Educational policy analysis archives

Educational policy analysis archives

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Educational policy analysis archives
Arizona State University
University of South Florida
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Educational policy analysis archives.
n Vol. 13, no. 1 (January 02, 2005).
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c January 02, 2005
Change and continuity in student achievement from grades 3 to 5 :a policy dilemma / Mary McCaslin, Heidi Legg Burross [and] Thomas L. Good.
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E DUCATION P OLICY A NALYSIS A RCHIVES A peer reviewed scholarly journal Editor: Sherman Dorn College of Education University of South Florida Copyright is retained by the first or sole author, who grants right of first publication to the Education Policy Analysis Archives EPAA is published jointly by the Colleges of Education at Arizona State University and the University of South Florida. Articles are indexed in the Directory of O pen Access Journals ( Volume 1 3 Number 1 January 2 200 5 ISSN 1068 2341 Change and Continuity in Student Achievement from Grades 3 to 5: A Policy Dilemma Mary McCaslin Heidi Legg Burross Thomas L. Good University of Arizona Citation: McCaslin, M., Burross, H. L., & Good, T. L. (2005, January 2). Change and c ontinuity in s tudent a chievement from g rades 3 to 5: A p olicy d ilemma Education Policy Analysis Archives, 13 (1). Retrieved [date] from http :// ( This article was accepted for publication by Gene V Glass, Editor 1993 2004. ) Abstract In this article we examine student performance on mandated tests in grades 3, 4, and 5 in one state. We focus on this interval, which w e term the fourth grade window, based on our hypothesis that students in grade four are particularly vulnerable to decrements in achievement. The national focus on the third grade as the critical benchmark in student performance has distracted researche rs and policy makers from recognition that the fourth grade transition is essential to our understanding of how to promote complex thinking and reasoning that is built upon a foundation of basic skills that may be necessary, but are not sufficient, for the more nuanced learning expected in subsequent grades. We hypothesized that the basic skills that define a successful third grade performance do not predict successful performance in subsequent years. We examined student performance over time using two me asures of student success: the Arizona Instrument to Measure Standards (AIMS), a standards based test; and the Stanford 9 (SAT9), a norm referenced test. Three groups of schools were included in these analyses. Schools were individually matched to the or iginal sample of interest, which were schools serving students of poverty that received state funding to implement Comprehensive School


Education Policy Analysis Archiv es Vol. 13 No. 1 2 Reform (CSR) models that emphasize continuity across grade levels The first comparison sample includes schools that al so serve students of poverty but did not receive CSR funding, nonCSR schools. The second comparison sample includes schools individually matched on all variables except economic status. These schools, which we term low poverty schools, are the wealth iest public schools in the state, with less than 10% of attending students receiving free or reduced lunch. Student test scores in math, reading, and writing (AIMS) or language (SAT9) were analyzed for the years 2000 2003. These intervals allowed the an alysis of two cohorts of the fourth grade window. Our results suggest that the reliance on third grade performance to label students and schools is untenable. Introduction This investigation began with a hypothesis that the fourth grade is a critical p eriod of schooling especially for students of po verty. Our initial focus was on schools selected for funding by the Arizona State Department of Education to implement a Comprehensive School Reform (CSR) model. CSR models are school wide reform efforts supported by Federal Title One funds that attempt to improve the educational outcomes of schools serving students of poverty by unifying curriculum, instruction, and management of that instruction across grades within a school. Several CSR models derived from best practices research, are available for schools to implement (e.g., Expeditionary Learning Outward Bound, Success for All) or design (the so called home grown approach). Our initial task was to assess the potential for various CSR models to p romote student achievement in grades 3 5 (see Good, Burross, & McCaslin, in press). In this paper we attend to our hypothesis, that the transition between grades 3 and 5, what we term the fourth grade window, mediates student performance in important wa ys. Elementary schools implementing funded CSR models were individually matched with schools not receiving state funds for school reform (nonCSR schools) based on geography, grade composition, size, and poverty levels (defined as % of students receiving free or reduced lunch). Changes in student test performance associated with the fourth grade window occurred similarly in both CSR and nonCSR schools. These findings are consistent with the cumulative deficit attributable to poverty (Hess & Shipman, 1 965; Pogrow, 1999); however, our hypothesis is that the fourth grade window is more pervasive than poverty, although it may well be exacerbated by it. To test this hypothesis we included schools individually matched to the original CSR schools using the sa me criteria for the nonCSR schools serving students of poverty geography, grade composition, and size but with low levels of poverty. In these low poverty comparison schools, less than 10% of the students received free or reduced lunch. Thus, the analyse s we focus on involve comparisons among three groups of schools, two matched groups of poverty schools in Arizona, one group receiving state funding to implement comprehensive school reform models and the other not, and one group of schools matched on all criteria except poverty rates of its students. The poverty schools are not the most impoverished public schools in the state; however, the low poverty comparison schools are the wealthiest public schools in the state. Student test performances on the Ariz ona Instrument to Measure Standards (AIMS) and the Stanford 9 ( SAT9 ) are tracked for four years, 2000 2003. These multi year performances allow two


