11 Experience Early, Logic Later Larry Weldon Simon Fraser University Canada Larry Weldon Simon Fraser University Canada E 2 L 2

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Experience Early, Logic LaterExperience Early, Logic Later

Larry Weldon

Simon Fraser University

Canada

Larry Weldon

Simon Fraser University

Canada

E2 L2

22

Eat Food,

Not Too Much,

Mostly Plants

33

Analyze Data,

Construct Tools,

Generalize Concepts

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Undergrad Stats Ed ProposalUndergrad Stats Ed Proposal

Immersion in Data Analysis, with Guidance and Feedback,

will promote a more Useful Knowledge of Statistics

than a Logical Sequence of Technique Presentations

Immersion in Data Analysis, with Guidance and Feedback,

will promote a more Useful Knowledge of Statistics

than a Logical Sequence of Technique Presentations

A return to Apprenticeship Education, but making use of modern resources

(statistical software and electronic communication.)

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OutlineOutline

Pedagogy Reform

Obstacles to Implementation

Experience & Logic

Technology Support

New Role of Textbooks

New Role of Math

Simulation Metaphor

Implications for Stat Ed

Pedagogy Reform


Experience & Logic

Technology Support


New Role of Math

Simulation Metaphor


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OutlineOutline

Pedagogy Reform


Experience & Logic

Technology Support


New Role of Math

Simulation Metaphor


Pedagogy Reform


Experience & Logic

Technology Support


New Role of Math

Simulation Metaphor


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Good Ideas from ICOTS2 (1986)Good Ideas from ICOTS2 (1986)

"Using the practical model [of teaching statistics] means aiming to teach statistics by addressing such problems in contexts in which they arise. At present this model is not widely used." (Taffe 1986)

"Using the practical model [of teaching statistics] means aiming to teach statistics by addressing such problems in contexts in which they arise. At present this model is not widely used." (Taffe 1986)

“Experiential Education”

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Good Ideas from ICOTS2 (1986)Good Ideas from ICOTS2 (1986)

"The interplay between questions, answers and statistics … if students have a good appreciation of this interplay, they will have learned some statistical thinking, not just some statistical methods." (Speed 1986)

"The interplay between questions, answers and statistics … if students have a good appreciation of this interplay, they will have learned some statistical thinking, not just some statistical methods." (Speed 1986)

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Quotes from ICOTS2 (1986)Quotes from ICOTS2 (1986)

… while most statistics professors like statistics for its own sake, most students become interested in statistics mainly if the subject promises to do useful things for them. …. Only then do most students seem to become sufficiently intrigued with statistics to want to learn about statistical theory." (Roberts 1986)

… while most statistics professors like statistics for its own sake, most students become interested in statistics mainly if the subject promises to do useful things for them. …. Only then do most students seem to become sufficiently intrigued with statistics to want to learn about statistical theory." (Roberts 1986)

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Quotes from ICOTS2 (1986)Quotes from ICOTS2 (1986)

"The development of statistical skills needs what is no longer feasible, and that is a great deal of one-to-one student-faculty interaction ..." (Zidek 1986)

"The development of statistical skills needs what is no longer feasible, and that is a great deal of one-to-one student-faculty interaction ..." (Zidek 1986)

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Implications from ICOTS2Implications from ICOTS2

Use Context to teach theory (Taffe) Whole process of data-based Q&A

(Speed) Abstractions do not motivate

(Roberts) Teacher-student interaction needed

for useful learning of statistics (Zidek)

Use Context to teach theory (Taffe) Whole process of data-based Q&A

(Speed) Abstractions do not motivate

(Roberts) Teacher-student interaction needed

for useful learning of statistics (Zidek)

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Status in 1996Status in 1996

Hands-on activities Working in small groups Frequent and rapid feedback Communicating results Explaining reasoning Computer simulations Open questions real settings Learning to work co-operatively

Hands-on activities Working in small groups Frequent and rapid feedback Communicating results Explaining reasoning Computer simulations Open questions real settings Learning to work co-operatively

In discussing what helps students learn, [David Moore] listed the following:

Phillips - ICME 8

How to incorporate

all these features???

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OutlineOutline

Pedagogy Reform


Experience & Logic

Technology Support


New Role of Math

Simulation Metaphor


Pedagogy Reform


Experience & Logic

Technology Support


New Role of Math

Simulation Metaphor


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Obstacles to ImplementationObstacles to Implementation

Domination of Math Culture in Stat EdMath eliminates context, stats incorporates it

Math appeals to minority, stats needed by many

Confusion of Math-Stat = Stat Theory

Administrative Control of Stats by Math Dept

Academic Disincentives to Curriculum Change

Publishers Reluctance to Innovate

Stat Theory:

Strategies for Information Extraction From Data With Context

Promote Data Analysis Courses - Instructor Is Guide

Find a new role for existing textbooks - evolve

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Context vs AbstractionContext vs Abstraction

Which is more interesting to students? Example of a new item of stat theory

“Zipf’s Law” chosen for obscurity!

Which is more interesting to students? Example of a new item of stat theory

“Zipf’s Law” chosen for obscurity!

