www.elsevier.com/locate/econedurev
The missing link: an econometric analysis on the impact of
curriculum standards on student achievement
Nicola A. Alexander
*University of Minnesota, 86 Pleasant Street S.E., Minneapolis, MN 55455-0221, USA
Received 15 December 1997; accepted 26 October 1998
Abstract
Nationwide, state policy-makers have increasingly adopted curriculum standards as a means of improving education. However, relatively little empirical research has been done to investigate if a link actually exists between curriculum standards and student performance. Using data collected by the New York State Department of Education, the impact of standards on high school student achievement is examined through estimation of a one-way fixed-effects model of the education production function. Curriculum standards are operationalized as the award of a “50–64” variance, which is considered a means of increasing the number of students taking demanding courses. The findings suggest that curricu-lum standards can improve student performance, but they do little to improve equity. While larger portions of pupils pass curriculum-based assessment exams, continued associations between select student characteristics and student performance remain. 2000 Elsevier Science Ltd. All rights reserved.
JEL classification: JEL I21
Keywords: Curriculum standards; Student achievement; Educational policy; “At-risk” pupils
1. Introduction
1.1. Overview
American public elementary and secondary schools were branded mediocre after the publication of A Nation at Risk in 1983 (National Commission on Excellence in Education, 1983). The educational conditions high-lighted by this report sparked a series of reform efforts across the nation, many of which called for additional aid to public schools (Odden, 1990). Despite increased funding, criticism of schools persisted and even intensi-fied. Many practitioners and academics questioned the notion that expending more money on the educational status quo would necessarily lead to improved outcomes (Hanushek, 1989, 1991).
* Tel.:+1-612-624-1507; fax:+1-612-624-3377.
E-mail address: [email protected] (N.A. Alexander).
0272-7757/00/$ - see front matter2000 Elsevier Science Ltd. All rights reserved. PII: S 0 2 7 2 - 7 7 5 7 ( 0 0 ) 0 0 0 0 3 - 0
Decision-makers in some states targeted selected school inputs (e.g. faculty, organization, curriculum) as areas in need of a major overhaul. Policy-makers dis-cussed the use of merit pay (Odden & Kelley, 1996), decentralization (Weiler, 1990; Odden, 1994) and more rigorous coursework (Altonji, 1994). Lately, however, input-oriented strategies are being replaced by an empha-sis on results (Monk, 1994).
The imposition of curriculum standards1
reflects these dual approaches (input and output) to improving
edu-1 Curriculum reform encompasses two key issues: (1)
cation. That is, in its early conceptualization, curriculum standards were associated with a prescriptive curriculum (NYS Department of Education, 1984) but later evolved into a more flexible, results-oriented strategy (NYS Department of Education, 1991). An assumed benefit of either approach is that promotion of a rigorous curricu-lum for all pupils will also reduce the gaps in perform-ance between different student groups. That is, if a rigor-ous curriculum yields high student performance then ensuring that all students participate in more demanding courses will generate improved achievement for all.
In recent years, proponents of curriculum standards have been at the forefront of educational reform, yet the literature remains relatively silent on the impact of this approach on educational outcomes. Little is known about the differential effects of curriculum reform on different cohorts of the student population. For instance, what are the impacts of curriculum standards on course selection and school attendance of at-risk pupils? Will there be a trade-off between improved performance on the part of some students (e.g. increased college attendance) and lower retention rates on the part of others? Is there a risk that the “educational bar” will be raised too high? With the widespread adoption of systemic education reform by states, there is a growing need for answers to these questions.
The primary objective of this research is to examine how curriculum standards affect student achievement. The underlying logic of the policy of curriculum stan-dards encompasses two assumptions. First, the types of courses that students take affect their educational achievement. Second, policy-makers know what types of courses lead to better outcomes and can prescribe or influence the time allotted to these “better” courses. If the first assumption is true, differential access to the cur-riculum becomes very important, particularly on equity grounds, and the relevance of curriculum standards becomes clearer.
This study is part of a larger body of research on cur-riculum standards. It continues the inquiry begun in an earlier paper, which explored changes in student class time spent on particular areas of the curriculum (Alexander, 1996). That descriptive look at the trends in course-taking patterns provides the context in which the behavioral model linking course-taking patterns, curricu-lum standards, and student achievement is developed. This study examines the relationships between curricu-lum standards and student achievement using pooled data on public school districts in New York State for the past 6 years, 1989/90 through 1994/95.