McCaslin, Burross & Good: Achievement from Grades 3 to 5 3 replications of longitudinal analyses of the fourth grade window, that is, two cohorts of students moving f rom grade 3 to grade 5. These comparisons inform the: 1) viability and robustness of the fourth grade window in student performance, 2) function of student socio economic status (and school resources) in this phenomenon, and 3) representation of student kn owledge as a function of test used (criterion or norm referenced) and the policy implications that emerge. Related Literature The economics of student performance Ample evidence suggests that poverty interferes with student performance (Ladd & Hansen, 1999). The number of children living in poverty is increasing rapidly (e.g., National School Boards Association, 1999; US Government Printing Office, 1999, Forum on Child and Family Statistics, 1999). Additionally, states and school districts have unequ al resources for schooling. Generally, schools that serve low income students receive fewer funds than do schools serving more affluent communities; unequal resources have been distributed within a school district as well as among them (Stiefel, Rubenstei n, & Berne, 1998; Ladd & Hansen, 1999). Schools whose students bring fewer home resources to the classroom also are comparatively under resourced; thus, typically the children of poverty attend schools with fewer financial resources. Some researchers a rgue that these are not troublesome relationships. Earlier Coleman (Coleman, Campbell, Hobson, McPartland, Meade, Weinfeld, & York, 1966) and more recently Hanushek (1997) make the argument that school expenditures are largely unrelated to student perform ance. One difference between then and now is that the genetics of home reason for ignoring differences in school funding (e.g., Jensen, 1973) has been replaced with an economics of home rationale. Others have argued for a guarded optimism that underfi nanced schools can use increased funding wisely and impact student performance (Hill, Cohen, & Moffitt, 1999; Ladd & Hansen, 1999). One manifestation of funding wisely is the comprehensive school reform initiative. Good, Burross, and McCaslin (in press) analyzed the effects of CSR programs in Arizona on reducing the differences in student test performance as a function of home or school poverty. Results suggest that money may be a necessary condition, but it may not be sufficient to increase student per formance in schools serving students of poverty. In this paper we broaden the discussion of school funding and student performance by 1) considering the effects associated with the saturation level of poverty (CSR: M= 80%; nonCSR: M=71%) and 2) including schools that serve students of relative affluence (non poverty: M= 5%). We examine the coincidence of student home economics and school resources and its relation to changes in student performance across grades 3 5. Critical periods in student learning It has been argued since the 1970s that student performance in the third grade (especially reading performance) predicts student performance in high school and beyond (e.g., Klaus, 1973). This reasoning is evident in the current Federal school reform initiative, No Child Left Behind. Third grade is considered a pivotal benchmark in students learning to


Education Policy Analysis Archiv es Vol. 13 No. 1 4 read. High stakes testing (that is, tests associated with high stakes consequences for students and/or their schools) often begin at the third grade. In some states third grade students are automatically retained if they fail to achieve a set testing standard (e.g., Florida); in others, failures in third graders test performance yield failing labels for schools with conditional threats of state take ov er (e.g., Arizona). Third grade has become the grade at which serious decisions are made about students and schools. Pogrow (1999), argue d that 3 rd grade test performance overpredicts the achievement of students of poverty and that the apparent gains in p overty students performance or at least apparent decreases in the difference between students of poverty and privilege dissipate by the time the students leave elementary school. Pogrow casts the problem as a cognitive wall that results from an increasi ngly complex curriculum for which the student of poverty is ill prepared. Similarly, McNeil (2000) argue d that school reform efforts in Texas, and the use of the high stakes Texas Assessment of Academic Skills test, causes poverty students to receive a cur riculum that is focused primarily on drill and practice of low level reading and math skills. She notes that these students lose in two ways. First, they do not have the opportunity to engage higher level math and reading concepts; second, they are not ge tting exposure to the fullness of what we consider an education (e.g., science, social studies) because time is spent on priority test areas. McNeil also describe d affluent school districts that argue that the mandated tests work to lower their standards their own assessments expect more thinking and advanced knowledge than the new school reforms. It appears that mandated tests may restrict the opportunities for students of poverty to be exposed to higher order learning while they restrict the opportuni ties for students of privilege to display their higher order learning. If this is the case, then the apparent gaps between students of poverty and wealth are more disparate than they appear on mandated tests. At minimum, they appear to reify a basic leve l curriculum for students of poverty. Others point to fourth grade as a particularly susceptible time for learners. Students are transitioning into more complex cognitive mechanisms (Case & Okamoto, 1996; Piaget, 1983) that can challenge their simple an d sure (Hofer & Pintrich, 1997) knowledge base at the same time they confront more complex learning formats (McCaslin, et al., 1994) and tasks (Chall, 1996) For example, the pattern of declining scores from third to fourth grade was observed on a standa rdized mathematical instrument in 26 nations (Wang, 2003). In this study, the exact same 20 test items were given to third and fourth grade students. The third graders outperformed the fourth graders on an average of 5.7 of the examined items and up to 1 6 of the items in one country. It may well be that the simple and sure curriculum and test representation of knowledge and knowing at the third grade does not serve subsequent learning as expected. This could be a due to a straightforward disconnect between the curricula and instructional strategies of the third and fourth grades, but it is also possible that the mechanization procedures that result in a successful third grader obviate the enhancement of subsequent thinking and learning of the fourt h grader. The Einstellung of Luchins and Luchins (1950) may apply to more than immediate problem solving. Consider the difficulty in getting students who have learned how to do long division with remainders! to keep their pencils on their desks as they me ntally estimate how many of one unit is found in another. Do the learning habits and beliefs about knowledge instilled in the early grades and reified in high stakes testing interfere with the struggle to understand complexity and probabilistic reasoning t hat are the hallmarks of what we consider an educated learner? We study students in grades 3 5, the period that we term the fourth grade window, because we suspect there is too much attention to the predictive power of grade 3