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“Theory”: Zipf’s Law“Theory”: Zipf’s Law

An empirical findingof relative sizes of things

Frequency * rank = constant

An empirical findingof relative sizes of things

Frequency * rank = constant

Total Freq = 300Constant=100

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Population*Rank = Constant?Population*Rank = Constant?

QuickTime™ and aTIFF (Uncompressed) decompressor

are needed to see this picture.



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1919






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Suggests Follow-upSuggests Follow-up

1. Why is Australia different?

2. To which kinds of counts does Zipf’s Law apply?

Point is:

Contextual Introduction conveys understanding of theory

wheras

Theory alone conveys ‘theory’ but not understanding

(Even with confirming example)

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Math Culture inhibits the joy of data analysis in learning statsMath Culture inhibits the joy of data analysis in learning stats

And so retards pedagogic reform in statistics

And so retards pedagogic reform in statistics

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OutlineOutline

Pedagogy Reform


Experience & Logic

Technology Support


New Role of Math

Simulation Metaphor


Pedagogy Reform


Experience & Logic

Technology Support


New Role of Math

Simulation Metaphor


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Data Analysis -> Stat TheoryData Analysis -> Stat Theory

Using applications to illustrate theory

(is common approach)

Using applications to construct theory

(is proposal here)

Using applications to illustrate theory

(is common approach)

Using applications to construct theory

(is proposal here)

Best Approach for undergraduate stats?

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Sports League - FootballSuccess = Quality or Luck?

Sports League - FootballSuccess = Quality or Luck?

2007 AFL LADDERTEAM Played WinDraw Loss Points FOR Points Against Ratio PointsGeelong 22 18 - 4 2542 1664 153 72Port Adelaide 22 15 - 7 2314 2038 114 60West Coast Eagles 22 15 - 7 2162 1935 112 60Kangaroos 22 14 - 8 2183 1998 109 56Hawthorn 22 13 - 9 2097 1855 113 52Collingwood 22 13 - 9 2011 1992 101 52Sydney Swans 22 12 1 9 2031 1698 120 50Adelaide 22 12 - 10 1881 1712 110 48St Kilda 22 11 1 10 1874 1941 97 46Brisbane Lions 22 9 2 11 1986 1885 105 40Fremantle 22 10 - 12 2254 2198 103 40Essendon 22 10 - 12 2184 2394 91 40Western Bulldogs 22 9 1 12 2111 2469 86 38Melbourne 22 5 - 17 1890 2418 78 20Carlton 22 4 - 18 2167 2911 74 16Richmond 22 3 1 18 1958 2537 77 14

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Leading QuestionsLeading Questions

Does Team Performance (as represented by league points) reflect Team Quality (as represented by the probability of winning a game)?

What would happen if every match 50-50?

Does Team Performance (as represented by league points) reflect Team Quality (as represented by the probability of winning a game)?

What would happen if every match 50-50?

“Equal Quality” Teams

Coin Toss (or computer) simulation ….

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Stat Theory?Stat Theory?

Understanding of “illusions of randomness” Opportunity for Hypothesis Test (via

simulation) Need for measures of variability ….(more in paper)

Understanding of “illusions of randomness” Opportunity for Hypothesis Test (via

simulation) Need for measures of variability ….(more in paper)

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Arms and Hands ExerciseArms and Hands Exercise

Ways to cross arms and to fold hands

(MacGillivray (2007)) Related? Related to Gender?

Ways to cross arms and to fold hands

(MacGillivray (2007)) Related? Related to Gender?

Theory Learned?Formulation of Data-Based QuestionSummary of Categorical Variable RelationshipsIllusions of Randomness…. (more)

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Examples of Experiential Learning Courses (SFU)Examples of Experiential Learning Courses (SFU)

STAT 100 - Statistics Appreciation Course– Survival analysis, Randomized Response, …

STAT 300 - Statistics Communication – Verbal explanations of stat theory and practice– Oral presentation of summary of official data

STAT 400 - Data Analysis– Data exploration by graphics and simulation– Comparison of parametric and non-par methods– Rescue of (almost) hopeless cases

STAT 100 - Statistics Appreciation Course– Survival analysis, Randomized Response, …

STAT 300 - Statistics Communication – Verbal explanations of stat theory and practice– Oral presentation of summary of official data

STAT 400 - Data Analysis– Data exploration by graphics and simulation– Comparison of parametric and non-par methods– Rescue of (almost) hopeless cases

More details at www.stat.sfu.ca/~weldon

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Experiential learning has potential to – Motivate student inquiry into stat theory

at all undergraduate levels– Encourage authentic learning of stat theory

Experiential learning has potential to – Motivate student inquiry into stat theory

at all undergraduate levels– Encourage authentic learning of stat theory

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Objections to Experiential Learning

