GAISE

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GAISE
Guidelines for Assessment and Instruction in
Statistics Education COLLEGE REPORT
Robin Lock Jack and Sylvia Burry Professor of
Statistics St. Lawrence University rlock_at_stlawu.ed
u
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GAISE College Group
Joan Garfield Univ. of Minnesota (Chair) Martha
Aliaga ASA George Cobb Mt. Holyoke
College Carolyn Cuff Westminster College Rob
Gould UCLA Robin Lock St. Lawrence University Tom
Moore Grinnell College Allan Rossman Cal Poly San
Luis Obispo Bob Stephenson Iowa State Jessica
Utts UC Davis Paul Velleman Cornell
University Jeff Witmer Oberlin College
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The Many Flavors of Introductory Statistics
Consumer
Producer
General
Discipline-specific
Large lecture
Small class
Year
Block
Semester
Quarter
H.S. (AP)
University
Two year college
Four year college
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Challenge in Writing Guidelines
Give sufficient structure to provide real
guidance to instructors.
Allow sufficient generality to include good
practices in the many flavors.
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Starting point Cobb Report (1992)
Report from discussions of the Focus Group on
Statistics Education in Heeding the Call for
Change

• Three Recommendations
• Emphasize statistical thinking
• More data and concepts, less theory and fewer
  recipes
• Foster active learning

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Statistically Educated Students

• Should believe and understand why

• Data beat anecdotes.
• Variability is natural and is also predictable
  and quantifiable.
• Random sampling allows results to be extended to
  the population.
• Random assignment in experiments allows cause and
  effect conclusions.

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Statistically Educated Students

• Should believe and understand why

• Association is not causation.
• Statistical significance does not necessarily
  imply practical significance.
• Finding no statistical significance in a small
  sample does not necessarily mean there is no
  difference/relationship in the population.

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Statistically Educated Students

• Should recognize

• Common sources of bias in surveys and
  experiments.
• How to determine the population (if any) to which
  inference results may be extended.
• How to determine when a cause and effect
  inference can be drawn.
• That words such as normal, random and
  correlation have specific statistical meanings.

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Statistically Educated Students

• Should understand the process through which
  statistics works to answer questions. How to

• Obtain or generate data.
• Graph data as an initial step in analysis.
• Interpret numerical summaries and graphical
  displays (answer questions / check conditions).
• Make appropriate use of statistical inference.
• Communicate results of a statistical analysis.

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Statistically Educated Students

• Should understand the basic ideas of statistical
  inference, including the concepts of

• Sampling distribution and how it applies to
  making inferences from samples.
• Statistical significance, including significance
  level and p-values.
• Confidence interval, including the confidence
  level and margin of error.

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Statistically Educated Students

• Should know

• How to interpret statistical results in context.
• How to read and critique news stories and journal
  articles that include statistical information.
• When to call for help from an experienced
  statistician.

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Guideline 1
Emphasize statistical literacy and develop
statistical thinking.
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Statistical Literacy and Thinking
Statistical literacy understanding the basic
language and fundamental ideas of statistics.
Statistical thinking the processes that
statisticians use when approaching or solving
practical problems.
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Suggestions for Teachers

• Model statistical thinking for students.
• Have students practice statistical thinking (e.g.
  open-ended problems and projects).
• Let students practice with choosing appropriate
  questions and techniques.

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Guideline 2
Use real data.
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Levels of Reality
Real Data that were actually collected or
generated to answer some question(s).
Realistic Hypothetical data with a context that
illustrate a specific point.
Naked Numbers with no context (and thus no
interest).
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Suggestions for Teachers

• Search for raw data from textbooks, software
  packages, web data repositories.
• Use summary data from textbooks, articles, and
  websites with poll/survey results.
• Get data from class activities and simulations.
• Make larger data sets available electronically.
  Practice data entry on small data sets.
• Return to a rich data set at various points in
  the course.
• Use data with students to answer interesting
  questions and generate new questions.

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Guideline 3
Stress conceptual understanding rather than mere
knowledge of procedures.
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Concepts vs. Procedures
Many (most?) introductory courses contain too
much material.
If students dont understand concepts, theres
little value in knowing procedures.
If students do understand concepts, specific new
procedures are easy to learn.
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Suggestions for Teachers

• Primary goal is not to cover methods, but to
  discover concepts.
• Focus on understanding of key concepts,
  illustrated by a few techniques, rather than a
  multitude of techniques with minimal focus on
  underlying ideas.
• Pare down content to focus on core ideas in more
  depth.
• Use technology for routine computations, use
  formulas that enhance understanding.

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Guideline 4
Foster active learning in the classroom.
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Types of Active Learning
Group or individual problem solving,
exploratory activities and discussion
Lab activities physical and computer-based
Demonstrations based on live results from
students or software
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Suggestions for Teachers

• Ground activities in the context of real
  problems.
• Intermix lectures with activities and
  discussions.
• Precede computer simulations with physical
  explorations.
• Collect data from students.
• Encourage predictions from students anticipating
  statistical results.
• Plan sufficient time to do and wrap up the
  activity.
• Provide lots of feedback and assessment.

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Guideline 5
Use technology for developing concepts and
analyzing data.
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Types of Technology
Graphing calculators Traditional statistical
packages Conceptual statistical
software Educational support Applets Spreadsheets
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Suggestions for Teachers

• Access large real data sets.
• Automate calculations.
• Generate and modify appropriate statistical
  graphics.
• Perform simulations to illustrate abstract
  concepts.
• Explore what happens if... scenarios.
• Create reports
• Consider
• Ease of data entry, ability to import data
• Interactive capabilities
• Dynamic linking between data, graphs, numerics
• Ease of use and availability

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Guideline 6
Use assessments to improve and evaluate student
learning.
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Types of Assessment
Homework Quizzes and exams Projects Activities Pre
sentations Lab reports Minute papers Article
critiques Class discussion/participation
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Suggestions for Teachers

• Integrate assessment as an essential (and
  current) component of the course.
• Use a variety of assessment methods.
• Assess statistical literacy (e.g. by interpreting
  or critiquing articles and graphs in the media).
• Assess statistical thinking (e.g. by doing
  student projects or open-ended investigative
  tasks).
• For large classes
• Use group projects instead of individual
• Use peer review of projects
• Use multiple choice items that focus on choosing
  interpretations or appropriate statistical
  approaches.

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Six Recommendations

1. Emphasize statistical literacy and develop
   statistical thinking
2. Use real data
3. Stress conceptual understanding rather than mere
   knowledge of procedures
4. Foster active learning
5. Use technology to develop conceptual
   understanding and analyze data
6. Use assessments to improve and evaluate learning

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Making It Happen
Evolution through small steps

• Find/develop a case study of statistical interest
• Find a new real data set
• Delete a topic from the list you currently cover
• Have students do a small project or new activity
• Integrate a neat applet into a lecture
• Try some new types of quiz/exam questions