McCaslin, Burross & Good: Achievement from Grades 3 to 5 5 and not enough attention t o the subsequent 2 years of schooling and their relationship with earlier learning opportunities and ultimate educational attainment especially for students of poverty. We want students to succeed in the long term and the current focus on 3 rd grade as the critical period in student performance seems ill advised. The measurement of student performance Students can fail test items for many different reasons. We typically think that a failure suggests that material was too difficult for students; howeve r, students may not have had an opportunity to learn material that is not too difficult for them, it is simply unknown to them. Opportunity to learn is a basic tenet for interpretation of student performance, both theoretically (Carroll, 1963) and practic ally (e.g., Berliner & Biddle, 1995; Good & Grouws, 1979). Students also can make simple material problematic and fail items that under represent their understanding. As we have noted, this is especially the case when students are progressing into a more sophisticated level of thinking about content (Case & Okamoto, 1996; Piaget, 1983) as higher levels of thinking and understanding are not always represented by the right answer. Successful test taking often is quite different from successful classroo m learning. When learning, students complete assignments that show their work and thinking. In math, the problems are worked out and teachers want to see the process students used to solve the problem, and in writing the revisions count. Directions are supposed to be clear and the objectives known: students know what to do and why they are doing it. Students believe their teachers want them to succeed. Not so, the test makers. Taking mandated tests is another story. When taking tests, classroom bulleti n boards, student work samples, and decorative posters are removed or covered for fear students might see something that helps them remember or answer an item correctly. Students show their knowledge in formats that require eye hand coordination to stay on the right bubble. Successful test taking is all about reading directions that can (and do) change unexpectedly, resisting the lure of the first familiar and intentionally seductive answer, moving on when confronted by difficulty, not wasting time wor king the problems through to completion, and keeping one eye on the clock. It is a considerable leap from student test performance to student learning. Even among those who agree about the use of testing, there are disagreements about the type of test, ti me of administration, and stakes involved with successes and failures. One consideration at any level of testing involves the method for reporting results. Specifically, norm referenced and standards based reporting provides different information. Norm referenced tests describe the individuals (i.e., student, class, school) performance in terms of how s/he did in relation to others who took the same test (e.g., percentiles). Standards based performances are reported based on the individuals performan ce in relation to a standard of excellence (e.g., percentage correct). Both methods of reporting results have advantages and problems. Norm referenced methods allow the user to determine the individuals relative standing, but do not provide general perf ormance information. Standards based methods depict the level of the individuals performance, but do not provide details about how others performed, and the standard and the cut score for success or failure may, at times, be arbitrary. Arizona Instrumen t to Measure Standards The Arizona Instrument to Measure Standards (AIMS) was born out of the Arizona Student Assessment Program (ASAP) test, both of which were designed by the Arizona Department of Education to measure state standards for students. Stude nts take the AIMS test in grades 3, 5, 8, and 10


Education Policy Analysis Archiv es Vol. 13 No. 1 6 through 12 in math, reading, and writing. These tests were developed in response to nationwide calls for stricter high school graduation requirements (Jorgensen, 1999). Both have reported reliability and va lidity problems since their inceptions (Smith, Heinecke, & Noble, 1999). Plans to make the AIMS test a requirement for high school graduation are in place despite many revisions of the test and delays in the implementation of the graduation requirement. This year s sophomore students took their first crack at the AIMS test in February 2004; the current plan is to allow up to 4 retakes by the end of senior year to achieve graduation. One wonders what incentives to complete high school remain for a success ful sophomore, but the focus of criticisms of the test has largely been on the lack of time provided between the introduction of the AIMS test in 1998 and related standards and the passing requirement for graduation originally proposed for the 1999 2000 sc hool year. This narrow time frame gave teachers little time to enact the standards within the classrooms and prevented revision and review to determine whether the standards were appropriately set (Jorgensen, 1999). Critics also claim that with standards set at college entrance levels and the lack of appeal process, special education and non native English speakers are unfairly denied graduation rights. At last report, surveys were being conducted across the state to gather public opinion about the timin g of the graduation requirement and stringency of the standards (WestEd, 2001). The recommendations by the board that conducted this survey included waiting another three to four years for graduation requirement implementation, review and implement indivi dual sections of the test in stages, and review current results to set transitory standards. It is useful to consider the standards represented in the AIMS test in relation to the National Assessment of Educational Progress (NAEP). In 2003, only 25% of Arizona fourth graders scored at the proficient level in math and 23% scored proficient in reading on the NAEP (Gassen, 2003). Both of these performances are at least 7% below the national average. The state superintendent of education, Tom Horne, has noted that the state of Arizonas standards tend to be lower than the nations standards (in Gassen, 2003). Stanford 9. Arizona started using the SAT9 during the 1996 1997 school year. It was administered in grades 2 11 to students across the state. T his standardized measure is given nationally and results are reported in terms of national percentile rankings. SAT9 results are used for ranking high schools. This method of reporting results has been criticized by some for lacking information about compa rison to an absolute standard ( Also, some states use the same form of the test year after year because of the costs associated with buying newer forms ( ). Another common criticism with this and any standardized measure (especially those with rankings and finances hinged on students success) is teaching to the test. Method Two measures of acade mic standards were used in this state: Arizona Instrument to Measure Standards (AIMS) and Stanford 9 ( SAT9 ). The AIMS test was administered in grades 3, 5, 8, and 10 through 12. The SAT9 was administered to grades 2 through 12 and included reading, langu age, and math performance areas. For this research, three samples of schools were used: CSR funded schools (CSR schools, n = 21); schools individually matched to the CSR schools based on geography, grade composition, size, and poverty level (non CSR s chools, n = 23); and schools individually


