Science SchoolsOn or Schools On Science?An exploration into the effects of science and on science in Arkansas middles schools

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Comparison of Average Gains of Math and Literacy to Science in Arkansas Middle Schools

ScienceMathematicsLiteracyAxis Title

Three Crises of Science Education Debate• No Child Left Behind does not hold states and schools accountable for proficiency in science. However,

the act did require states to conduct science assessments starting in the 2007-08 school year at least once during grades 3-5, grades 6-9, and grades 10-12 (Leath 2007).

• Since 2006, Representative Vernon Ehlers (MI-3) has tried during three different sessions of congress to pass a bill that “amends the Elementary and Secondary Education Act of 1965 to require the use of science assessments in the calculation of adequate yearly progress, and for other purposes.” The bill originally had 17 cosponsors in 2006 but dropped to 10 in 2007 and 4 in 2009. (Library of Congress).

• According to James Gates, Jr, a physicist with the University of Maryland, the United States is “confronting a third crisis in Science Education.”

1. The first was World War II in which the US handled successfully because of our innovations that went beyond the competition’s. 2. The second, Russia’s Sputnik, created the loudest SOS call for better science education, which did lead to NASA and men on the moon by 1969. 3. The third and current crisis is STEM (science, technology, engineering, and mathematics) education which he believes will determine the future economy (Gates 2010). “After World War II… we could count on the brightest minds from around the world seeking opportunity to come to us because we were the place where the most opportunity was apparent." (Dove 2010). A SILENT CRISIS “It's quiet because it takes years to educate a world-class scientist or engineer. It starts with the very early educational years and goes all the way through levels of advanced study.” Problems in the public school system may take a generation before affecting the job market and university laboratories (Dove, 2010).

A TWO DIMENSIONAL CRISISThe current crisis is not only for producing more globally competitive engineers and scientists but for producing a scientifically literate general population as well(Gross 2009).

Previous Research • Greene, Trivitt, and Winters found in Florida that students in both sanctioned and nonsanctioned

schools made gains in science and their findings suggested correlations in learning across high stakes and low stakes subjects (Greene 2010).

• Center on Education Policy conducted a national survey and found that since NCLB, “71 percent of the nation's 15,000 school districts had reduced the hours of instructional time spent on history, music and other subjects to open up more time for reading and math,” (Dillon 2006).

• Carnegie Mellon University and the University of Pittsburgh found that students taught through direct instruction were more likely on average to become “experts” in designing scientific experiments than those taught through what is often called discovery learning. The practice of direct instruction may allow time for science class even if time is limited for hands on discovery learning (Cavanah 2004).

Mutualism, Commensalism, or Parasitism? Crossing Borders in Literacy and Science Instruction: Perspectives on Theory and Practice (2004) EW Saul

Language and Literacy in Science Education (2001) JJ Wellington

Linking Science and Literacy in the K-8 Classroom (2006) R Douglas

Integrating Science with Mathematics & Literacy (2007) EL Hammerman and D Musial

Literacy and Science: a Learning Limitation or Route to Good Teaching (2002) T Tate

Science Essentials: Integrating Science and Literacy (2006) L Brenner

Problem –Based Learning for Math & Science: Integrating Inquiry and the Internet (2006) DL Ronis

"Having a scientifically literate public is going to be critical as our nation

wrestles with problems whose solutions seem inherently to involve science and

technology.” ~James Gates Jr.

"...a very large percent of our teachers who teach math and science have neither

certification nor a degree to teach those two subjects.“~ Bart Gordon

"I regard the collapse of math and science education as the greatest long-term strategic problem the

United States has, and likely to end our role as the leading country in the

world.” ~Newt Gingrich

It's great to see people doing hands-on science.

Science is fun.“ ~ Bill Gates

"Either you're a nerd, or you work for one.”

~Vernon Ehlers

“Science and education have been vital to America's success, and they will be

indispensable to our future.” ~Kay Bailey Hutchison

Questions of InterestWhat school and local characteristics affect science learning?