1.2. The issues
Much of the research on tracking has found that the quality of the curriculum to which a student is exposed has an impact on the quality of learning that takes place
(Oakes, 1985; Vanfossen, Jones & Spade, 1987). This influence is often mediated through the impact that cur-riculum tracks have on the choice of courses selected by students (Lee & Bryk, 1988). This influence is above and beyond and even greater than the impact of prior aca-demic performance and interests (Vanfossen et al., 1987). Course-taking patterns in turn influence how much students learn of subjects such as mathematics, science, or business, and also how much practice they obtain in reading and vocabulary (Vanfossen et al., 1987). Consequently, many authors contend that students in non-academic tracks are not given an environment that encourages them to increase their performance and their educational and occupational aspirations (Oakes, 1985; Vanfossen et al., 1987). They also note that too often poor, minority students are over-represented in these low, special, or vocational tracks (Page & Valli, 1990, p. 2).
This line of argument implies that the more knowledge to which a student is exposed, the more that student will remember in absolute terms. An example will illustrate this point. Let us assume that an academic curriculum provides three times the “knowledge” of a low-track cur-riculum. Thus, remembering 50% of the academic course work produces absolutely more “knowledge” than remembering 100% of the less-challenging material, all else being equal. This assumption is supported by the work of Alexander and Pallas (1984). These authors find that the test scores of students who complete the “New Basics”2 are considerably higher, on average, than of those who do not. However, these findings may overstate the influence of taking a challenging curriculum. That is, while Alexander and Pallas note that “better” students are likely to take more challenging courses, they only control for different innate abilities by including a pre-dictor variable for prior performance. The authors do not adequately address the issue of selection bias.
Bishop (1993, 1994, 1996) argues that policy-makers can greatly influence the quality of schooling for all stu-dents if they make use of the appropriate signals and incentives. According to the author, increased reliance on sound high school education by employers and insti-tutions of higher learning will act as a signal to those involved in the educational process (parents, teachers, students). Moreover, external curriculum-based assess-ments in specific high school subjects will increase the students’ rewards for learning. Bishop contends that this combination of signals and rewards will persuade the
2 The “New Basics” include four units of English, three units
student to choose more demanding courses and to work harder in them (Bishop, 1994, p. 2).
Even when one considers curriculum-based assess-ment as the route to improved student performance, the success of this strategy is linked to its ability to induce students to take substantive, rigorous courses (Bishop, 1994). While the educational process is not highly conscribed under this approach, the crux of the policy remains the assumed link between curriculum quality and student performance. Consequently, this paper cate-gorizes as examples of curriculum standards those edu-cation strategies that explicitly or implicitly aim to impel students to take more traditionally challenging courses.
Standards proponents hope that exposing students to challenging material will start a chain reaction of bene-fits, where both curriculum mastery and post-secondary opportunities are improved. The benefits sought, how-ever, are not just the tangible ones of increased college attendance or pass rates in selected subjects. It is assumed that there are also intangible benefits to be gained from increased exposure to higher levels of knowledge. These intangibles may include the acquire-ment of better conceptual and analytical skills, as well as improved self-confidence. Well documented in the literature is the focus on problem-solving and leadership skills commonly provided to students in academic tracks. This contrasts with the more control-oriented approach that marks the learning experience of pupils in non-aca-demic tracks (Oakes, 1985).
1.2.1. The link between curriculum standards and student performance
The effect of curriculum standards on student perform-ance can be explored through the estimation of a simple education production function. Generic measures of edu-cational outcomes (i.e. college attendance, retention rates, and diploma type) are regressed on district, school, and classroom characteristics. Similarly, more specific measures of student performance, such as pass rates in particular subjects, are also regressed on “inputs” in the schooling production process. In both the generic and subject-specific models of student outputs, curriculum standards policy is entered as an input, holding other educational factors constant. Three expectations emerge regarding the relationship between student outcomes and implementation of curriculum standards as given below.
Hypothesis I. There will be a positive association
between the implementation of curriculum standards policy and generic student outcomes.
Hypothesis II. There will be a positive association
between the implementation of curriculum standards policy and subject-specific student outcomes.
Hypothesis III. The magnitude of the association
between demographic characteristics and student
out-comes will be lessened after the implementation of curriculum standards policies.