McCaslin, Burross & Good: Achievement from Grades 3 to 5 7 matched to the CSR schools based on geography, grade composition, and size, but have low poverty levels, defined as less than 10% of attending students received free or reduced lunch (low poverty schools, n = 21 ). There were originally 27 CSR schools, but only SAT9 scores for grades three through five and AIMS scores for grades three and five were included in this study. There were more of the non CSR schools with grades 3 and 5 than the CSR schools with grades 3 and 5 because one criterion for matching with the CSR schools was that the non CSR and low poverty schools had at least the same grades as the CSR schools, two non CSR kindergarten through grade eight schools were matched to CSR grades six through eight schools. Poverty level was defined as percentage of students receiving free or reduced lunches. The percentage of students receiving free and reduced lunches was presented on the state web site ( safety/cnp/frpercentages.asp ). This information was broken into frequencies of students receiving reduced price lunches, free lunches, and those who paid full price. The free/reduced lunch percentage was cal culated by adding all of these frequencies and dividing that into the sum of those receiving free and those receiving reduced price lunches. Poverty matches were conservative: non CSR matching schools were selected at the same poverty level or less so tha t CSR schools as a group have the highest saturation of poverty in the study. The free/reduced lunch rates on the three types of schools were subjected to ANOVA procedures (F(2, 346) = 719.77, p < .001). Scheff post hoc analyses revealed that each group significantly differed from the others with p < .001: CSR: M = 80.74, s = 14.86; nonCSR: M = 69.87, s = 18.58; and low poverty: M = 4.99, s = 2.20. The nonCSR schools had lower poverty less saturation levels than the CSR schools (see Table 1). Table 1 Free and reduced l unch percentages by school type School type 2000 2001 2002 2003 Overall Means Mean 79.81 79.29 79.41 80.76 79.82 N 25 25 24 24 24.5 SD 15.77 15.24 15.15 11.49 14.41 Minimum 29.00 38.00 29.09 51.61 36.93 CSR Maximum 95.24 100.0 0 95.01 95.11 96.45 Mean 69.72 68.61 72.80 71.81 70.74 N 23 23 23 23 23 SD 18.99 17.01 15.59 17.28 17.22 Minimum 32.90 37.22 40.78 34.92 36.46 N on CSR Maximum 97.33 94.96 95.03 97.06 96.10 Mean 4.55 4.82 5.28 5.54 5.06 N 19 20 20 20 19.7 5 SD 2.25 2.11 1.95 2.25 2.13 Minimum 1.10 1.16 1.41 .95 1.16 L ow P ov Maximum 8.84 8.66 8.72 9.20 8.86


Education Policy Analysis Archiv es Vol. 13 No. 1 8 Variables AIMS results were reported in terms of percentage of students by grade and school who fell into the following categories: Exceeds the Sta ndard, Meets the Standard, Approaches the Standard, and Falls Far Below the Standard ( celevels.asp ). By both state standards and for use in this report, students who Exceed and Meet the standard were considered to have passed the AIMS test. AIMS results for third and fifth grade students were used in analyses. Percentages were repo rted only when at least 10 students had taken the exam within each category. Third through fifth grade results for SAT9 also were used in these analyses ( .gov/ResearchPolicy/SAT9Results/2003/default.asp ). SAT9 results were reported as norm referenced national percentile ranks by grade and performance area. These data were transformed to normal curve equivalence scores and missing data imputed using regre ssion analyses. Both tests have math and reading subtests. The AIMS test has a writing section and the SAT9 has language. The AIMS test was not administered to fourth graders, but the SAT9 test was. The tests were similar in many ways; however, the met hods of reporting results, subtests, and grade compositions of each test differ. These similarities and differences will be described in more detail as the results of the analyses are presented subsequently. The correlations between AIMS and SAT9 overall mean scores were all significant (all above r = .86, p < .01), across and between years. Schools maintained relative standings on these two measures every year. Table 2 contains the correlations between AIMS and SAT9 for grades 3 and 5 for each year of the study. The correlations remain strong and relatively constant in each instance. Table 2 Correlations between AIMS and SAT9 scores by grade, 2000 2003 Third grade Fifth grade AIMS 2000 AIMS 2001 AIMS 2002 AIMS 2003 AIMS 2000 AIMS 2001 AIMS 200 2 AIMS 2003 r .868 .842 .838 .799 .837 .858 .905 .868 SAT9 2000 N* 159 171 160 146 171 171 177 156 r .862 .911 .865 .804 .795 .889 .878 .837 SAT9 2001 N* 159 174 163 149 171 174 180 159 r .864 .868 .899 .828 .806 .872 .908 .867 SAT9 2002 N* 159 17 4 163 149 171 174 180 159 r .827 .823 .848 .901 .771 .826 .856 .907 SAT9 2003 N* 159 174 163 149 171 174 180 159 Note. All are significant at the 0.01 level. N is the number of grades for each year.


McCaslin, Burross & Good: Achievement from Grades 3 to 5 9 Results Free and reduced lunch percentages Because of the manner in which schools were selected, there are three distinct distributions of free and reduced lunch percentages over the four years. Table 3 displays the correlations for each year (2000 through 2003) between free/reduced lunch percen tages and the AIMS and SAT9 scores for all schools. Table 3 Correlations between free/reduced lunch percentages and A IMS and SAT9 scores, 2000 2003 AIMS 20 00 AIMS 20 01 AIMS 20 02 AIMS 20 03 SAT 9 20 00 SAT 9 20 01 SAT 9 20 02 SAT 9 20 03 POV 20 00 r .826 810 .845 .778 .770 .727 .758 .726 N 59 62 61 55 62 63 63 63 POV 20 01 r .945 .922 .947 .892 .879 .841 .864 .828 N* 57 59 58 52 60 61 61 61 POV 20 02 r .921 .900 .942 .880 .885 .842 .871 .834 N 56 58 57 51 57 58 58 58 POV 2 0 03 r .910 .904 .934 .877 .863 .835 .855 .831 N* 57 59 58 52 58 59 59 59 Note. All significant at the 0.01 level (2 tailed). N is the number of schools since poverty data is available at the school level only. Correlations between free/r educed lunch percentages and AIMS and SAT9 scores were all below r = .72 ( p < .001) across and between years. That is, the higher the test scores, the lower the percentage of students receiving free and reduced lunches. This finding also was obtained wh en the low poverty schools were removed from the analysis and just the CSR and non CSR schools (which both served students of poverty yet differed in saturation of poverty) were analyzed. The correlations between free/reduced lunch percentages and AIMS sc ores for these two poverty groups were below r = .46, p < .01. The relationships between free and reduced lunch percentages and SAT9 scores in these poverty schools were in the same direction, between r = .56 and r = 0, and many were non significant. T he relationship between saturation of poverty (the percentage of students receiving free and reduced lunches) and performance on the AIMS was stronger than the relationship between the saturation of poverty and SAT9 scores.