How does gains in science, literacy, and math affect each other? How much are students gaining in science compared to gains in math and literacy?

Does some Arkansas schools perform better in science than other schools?

Methodology • In order to capture a better picture of science achievement, a three step OLS regression

analysis was implemented.• The first two steps included clustering by schools.• All test scores and student level data came from the Arkansas NORMES database. School

level variables were from the National Center for Education Statistics and the Arkansas Department of Education.

• All tests were standardized by grades and year (2008 5th graders and 2010 7th graders) – one cohort group

Regressions I

scienceist = 0 + 1Testsit-2 + 2 Jist + 3Dst + + 4 Kist + εist

Where scienceist is ACTAAP test score for science for student i in school s during year t, specifically 2010. Tests are the language, math and , science test for student i in time t , 2008. J is a group of controls for student observed characteristics, and D is a group of school and location variables of specific interest: congressional district, location of school from urban areas, years student is in a school between 5th grade to 7th, voter approved property tax, and average starting salary of teacher. K are the math and literacy tests at year t. And ε is a stochastic error term.

Regression II, Multiple Equation Regression

Testsist = 0 + 15thTestsist-2 + 2 Jist + 3Hst + Hst*Hst +εist

Where 7thTestsist is ACTAAP test scores for math, literacy and science for student i in school s during year t, specifically 2010. 5thTests are the literacy, math and, science tests for student i in time t , 2008. J is a group of controls for student observed characteristics, and His a dummy category for schools which had negative test score averages in math, literacy and /or science for students between 5th and 7th grade. H*H are a series of interaction terms between schools that are negative in more than one subject . And ε is a stochastic error term.

Regression III, Individual School Effects

scienceist = 0 + 1Testsit-2 + 2 Jist + 3Fst + + 4 Kist + εist

Where scienceist is ACTAAP test score for science for student i in school s during year t, specifically 2010. Tests are the language, math and , science test for student i in time t , 2008. J is a group of controls for student observed characteristics, and F is a group of dummies for each school testing at time t for science. K are the math and literacy tests at year t. And ε is a stochastic error term.

Arkansas• Started testing science in 2008 for grades 5th, 7th, and 10th grade biology. • Arkansas Comprehensive Testing, Assessment and Accountability Program (ACTAAP)• Science tests are on 0-500 scale score and a 0-80 raw score. 250 is proficient at both grades. • Math and literacy are on a 0-1000 scale and differ in the raw scale base scores. Proficiency

minimum increases with grade. NAEP TEST Arkansas

Gr. YearState

Avg/SENationalA

vg/SE

At or above Prof.

Pct/SE Gr. Year

At or above Prof.

Science 4 2009 146 (1.2) 149 (0.3) 29 (1.1) 5 2009 43.7 2005 147 (1.2) 149 (0.3) 24 (1.1) 2008 38.3

8 2009 144 (1.3) 149 (0.3) 24 (1.0) 7 2009 33.3 2005 144 (1.0) 147 (0.3) 24 (1.5) 2008 33.7

Reading 4 2009 216 (1.1) 220 (0.3) 29 (1.4) 5 2009 67.8 2005 217 (1.1) 217 (0.2) 30 (1.1) 2008 65.1

8 2009 258 (1.2) 262 (0.3) 27 (1.4) 7 2009 70.6 2005 258 (1.1) 260 ((1.3) 27 (1.4) 2008 68.5

Math 4 2009 238 (0.9) 239 (0.2) 36 (1.4) 5 2009 63.12005 236 (0.9) 237 (0.2) 34 (1.4) 2008 58.8

8 2009 276 (1.1) 282 (0.3) 27 (1.2) 7 2009 67.8 2005 272 (1.2) 278 (0.2) 22 (1.1) 2008 63.5

ReferencesCavanagh, S. (2006, Nov. 10). NCLB Could Alter Science Teaching. Education Next. Retrieved from www.edweek.org

Dillon, S. (2006). Schools Cut Back Subjects to Push Reading and Math. The New York Times. Retrieved from ww.nytimes.com.