1.2.2. Curriculum standards in NYS
New York State provides a good natural experiment for evaluating the effectiveness of curriculum standards policies as a means of improving student outputs. Policy-makers in that state have long relied on curriculum stra-tegies as part of the agenda of educational reform. Poli-cies have ranged from a prescriptive approach detailing the types of courses to be taken to curriculum-based assessment, where student performance on exams is key. More recently, educators have tried to increase the num-ber of students taking a demanding curriculum by reduc-ing the procedural burden of double testreduc-ing borne by schools (and some pupils) when “marginal” students fail a Regents exam. That is, school districts could request a variance from the educational regulations in order to develop a “safety net” for students whose scores fell just short of passing. This safety net would allow those stu-dents who marginally failed the Regents exam (i.e. scored between 50 and 64%) to earn a passing grade in the corresponding subject on the less challenging, Regents Competency Test (RCT). District leaders felt that without this variance, there was a disincentive for “marginal” pupils to take Regents courses because if they failed the Regents examinations they still needed to demonstrate competency by taking the RCT.
While the burden of double testing borne by students is important, it is the perception held by school/district leaders regarding this burden that is germane to this study. Recall the underlying notion of curriculum stan-dards regarding a link between curriculum quality and student performance: curriculum quality affects student outcome. Since it is school administrators who often determine the rigor of the curriculum to which a student is exposed, their choices are key to a voluntary standards strategy. The more burdensome that these policy-makers find it to double test students, the less likely that “mar-ginal” pupils will be encouraged to take the more demanding curriculum and associated exams. By remov-ing the procedural cost of double testremov-ing, state policy-makers are essentially removing some of the costs asso-ciated with encouraging all students to take demanding courses. If the additional benefits of more students taking a rigorous curriculum outweigh the additional cost of receiving a variance (i.e. annual reports, smaller classes, teacher training), then school administrators are likely to encourage students to take more demanding courses.
Table 1
Total number of variances by year and by subject, 1990–1995 Subject
Year Biology English Earth Science Global Studies Math I US History Any
1990 0 0 0 0 0 0 0
1991 0 0 0 0 0 0 0
1992 2 3 5 9 9 9 9
1993 8 61 59 71 68 73 76
1994 33 123 110 127 125 134 142
1995 55 194 188 199 203 218 239
Table 2
Distribution of variances among districts
Number of variances, 1990–1995
1 2 3 4 5 6
Number of districts 15 14 14 37 122 37
(n=239)
Similarly, for those subjects where those students who fail the Regents exam are considered unlikely to get between 50 and 64%, the benefit of a variance is nulli-fied. Where students are likely to pass the Regents exam (i.e. a score of 65 or higher), there is no variance needed. These results are reflected in the data presented in Tables 1 and 2. Note that the data in the tables only reflect variance information for those districts in the 6-year panel. The information in Table 1 illustrates the number of districts3that exchanged the burden of “dou-ble-testing” for the requirements associated with receiv-ing a 50–64 variance. It may be assumed that there was a concomitant increase in the number of students taking the more demanding course in the varianced subject. Note that New York City is excluded from the analysis because of data limitations.
In 1992, only nine schools districts had received a “50– 64” variance in at least one subject; by 1995, this number had increased to 239. Most of these districts were issued variances for US history (218 districts). By contrast, less than one-third of the districts (n=55) were awarded a variance for biology. These data suggest that the additional costs of exposing more students to a demanding curriculum are higher for biology than for US history. By extension, more “marginal” students were
3 Because this study focuses on high schools, the
infor-mation provided in school level data is essentially that for dis-tricts. Exceptions are for NYC, Buffalo, Rochester and Syra-cuse, which have multiple high schools.
more likely to be encouraged/mandated to take a demanding US history course than would be for biology. The data shown in Table 2 present information on the distribution of variances among school districts from 1989/90 through to 1994/95. Most school districts tended to be awarded “50–64” variances in five subjects, often-times omitting biology. Rarely did school districts receive variances in three or fewer subjects. These find-ings suggest that few additional resources are needed to enjoy the benefits of the fifth variance after work has been done to bring about variances for four subjects. It is important to note, however, that even in those districts where a “50–64” variance was awarded, students still had the option to take the RCT exam. Consequently, information on the association between standards policy and retention rates may not be very telling for contexts when this option is removed.
specific subjects over the time frame studied have chosen to apply for exemption. It is therefore possible that the factors that led these districts to adopt this strategy would have also led to higher student achievement in the absence of said policy.