Education Policy Analysis Archiv es Vol. 13 No. 1 10 Differences among school type s Low poverty schools had higher mean scores than the CSR and non CSR schools on the AIMS performance areas, with overall mean scores 40 50 points higher in all cases (Table 4). The lowest percentage of students in low poverty schools who passed in any year and performance area was 40% of fifth grade students in math in 2002 at one school, and there were schools with 100% passing in third grade writing in 2000, 2002, and 2003. At least six CSR and non CSR schools had no students pass math in third or fi fth grade one or more years. Table 4 AIMS passing percentage means by year, performance area, grade and school type Y ear A rea G rade S chool type 2000 2001 2002 2003 CSR (N = 14) 23.57 34.36 36.50 37.21 Non CSR ( N = 14) 34.14 40.86 46.14 49.14 T hird Low Pov ( N = 19) 83.42 84.53 85.37 83.53 CSR (N = 15) 10.87 15.33 18.60 22.60 Non CSR ( N = 17) 19.12 31.06 25.88 29.00 M ath F ifth Low Pov ( N = 19) 66.68 72.68 75.32 74.32 CSR (N = 15) 46.40 50.80 50.87 52.73 Non CSR ( N = 14) 55.29 56.93 59.64 61.64 T hird Low Pov ( N = 19) 92.00 91.21 91.95 89.26 CSR (N = 15) 38.07 29.00 29.40 32.40 Non CSR ( N = 17) 46.71 38.29 38.59 41.18 R eading F ifth Low Pov ( N = 19) 89.05 82.21 83.37 79.47 CSR (N = 15) 54.00 52.93 53.00 54.1 4 Non CSR ( N = 17) 68.77 63.15 66.08 67.62 T hird Low Pov ( N = 19) 96.21 92.95 93.95 90.79 CSR (N = 15) 26.73 30.00 25.53 32.67 Non CSR ( N = 17) 36.06 41.29 36.35 39.76 W riting F ifth Low Pov ( N = 19) 79.05 80.00 83.47 80.05 Note. Includes only those scho ols with reported scores for all years within grade by each year and performance area. N is the number of schools with reported passing percentages within each grade and performance area. Student performance on the SAT9 show similar, although weaker, trends (Table 5). The low poverty schools consistently outperformed the CSR and non CSR schools both within and between grades across years.


McCaslin, Burross & Good: Achievement from Grades 3 to 5 11 Table 5 SAT9 NCE score means by year, performance area, grade and school type Y e ar A rea G rade S chool type* 2000 2001 2002 2003 CSR (N = 21) 35.24 39.70 41.22 39.86 Non CSR ( N = 21) 39.21 39.39 42.11 43.34 Third Low Pov ( N = 19) 65.42 66.89 66.89 65.95 C SR (N = 22) 40.14 40.46 42.22 41.69 Non CSR ( N = 22) 42.61 44.42 45.24 46.00 Fourth Low Pov ( N = 19) 67.89 68.53 68.68 70.26 CSR (N = 22) 36.86 40.58 41.34 42.28 Non CSR ( N = 22) 43.75 44.65 46.07 46.05 M ath Fifth Low Pov ( N = 19) 68.89 69.53 70.63 70.16 CSR (N = 21) 33.90 37.66 38.85 38.00 Non CSR ( N = 21) 39.55 39. 53 41.49 42.10 Third Low Pov ( N = 19) 62.32 62.16 62.58 62.84 CSR (N = 22) 39.05 39.69 40.27 41.19 Non CSR ( N = 22) 41.93 43.96 43.69 43.50 Fourth Low Pov ( N = 19) 65.05 67.37 66.74 68.00 CSR (N = 22) 37.23 38.54 37.93 40.00 Non CSR ( N = 22 ) 41.61 41.87 43.25 44.78 R eading Fifth Low Pov ( N = 19) 63.84 63.53 64.89 64.11 CSR (N = 21) 39.00 41.47 43.22 41.96 Non CSR ( N = 21) 42.26 42.82 44.68 44.96 Third Low Pov ( N = 19) 64.89 66.53 66.26 65.00 CSR (N = 22) 38.91 38.85 40.84 40.32 Non CSR ( N = 22) 42.16 42.46 42.78 42.91 Fourth Low Pov ( N = 19) 60.16 62.21 62.26 62.84 CSR (N = 22) 35.64 36.76 36.75 37.87 Non CSR ( N = 22) 40.66 40.19 41.80 42.46 L anguage Fifth Low Pov ( N = 19) 59.74 59.47 60.53 59.89 Note. Includes only those school s with reported scores for all years within grade by each year and performance area. N is the number of schools with reported NCE percentile scores within each grade and performance area. Longitudinal analyses Repeated measures analyses of variance (RM ANOVA) were performed on AIMS scores from third to fifth grades with a two year lapse (third in 2000 to fifth in 2002, cohort 1; third in 2001 to fifth in 2003, cohort 2). In all cases, the low poverty schools outperformed the CSR and non CSR schools ( p < .001). There were ordinal interaction effects for school type (CSR, non CSR, and low poverty) over time for reading and for writing in cohort 1, 2000 third graders to 2002 fifth graders, with less of a decrease in scores in the low poverty schools th an the CSR or non CSR schools. There were decreases in scores for all AIMS performance areas and school types for cohort 1, third grade in 2000 to fifth grade in 2002, and cohort 2, third grade in 2001 to fifth grade in 2003 ( p < .001; Table 6). A compa rison between cohorts shows that although a decrement in their own performance trajectory, fifth grade students in poverty schools (CSR