Dove, A. (2010). Science Education Crisis Intervention. New York Academy of Science Movement. Retrieved from www.nyas.org

Gates, J.S. (2010) Confronting a third crisis in US Science Education. Science News, 177(11), 32.

Greene, J.P, Trivitt, J. & Winters, M.A. (2010) Impact of High Stakes Testing on Student Proficiency in Low Stakes Subject. Economics of Education Review, 29(1) 138-146.

Gross, K.L. & Mittelback, K.G. (2009). What Maintains the Integrity of Science: An Essay for Nonscientists. Emory Law Journal, 58.

Leath. A. (2007). Student Performance Assessment in Science. American Institute of Physics, 74.

Library of Congress. Bill Text 111th Congress (2009-2010) H.R.2511 .II. Retrieved from Thomas.gov

Math F(15,319)=766.25 Prob>F= 0.0000 R2=0.6817

Literacy F(15,319)=1882.33 Prob>F= 0.0000 R2=0.7352

Regression II: Math, Literacy, Science Comparisons

science F(15,319)-1292.26

Prob>F =0.0000 R2=0.6981

Science 2008 0.430889 43.24 *** 0.222184 24.47 *** 0.1856 28.22 ***Neg Gain Science -0.31095 -5.84 *** -0.05585 -1.06 -0.0499 -2.59 ***Math 2008 0.171388 18.65 *** 0.485875 49.67 *** 0.1264 19.76 ***Neg Gain Math -0.23459 -4.2 *** -0.28765 -5.13 *** -0.0498 -2.22 **Literacy 2008 0.232414 25.16 *** 0.130816 16.35 *** 0.5094 69.72 ***Neg Gain Literacy -0.01477 -0.33 0.015997 0.39 0.0579 2.58 ***(-Science*-Math) 0.179911 2.38 ** 0.026582 0.36 0.038 1.16 (-Lit*-Math) 0.099649 1.5 -0.05619 -0.87 -0.0805 -1.96 *(-Lit*-Science) 0.000615 0.01 -0.0209 -0.32 -0.0244 -0.75 (-lit*-Math*-Science) -0.12505 -1.4 -0.02975 -0.34 -0.0709 -1.32 Constant 0.335369 9.12 *** 0.243974 7.15 *** 0.017 1.25

z_science_scalet2010 z_math_scalet2010 z_literacy_scalet2010z_science_scalet2010 1 z_math_scalet2010 0.3872 1 z_literacy_scalet2010 0.3572 0.3157 1

