan Chernoff, Georges Monette, and Ayse Basar Bener, will be joined by Skype
to Jordan Ellenberg (University of Wisconsin-Madison) to further discuss connections
of statistics and probability to curriculum.
Bio of Dr. Ellenberg (http://www.jordanellenberg.com/about/): Jordan Stuart
Ellenberg is a Professor of Mathematics at the University of Wisconsin-Madison.
He writes the "Do the Math" column in the online journal Slate and
has authored a non-fiction book "How Not to Be Wrong" which is full
of examples from the world of probability and statistics.
2:00 PM ADJOURNMENT
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