Education Policy Analysis Archiv es Vol. 13 No. 1 12 and nonCSR) in 2003 scored higher in math than the fifth grade students in these schools in 2002. Further, the variatio n in student performance in third grade differed as a function of school type ( p = .01) such that AIMS scores in CSR and nonCSR schools were more varied than in nonpoverty schools. This difference in dispersion as a function of school type was not found i n the fifth grade. Table 6 AIMS performance area percentage passing means by school type and year/grade Year/grade Area S chool type 2000 3rd grade 2002 5th grade 2001 3rd grade 2003 5th grade CSR* 20.89 (10.82) 16.67 (11.22) 34.12 (19.57) 25.1 3 (14.24) Non CSR ( N = 18) 35.11 (22.06) 26.17 (15.69) 39.83 (21.63) 30.50 (19.19) Math Low Poverty** 83.42 (7.96) 75.32 (12.18) 84.30 (6.22) 74.20 (12.17) CSR* 44.06 (13.22) 28.00 (12.65) 51.56 (17.31) 34.31 (15.60) Non CSR ( N = 18) 55.44 (17.5 9) 40.44 (14.90) 55.67 (17.53) 41.39 (17.49) Reading Low Poverty 92.00 (3.94) 83.37 (8.41) 90.90 (5.73) 79.00 (9.80) CSR* 52.17 (12.97) 24.83 (8.91) 53.25 (15.41) 34.56 (14.75) Non CSR ( N = 18) 67.39 (21.28) 38.44 (13.70) 62.00 (21.53) 39.72 (18.20 ) Writing Low Poverty** 96.21 (2.42) 83.47 (7.46) 93.00 (3.89) 79.70 (9.04) Note. Values in parentheses are standard deviations. N = 18 for CSR schools third grade in 2000 and fifth grade in 2002 and N = 16 for CSR schools in third grade in 2001 and fifth grade in 2003. ** N = 19 for non CSR schools third grade in 2000 and fifth grade in 2002 and N = 20 for non CSR schools in third grade in 2001 and fifth grade in 2003. Student performance on the SAT9 indicated changes in performance from third to fifth grade; however, these results are not as straightforward as the AIMS test data (Tables 6 and 7). For both sets of longitudinal analyses (cohort 1, third grade in 2000 to fifth grade in 2002; cohort 2, third grade in 2001 to fifth grade in 2003), the stat istical results were the same. There were no interaction effects for time by school type in any performance area. Scores changed significantly over time in all performance areas ( p < .01): there was a linear drop in language, a linear improvement in math and a quadratic change in reading, with an increase in fourth grade scores then slight decrease in fifth grade for almost all school types.


McCaslin, Burross & Good: Achievement from Grades 3 to 5 13 Table 7 SAT9 performance area means and standard deviations by school type and year/grade Year A rea S chool ty pe 2000 3rd grade 2001 4th grade 2002 5th grade 2001 3rd grade 2002 4th grade 2003 5th grade CSR (N = 21) 35.24 (9.77) 40.11 (8.18) 40.88 (8.26) 39.70 (7.21) 41.76 (7.94) 41.81 (8.67) Non CSR ( N = 21) 39.21 (10.06) 44.11 (7.25) 45.69 (9.3 2) 39.39 (8.75) 44.87 (8.49) 45.53 (10.48) M ath Low Poverty* 65.42 (6.55) 68.53 (4.02) 70.63 (5.00) 66.75 (6.29) 68.45 (5.82) 69.95 (5.91) CSR (N = 21) 33.90 (8.32) 39.49 (8.86) 37.65 (7.27) 37.66 (8.22) 39.95 (11.44) 39.43 (10.51) Non CSR ( N = 2 1) 39.55 (7.56) 43.73 (11.88) 42.98 (10.53) 39.53 (9.62) 43.58 (10.38) 44.29 (10.61) R eading Low Poverty* 62.32 (7.30) 67.37 (5.83) 64.89 (5.49) 61.95 (6.23) 66.50 (4.81) 63.80 (5.19) CSR (N = 21) 39.00 (8.45) 38.56 (8.14) 36.36 (13.62) 41.47 (7.29) 40.65 (9.30) 37.53 (9.41) Non CSR ( N = 21) 42.26 (8.32) 42.25 (12.53) 41.36 (13.27) 42.82 (7.80) 42.58 (8.27) 42.15 (8.33) L anguage Low Poverty* 64.89 (7.24) 62.21 (6.38) 60.53 (6.23) 66.40 (5.39) 62.15 (6.59) 59.80 (5.28) Note. V alues in parentheses are the standard deviations. N = 19 for low poverty schools in third 2000 to fifth 2002 and N = 20 for low poverty schools in third 2001 to fifth 2003. Discussion The viability of the fourth grade window in student performance Third grade scores on the AIMS test were a poor predictor of performance on the fifth grade test. The percentages of students passing the AIMS test in all performance areas decrease as the same cohort of students moves from third to fifth grade. Scores declined as predicted in both stu dent cohorts. All schools dropped in percentage of students passing in each performance area of the AIMS test. Students in low poverty schools, however, earned higher scores than those students in schools of both levels of poverty. Even in these schools, which had 80 90% of students passing the AIMS test, however, the fourth grade window is evident, indicating that greater resources alone are not the solution to declining performances in fifth grade. The same trend is evident on the SAT9 for language, but not in math or reading. In those performance areas, the relative ranking of grades improved after third grade. Since the performance areas for reading and math on the AIMS and the SAT9 are highly correlated, the difference may be less due to content and more to the way in which test results are reported. If all students perform poorly on a norm referenced exam, their relative ranking can remain the same and difficulties experienced by all of the students go unnoticed. The correlations