Breusch-Pagan test of

independence: chi2(3) = 11379.917, Pr = 0.0000

Regression I - School Characteristics Effects on ScienceIndependent Variables R. # βhat t p>t R and hypothesis testsliteracy standardized score 2008 0.2504 22.5*** Number of obs = 29708math standardized score 2008 0.16725 15.4*** F( 13, 303) = 1061.29 science standardized score 2008 0.4015 33.9*** Prob > F = 0.0000 English language learners 0.12349 3.52*** R-squared = 0.6622 students with disabilities -0.03027 -1.58 (1) years2 - years3 = 0 economiccally disadvantaged -0.11556 -8.54*** F( 1, 303) = 0.01 female students -0.08627 -10.9*** Prob > F = 0.9103charter schools -0.10445 -0.62 ( 2) years1 - years3 = 0minority students -0.19046 -9.53*** F( 1, 303) = 0.45 average teacher starting salary 1.3E-05 2.85** Prob > F = 0.5016voted mils by district 0.00196 0.46 ( 3) years1 - years2 = 0in new school 7th grade 2 -0.01722 -0.41 F( 1, 303) = 0.20 "years1" 3 -0.02068 -0.61 Prob > F = 0.6567in same school 6th and 7th grade 1 0.01888 0.45 ( 4) town - rural = 0 "years2" 3 -0.00191 -0.04 F( 1, 303) = 0.29 in same school 5th, 6th, and 7th 1 0.02417 2.38** Prob > F = 0.5880 "years3" 2 0.00648 0.14 ( 5) town - suburb = 0 school in a town 4 -0.04083 -0.76 F( 1, 303) = 0.00 5 0.00161 0.02 Prob > F = 0.9860school in a suburb 4 -0.04243 -0.46 ( 6) rural - suburb = 0 6 -0.02177 -0.24 F( 1, 29693) = 0.06school in a rural area 4 -0.02066 -0.44 Prob > F = 0.8085 5 0.02177 0.24 ( 7) town - city = 0 school in a city 5 0.04243 0.46 F( 1,303) = 0.58 6 0.021 0.44 Prob > F = 0.4461Congressional Code 501 7 0.0713 2** ( 8) rural - city = 0 9 -0.03875 -0.79 F( 1, 303) = 0.19 Congressional Code 502 7 0.11005 2.2** Prob > F = 0.6595 8 0.03875 0.79 ( 9) suburb - city = 0 Congressional Code 503 7 0.1462 3.18*** F( 1, 303) = 0.21 8 0.0749 1.66* Prob > F = 0.6453Congressional Code 504 8 -0.0713 -2** ( 10) district_501 - district_502 = 0 9 -0.11005 -2.2** F( 1, 303) = 0.62 constant 1 -0.36696 -1.7* Prob > F = 0.0004 2 -0.35007 -1.43 ( 11) district_502 - disrict_503 = 0 3 -0.34056 -1.54 F( 1, 303) = 0.38 4 -0.29649 -1.25 Prob > F = 0.5364 5 -0.33893 -1.42 ( 12) district_501 - disrict_503 = 0 6 -0.31715 -1.5 F( 1, 303) = 2.76 7 -0.11229 -0.53 Prob > F = 0.0975 8 -0.04099 -0.78 ( 13) district_502 - district_504 = 0 9 -0.00224 -0.01 F( 1, 303) = 4.85 ~* 90% ,**95%, and ***99% confidence interval Prob > F = 0.0285

~urban-centric codes, congressional codes from the National Center for Education Analysis and Regional Assistance

( 14) disrict_503 - district_504 = 0 F( 1, 303) = 10.10

~Different districts have schools with various grades. Prob > F = 0.0016

~School grade levels, mils, and teacher salaries from Arkansas Department of Education

( 15) district_501 - district_504 = 0 F( 1, 303) = 4.00

~remaining data from University of Arkansas Prob > F = 0.0463

Conclusions• Regression I model did not conclude any significant effects on science if a

middle school student changes schools between 5th and 7th grade, although it did lean towards the three year model. However in comparison to the other two models, the 3 years model did not show significance using a hypothesis test between the other 2 models.

• When controlling all other factors, location of the town based on the IES definitions for urban-centric was not significant on science education.

• The Congressional district provided mixed results depending on which dummy was selected.

• Voter determined property tax was also insignificant on science education.

• Average starting teacher salary was significant in large units. So further studies should include a cost analysis.

• R2 was greater for all models when the 2010 math and reading scores were included, which suggests that the two year time span between the tests is not as strong of an indicator as a more current year.

• However the 2010 multiple regression results suggests the three exams are correlated about 30%, with math and science having the strongest correlation followed by literacy and science. Also science and literacy gains are negatively influenced by a school that where on average students lose gains in literacy and/or science. Math is only negatively related to schools on average where students lose gains in math .

• When comparing the individual school effect results with their state AYP status, the results are mixed. Some schools that are predicted to have negative average gains in all three subjects (with everything else controlled) are “Achieving” , while other schools that predict positive gains across the board on average are “5 year Whole School Improvement .” This may simply mean the 5 year school is improving and the achieving school slipped some but was already high above the proficiency mark.

• Although this provides a snapshot of the relationship between math, reading, and literacy yet a longitudinal study would provide more insight, especially on the effect of individual schools gains by subject.

After 50 years policy makers are still saying SOS By Charlie M. Belin