Objections to Experiential Learning

1. Chaotic collection of techniques

(No general framework for applications) 2. Lack of complete coverage of basics

1. Chaotic collection of techniques

(No general framework for applications) 2. Lack of complete coverage of basics

Some New Technologies can help

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OutlineOutline

Pedagogy Reform


Experience & Logic

Technology Support


New Role of Math

Simulation Metaphor


Pedagogy Reform


Experience & Logic

Technology Support


New Role of Math

Simulation Metaphor


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Rodney CarrRodney CarrDeakin U

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Other Technology AidsOther Technology Aids

Wessa(2007) Reproducible Computations– Allows program use without program skill– Tracks use, enables instructor oversight– Encourages useful instructor-student interaction

Stirling(2002) CAST - Computer Assisted Statistics Teaching. – Electronic textbook – Includes student-modifiable simulations

Wessa(2007) Reproducible Computations– Allows program use without program skill– Tracks use, enables instructor oversight– Encourages useful instructor-student interaction

Stirling(2002) CAST - Computer Assisted Statistics Teaching. – Electronic textbook – Includes student-modifiable simulations

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OutlineOutline

Pedagogy Reform


Experience & Logic

Technology Support


New Role of Math

Simulation Metaphor


Pedagogy Reform


Experience & Logic

Technology Support


New Role of Math

Simulation Metaphor


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Textbook as ReferenceTextbook as Reference

New instructors, or instructors new to stats,tend to use textbook for lesson sequence

Less secure, but more interesting, to takeexperiential data analysis approach,and use text as reference support.

Electronic textbooks particularly useful here

New instructors, or instructors new to stats,tend to use textbook for lesson sequence

Less secure, but more interesting, to takeexperiential data analysis approach,and use text as reference support.

Electronic textbooks particularly useful here

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OutlineOutline

Pedagogy Reform


Experience & Logic

Technology Support


New Role of Math

Simulation Metaphor


Pedagogy Reform


Experience & Logic

Technology Support


New Role of Math

Simulation Metaphor


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Teaching Future PractitionersTeaching Future Practitioners

"A very limited view of statistics is that it is practiced by statisticians. … The wide view has far greater promise … ” Cleveland (1993)

"A very limited view of statistics is that it is practiced by statisticians. … The wide view has far greater promise … ” Cleveland (1993)

Math-Stat Not the Target for Undergraduates

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Math as an “simplifier”Math as an “simplifier”

Identify Common Approaches (e.g. regression, residual plots, conditioning, …)

Compare and Contrast Methods (e.g. hypoth tests vs CIs, parametric vs non-parametric, …)

Discuss role of models (simpler than reality, simulation role, independence, …)

… Anything that clarifies and reduces ambiguity

Identify Common Approaches (e.g. regression, residual plots, conditioning, …)

Compare and Contrast Methods (e.g. hypoth tests vs CIs, parametric vs non-parametric, …)

Discuss role of models (simpler than reality, simulation role, independence, …)

… Anything that clarifies and reduces ambiguity

“Logic Later”

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OutlineOutline

Pedagogy Reform


Experience & Logic

Technology Support


New Role of Math

Simulation Metaphor


Pedagogy Reform


Experience & Logic

Technology Support


New Role of Math

Simulation Metaphor


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Statistics for the PractitionerStatistics for the Practitioner Experiential Immersion provides

Authentic Target Simulation Metaphor:

Experiential Immersion provides Authentic Target

Simulation Metaphor:

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The aggregate of guided data analysis experiences

is what practitioners actually need to learn

Experiential learning is Authentic Learning

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OutlineOutline

Pedagogy Reform


Experience & Logic

Technology Support


New Role of Math

Simulation Metaphor


Pedagogy Reform


Experience & Logic

Technology Support


New Role of Math

Simulation Metaphor


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Teaching vs LearningTeaching vs Learning

Teachers can encourage authentic learning

Difficult to arrange in conservative depts Difficult to do with large classes Difficult for teachers without practical exp’ce

Nevertheless, a worthwhile goal.

Teachers can encourage authentic learning

Difficult to arrange in conservative depts Difficult to do with large classes Difficult for teachers without practical exp’ce

Nevertheless, a worthwhile goal.

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Analyze Data,

Construct Tools,

Generalize Concepts

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Thank you.Thank you.

Follow-up ([email protected])

www.stat.sfu.ca/~weldon

Follow-up ([email protected])

www.stat.sfu.ca/~weldon

The End

mailto:[email protected]

mailto:[email protected]

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Gasoline ConsumptionGasoline Consumption

Each Fill - record kms and litres of fuel used

Smooth--->SeasonalPattern

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Another Example:Theory of Smoothing

Another Example:Theory of Smoothing

Smoothing amplifies signal but introduces bias

by cutting off peaks and valleys

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Illustration of EffectIllustration of Effect











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Intro to smoothing with context …

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Suggests follow-upSuggests follow-up

1. How do you choose the amount of smoothing to produce useful information?

2. Why does a seasonal pattern occur?

1. How do you choose the amount of smoothing to produce useful information?

2. Why does a seasonal pattern occur?

Again, Point is …

New theory is best introduced through data exploration.

Documents

11 Experience Early, Logic Later Larry Weldon Simon Fraser University Canada Larry Weldon Simon Fraser University Canada E 2 L 2