Education Policy Analysis Archiv es Vol. 13 No. 1 14 between the A IMS and the SAT9 tests in the fifth grade remain strong, suggesting that the tests continue to be aligned, thus, the drop from third to fifth grade does not appear to be a function of abnormalit ies in the AIMS test, although the feasibility of the cut scor es the standard of excellence criteria is worthy of consideration. Policy implications It is likely that policy makers using the results from the AIMS test would conclude that, despite several years of reform efforts, students across the board are dropp ing in their achievement from grades 3 to 5. This conclusion could warrant increasing sanctions to keep fourth and fifth grade teachers more squarely on a curriculum aligned with the test. This would mean a curriculum focused even more on reading and mat h and less time on science, physical education, music, and other non tested content areas. It would not be surprising to find pejorative notions of youth (Nichols and Good, 2004) moving further into childhood as students are held accountable for their a chievement decrements. School leaders may interpret the problem as reassigning effective teachers to the fifth grade (as likely has already been done with the third grade), thereby rendering fourth grade students even more vulnerable to achievement diffic ulties. Consider as well that there is some indication that poverty learners are becoming more similar with schooling while advantaged learners are becoming more diverse. The variation in third grade performance associated with school type dissipates at the fifth grade. Although this may be seen as a laudable achievement by some (exposure to schooling restricting the variation among poverty learners even if associated with a lower mean), others might worry that the variation among fifth graders of relati ve privilege is eroding earlier accomplishments. In each case, a more clear focus on the fourth grade window rather than a policy of benign neglect seems warranted. In contrast, policy makers using the Stanford 9 results can maintain their current posi tion regarding school reform as the data are essentially non informative. We already know that poverty interferes with student performance. The economics of home in combination with a normal distribution of student achievement suggests that if someone is to be at the bottom it is understandably the poor. The same conclusion could support a call for increased resources for schools serving students of poverty. The notion of saturation of poverty affords a third alternative: the feasibility of designing sc hool populations sensitive to home economics such that the saturation of poverty students attending a given school is kept below a specific ratio. Our analysis suggests that 80% level of poverty is more formidable than 70%. Research on poverty saturatio n thresholds and their relation to changes in student achievement seems warranted. A major implication that emerges from both the AIMS and SAT9 results is that third grade performance is not particularly informative. The notion of third grade as the crit ical moment in learning th at predicts future success is unwarranted. The fourth grade window is a compelling and understudied interval in student achievement. It is important that research examine more deeply the potential linkages between, and enactment of, curriculum and instruction expectations across the third, fourth, and fifth grades Student mediation of these linkages seems especially promising. A better understanding of instructional dynamics in relation to the c hanging learning, reasoning, moti vational and emotional capabilities of students is an important step toward understanding and potentially reversing their achievement declines


McCaslin, Burross & Good: Achievement from Grades 3 to 5 15 Acknowledgment This work was supported by a grant from the US Office of Educational Research and Improvem ent (OERI) Grant No. R306S000033. The authors take full responsibility for the work and no endorsement from OERI should be assumed. Thanks to Darrell Sabers for his helpful comments.


Education Policy Analysis Archiv es Vol. 13 No. 1 16 References Berliner, D. C., & Biddle, B. (1995). The manufactured cri sis: Myths, fraud, and the attack on Americas public schools. Reading, MA: Addison Wesley Publishing Co. Carroll, J. B. (1963). A model for school learning. Teachers College Record, 64 723 733. Case, R., & Okamoto, Y., in collaboration with Griffin, S ., McKeough, A., Bleiker, C., Henderson, B., & Stephenson, K. M. (1996). The role of central conceptual structures in the development of childrens thought. Monographs of the Society for Research in Child Development, 61 (1 2, Serial No. 246). Chall, J.S. (1996). American Reading Achievement: Should We Worry? Research in the Teaching of English, 30 (3), 303 310. Coleman, J., Campbell, E., Hobson, C., McPartland, J., Meade, A., Weinfeld, F., & York, R. (1966). Equality of educational opportunity Washing ton, DC: US Department of Health, Education and Welfare. Gassen, S.G. (2003). Ariz. scores below U.S. average on federal test. Arizona Daily Star 14 Nov., A6. Good, T., Burross, H. L., & McCaslin, M. (in press). Comprehensive School Reform: A longitu dinal study of school improvement in one state. Teachers College Record Good, T., & Grouws, D. (1979). The Missouri mathematics effectiveness project: An experimental study in fourth grade classrooms. Journal of Educational Psychology, 71, 355 362. Han ushek, E. (1997). Assessing the effects of school resources on student performance: An update. Educational Evaluation and Policy Analysis, 19 (2), 141 164. Hess, R., & Shipman, V. C. (1965). Early experience and the socialization of cognitive modes in chi ldren. Child Development 36 869 886. Hill, H., Cohen, D., & Moffitt, S. (1999). Instruction, poverty, and performance. In G.Orfield & E. Debray (Eds.), Hard work for good schools: Facts not fads in Title I reform. The civil rights project: Harvard Uni versity (mimeo, pp. 55 76). Hofer, B. K., & Pintrich, P. R. (1997). The development of epistemological theories: Beliefs about knowledge and knowing and their relation to learning. Review of Educational Research, 67 88 140. Jensen, A. R. (1973). Educab ility and group differences New York: Harper & Row. Jorgensen, O. (1999). Arizona AIMS for success. Graduation Standards put learning and diplomas on the line. Clearing House, 73(1) 23 25.


McCaslin, Burross & Good: Achievement from Grades 3 to 5 17 Klaus, P. E. (1973). Yesterdays children: A longitudin al study of children from Kindergarten into the Adult years. A Wiley Interscience Publication. New York: John Wiley & Sons. Ladd, H., & Hansen, J. (Eds.). (1999). Making money matter: Financing Americas schools. Washington, DC: National Academy Press. Luchins, A.S., & Luchins, E. H. (1950). New experimental attempts at preventing mechanization in problem solving. Journal of Genetic Psychology, 42 pp. 279 297. McCaslin, M., Tuck, D., Wiard, A., Brown, B., LaPage, J., & Pyle, J. (1994). Gender composit ion and small group learning in fourth grade mathematics. Elementary School Journal, 94, pp. 467 482. McNeil, L. (2000). Creating new inequalities: Contradictions of reform. Phi Delta Kappan, 81 (10), 728 734. National School Boards Association (1999, Nov ember 29). Ten critical threats to Americas children: Warning signs for the next millennium. A report to the nation presented by The Hospital, Youth Crime Watch of American [online]. Available at Nichols, S., & Good, T. (2004). Americas teenagers myths and realities: Media images, schooling, and the social costs of careless indifference. Mahwah, NJ: Lawrence Erlbaum Associates, Inc. Piaget, J (1983). Piagets theory. In P. Mussen (Ed.), Handbook of child psychology: Vol. 1. W. Kessen (Ed.), History, theory, and methods (pp. 103 128). New York: John Wiley & Sons, Inc. Pogrow, S. (1999). Overcoming the cognitive wall: Accelerating the learni ng of Title I students after the third grade. In G.Orfield & E. Debray (Eds.), Hard work for good schools: Facts not fads in Title I reform. The civil rights project: Harvard University (mimeo). Smith, M. L., Heinecke, W. F. & Noble, A. J. (1999). Assess ment policy and political spectacle. Teachers College Record, 101 (2), 157 191. Stiefel, L., Rubenstein, R., & Berne, R. (1998). Intra district equity in four large cities: Data, methods, and results. Education Finance, 23 (4), 447 467. US Government Print ing Office. (1999, July). Americas children: Key national indicators of well being. (NCES 1999 019). Washington DC: Federal Interagency Forum on Child and Family Statistics.


