g. p. richardson aaas stem meeting, nov 9-10, 2011 1 rockefeller college of public affairs and...

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G. P. Richardson AAAS STEM Meeting, Nov 9-10, 2011 1 Rockefeller College of Public Rockefeller College of Public Affairs and Policy Affairs and Policy University at Albany University at Albany Fixed and Sliding Goals in Education? A tiny example of a famous systems insight, applied to education

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G. P. RichardsonAAAS STEM Meeting, Nov 9-10, 2011

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Rockefeller College of Public Affairs and PolicyRockefeller College of Public Affairs and PolicyUniversity at AlbanyUniversity at Albany

Fixed and Sliding Goals in Education?

A tiny example of a famous systems insight, applied to education

G. P. RichardsonAAAS STEM Meeting, Nov 9-10, 2011

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Rockefeller College of Public Affairs and PolicyRockefeller College of Public Affairs and PolicyUniversity at AlbanyUniversity at Albany

Striving to Reach an Achievement Goal

G. P. RichardsonAAAS STEM Meeting, Nov 9-10, 2011

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Rockefeller College of Public Affairs and PolicyRockefeller College of Public Affairs and PolicyUniversity at AlbanyUniversity at Albany

Striving to Reach an Achievement Goal

G. P. RichardsonAAAS STEM Meeting, Nov 9-10, 2011

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Rockefeller College of Public Affairs and PolicyRockefeller College of Public Affairs and PolicyUniversity at AlbanyUniversity at Albany

Striving to Reach an Achievement Goal

G. P. RichardsonAAAS STEM Meeting, Nov 9-10, 2011

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Rockefeller College of Public Affairs and PolicyRockefeller College of Public Affairs and PolicyUniversity at AlbanyUniversity at Albany

Striving to Reach an Achievement Goal

G. P. RichardsonAAAS STEM Meeting, Nov 9-10, 2011

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Rockefeller College of Public Affairs and PolicyRockefeller College of Public Affairs and PolicyUniversity at AlbanyUniversity at Albany

Striving (But Falling a Bit Short)

G. P. RichardsonAAAS STEM Meeting, Nov 9-10, 2011

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Rockefeller College of Public Affairs and PolicyRockefeller College of Public Affairs and PolicyUniversity at AlbanyUniversity at Albany

Where Does the Goal Come From?

G. P. RichardsonAAAS STEM Meeting, Nov 9-10, 2011

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Rockefeller College of Public Affairs and PolicyRockefeller College of Public Affairs and PolicyUniversity at AlbanyUniversity at Albany

Where Does the Goal Come From?

G. P. RichardsonAAAS STEM Meeting, Nov 9-10, 2011

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Rockefeller College of Public Affairs and PolicyRockefeller College of Public Affairs and PolicyUniversity at AlbanyUniversity at Albany

A Flexible Goal Can Slide

G. P. RichardsonAAAS STEM Meeting, Nov 9-10, 2011

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Rockefeller College of Public Affairs and PolicyRockefeller College of Public Affairs and PolicyUniversity at AlbanyUniversity at Albany

Sliding Goals in Sophistication of School Texts?School Texts Have Gotten Simpler Over Time

• The most difficult readers were generally published before 1918. By modern standards, Professor McGuffy’s pre- and post-Civil War readers were very difficult.

• Average sentence length of 1963-91 books was shorter than that of 1945-1962 books.

• Mean length dropped from 20 to 14 words, “the equivalent of dropping one or two clauses from every sentence”

• Wording of schoolbooks after 1963 for 8th graders was as simple as that in books used by 5th graders before 1963

• Wording of 12th grade texts after 1963 was simpler than the wording of 7th grade texts before 1963.

• Today’s mean sixth, seventh, and eighth grade readers are simpler than fifth grade readers were before World War II.

G. P. RichardsonAAAS STEM Meeting, Nov 9-10, 2011

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Rockefeller College of Public Affairs and PolicyRockefeller College of Public Affairs and PolicyUniversity at AlbanyUniversity at Albany

Could Declining Sophistication of Texts Account for Declining SAT Verbal Scores?

G. P. RichardsonAAAS STEM Meeting, Nov 9-10, 2011

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Rockefeller College of Public Affairs and PolicyRockefeller College of Public Affairs and PolicyUniversity at AlbanyUniversity at Albany

Sliding Goals in School Texts?

G. P. RichardsonAAAS STEM Meeting, Nov 9-10, 2011

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Rockefeller College of Public Affairs and PolicyRockefeller College of Public Affairs and PolicyUniversity at AlbanyUniversity at Albany

A Formal Model to Fit to the Data

G. P. RichardsonAAAS STEM Meeting, Nov 9-10, 2011

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Rockefeller College of Public Affairs and PolicyRockefeller College of Public Affairs and PolicyUniversity at AlbanyUniversity at Albany

Historical and Simulated SAT Verbal Scores

G. P. RichardsonAAAS STEM Meeting, Nov 9-10, 2011

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Rockefeller College of Public Affairs and PolicyRockefeller College of Public Affairs and PolicyUniversity at AlbanyUniversity at Albany

The Endogenous Point of View

Causal forces (school texts?) are not exogenous but feed back upon themselves

“System as cause”

G. P. RichardsonAAAS STEM Meeting, Nov 9-10, 2011

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Rockefeller College of Public Affairs and PolicyRockefeller College of Public Affairs and PolicyUniversity at AlbanyUniversity at Albany

The “X/N” Matrix

Striving for understanding and

leverage, but failing

Achieving understanding and

leverage

Accepting fate, Predicting, Preparing

Confused, Misguided, Misguiding

Exogenous Endogenous

True (Predominant) State of Affairs

Exo

geno

usE

ndog

enou

s

Pre

dom

inan

t Mod

e of

Ana

lysi

s

G. P. RichardsonAAAS STEM Meeting, Nov 9-10, 2011

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Rockefeller College of Public Affairs and PolicyRockefeller College of Public Affairs and PolicyUniversity at AlbanyUniversity at Albany

School Texts Studies

• Marilyn Jager Adams, Advancing our Students’ Language and Literacy, The Challenge of Complex Texts, The American Educator 34, 4 (winter 2010-11)

• Hayes, Wolfer & Wolfe (1996), Schoolbook Simplification and Its Relation to the Decline in SAT-Verbal Scores, American Educational Research Journal 33 (2): 498-508.  • http://www.soc.cornell.edu/hayes-lexical-analysis/schoolbooks/Papers/HayesWolfe

rAndWolf1996.pdf

G. P. RichardsonAAAS STEM Meeting, Nov 9-10, 2011

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Rockefeller College of Public Affairs and PolicyRockefeller College of Public Affairs and PolicyUniversity at AlbanyUniversity at Albany

Optimal Parameters for SAT Fit

• Maximum of simulations/optimizations found at:

• Pressure to devote time and effort elsewhere = 7.99971

• Time to act on gap = 17.2977

• *Time to adjust goal = 37.935

• Simulations = 25138

• Optimizations = 107

• Pass = 3

• Payoff = -711.039

• ---------------------------------

• The final payoff is -711.039