Education Policy Analysis Archiv es Vol. 13 No. 1 18 Wang, J. (2003). An analysis of item score difference between 3 rd and 4 th gra des using the TIMSS database. Presented at the Annual Meeting of the American Educational Research Association (Chicago, IL, April 21 23, 2003). WestEd (2001). AIMS as a high school graduation requirement: Analysis of public survey data and recommendatio ns. About the Authors Mary McCaslin is a professor of Educational Psychology at the University of Arizona. Her research interests include the co regulation of classroom opportunities for student learning, motivation, and identity. Heidi Legg Burross is adjunct instructional faculty at the University of Arizona. Her research interests include student transit ions and perceptions of performance and achievement. Thomas L. Good is a professor of Educational Psychology at the University of Arizona. His research interests include the study of classrooms, the communication of expectations, and the socialization of youth.


McCaslin, Burross & Good: Achievement from Grades 3 to 5 19 Education Policy Analysis Archives http:// Editor: Sherman Dorn University of South Florida Production Assistant: Chris Murrell, Arizona State University Genera l questions about appropriateness of topics or particular articles may be addressed to the Editor, Sherman Dorn, epaa EPAA Editorial Board Michael W. Apple University of W isconsin David C. Berliner Arizona State University Greg Camilli Rutgers University Linda Darling Hammond Stanford University Mark E. Fetler California Commission on Teacher Credentialing Gustavo E. Fischman Arizona State Univeristy Richard Garlikov Birmingham, Alabama Gene V Glass Arizona State University Thomas F. Green Sy racuse University Aimee Howley Ohio University Craig B. Howley Appalachia Educational Laboratory William Hunter University of Ontario Institute of Technology Patricia Fey Jarvis Seattle, Washington Daniel Kal ls Ume University Benjamin Levin University of Manitoba Thomas Mauhs Pugh Green Mountain College Les McLean University of Toronto Heinrich Mintrop University of California, Berkeley Michele Moses Arizona St ate University Gary Orfield Harvard University Anthony G. Rud Jr. Purdue University Jay Paredes Scribner University of Missouri Michael Scriven Western Michigan University Lorrie A. Shepard University of Colorado, Boulder Robert E. Stake University of Illinois UC Kevin Welner University of Colorado, Boulder Terrence G. Wiley Arizona State University John Willinsky University of British Columbia


Education Policy Analysis Archiv es Vol. 13 No. 1 20 Archivos Analticos de Polticas Educativas Associate Editors Gustavo E Fischman & Pablo Gentili Arizona State University & Universidade do Estado do Rio de Janeiro Founding Associate Editor for Spanish Language (1998 2003) Roberto Rodrguez Gmez Editorial Board Hugo Aboites Universidad Autnoma Metropolitana Xochimilc o Adrin Acosta Universidad de Guadalajara Mxico Claudio Almonacid Avila Universidad Metropolitana de Ciencias de la Educacin, Chile Dalila Andrade de Oliveira Universidade Federal de Minas G erais, Belo Horizonte, Brasil Alejandra Birgin Ministerio de Educacin, Argentina Teresa Bracho Centro de Investigacin y Docencia Econmica CIDE Alejandro Canales Universidad Nacional Autnoma de Mxico Ursula Casanova Arizona State University, Tempe, Arizona Sigfredo Chiroque Instituto de Pedagoga Popular, Per Erwin Epstein Loyola University, Chicago, Illinois Mariano Fernndez Enguita Universidad de Salamanca. Espaa Gaudncio Frigotto Universidade Estadual do Rio de Janeiro, Brasil Rollin Kent Universidad Autnoma de Puebla. Puebla, Mxico Walter Kohan Universidade Estadual do Rio de Janeiro, Brasil Roberto Leher Universidade Estadual do Rio de Janeiro, Brasil Daniel C. Levy University at Albany, S UNY, Albany, New York Nilma Limo Gomes Universidade Federal de Minas Gerais, Belo Horizonte Pia Lindquist Wong California State University, Sacramento, California Mara Loreto Egaa Programa Interdisciplinario de Investigacin en Educacin Mariano Narod owski Universidad Torcuato Di Tella, Argentina Iolanda de Oliveira Universidade Federal Fluminense, Brasil Grover Pango Foro Latinoamericano de Polticas Educativas, Per Vanilda Paiva Universidade Estadual do Rio de Janeiro, Brasil Miguel Pereira Cate dratico Universidad de Granada, Espaa Angel Ignacio Prez Gmez Universidad de Mlaga Mnica Pini Universidad Nacional de San Martin, Argentina Romualdo Portella do Oliveira Universidade de So Paulo Diana Rhoten Social Science Research Council, New Y ork, New York Jos Gimeno Sacristn Universidad de Valencia, Espaa Daniel Schugurensky Ontario Institute for Studies in Education, Canada Susan Street Centro de Investigaciones y Estudios Su periores en Antropologia Social Occidente, Guadalajara, Mxico Nelly P. Stromquist University of Southern California, Los Angeles, California Daniel Suarez Laboratorio de Politicas Publicas Universidad de Buenos Aires, Argentina Antonio Teodoro Universi dade Lusfona Lisboa, Carlos A. Torres UCLA Jurjo Torres Santom Universidad de la Corua, Espaa


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