Assume, for instance, that school districts with more “effort” have higher student performance even without this curriculum initiative. Assume also that “effort” is one of the factors that drive districts to apply for a vari-ance (see Fig. 1). Excluding this variable from the model thus leads to an upward bias in the estimation of the impact of curriculum standards on student achievement. Concerns regarding selectivity bias are especially rel-evant to this study because of the historical and insti-tutional processes that led to the formal institution of the new policy on standards in New York State.
In summary, there are several reasons that may have pushed districts to make this appeal for a “50–64” vari-ance, and each may have an individual impact on student performance. For instance, parents may have forced higher standards into schools. Many of the early requests came from relatively wealthy suburban districts where a majority of their students already take the Regents exam. School districts may have had sufficient institutional “slack”. Being granted a variance obliged school districts to make annual reports to the NYS Department of Edu-cation and often entailed having smaller classes and improved teacher training.4 In addition, the receipt of variances by districts may have induced neighboring dis-tricts to apply for the same. Besides the above district-specific attributes, time and the increased emphasis nationwide on student performance may have contrib-uted to changes in educational output.
1.2.4. Controlling for district-specific influences and historical trends
One way of isolating the effect of curriculum stan-dards on student achievement is to adopt a one-way fixed effects model and to include a variable that represents each year of the sample. This strategy controls for
dis-Fig. 1. Tracing the true impact of curriculum standards.
4 This information comes from anecdotal evidence and
dis-cussions with Nick Argyros of the NYS Department of Edu-cation, who handles the awarding of the variances in question.
trict-specific characteristics by creating dummy variables for districts in the analysis. Similarly, inclusion of a dummy variable for each year allows historical trends to be taken into account. These strategies allow the model to address concerns that apparent effects of curriculum policy on student achievement may be merely historical artifacts or caused by particular district attributes. Thus, average student performance in a district (Ai) is thought
to be a function of district-specific characteristics (Di),
time (yearbi), a vector of explanatory variables (ci), and
an error term (ei):
Ai5aiDi1bici1ei
Note that it is assumed that implementation of the variance is immediate. As indicated, school districts had to have resources in place and to have presented a viable plan of action to the NYS Department of Education in order to gain approval for their variance request.5
2. Institutional features of NYS
2.1. Research population
New York State is the only state with a long-standing reliance on a curriculum-based examination system covering the majority of high school graduates. In the fall of 1995, the high school student population of New York State, excluding NYC, comprised 1.9% Asians, 7.8% blacks, 3.8% Latinos, 0.004% Native Americans, and 86.1% whites (NYS Department of Education, 1995). The relative diversity of the population allows investigation of the associations between socioeconomic characteristics, curriculum policy, and student outcomes. The following analysis focuses on the population of public school students in grades 9–12 in New York State, excluding New York City.6Those grades are examined because much of the discussion on curriculum standards centers on high school students. To the extent that cur-riculum reform has some universal effects, the findings of this study may have important implications for the rest of the students and to the nation as a whole. The definitions and simple statistics weighted by high school
5 Ideally, a lag of the standards policies would be included to
reflect that the full effect of these strategies is not instantaneous. Instead, the impact of these polices may be distributed over a period of years and would be more appropriately captured using distributed-lag models. Given the shortness of the panel and other data limitations, this option was not possible.
6 As indicated, there were problems surrounding data on
student population of the variables used in the analyses are presented in Table 3.
3. Findings
The data in Table 4 are the estimates derived for the models of generic measures of student performance as outputs in the education production process. The general measures of achievement used are 4-year college attend-ance, diploma type, and retention rates. Dummy vari-ables are used to capture the award of a variance in any subject. Interaction variables were created between the student demographic variables and the receipt of a vari-ance to explore if approved varivari-ances altered the associ-ation between the profile of schools and student out-comes. Faculty, district size, school diversity and organization are also included as controls. Most of the background variables have the expected associations with educational outcomes.
Even when non-policy variables (e.g. historical trends, diversity of the student body, etc.) are controlled for, the association between the award of a subject variance and portion of graduates with Regents diplomas remains. The data suggest receipt of a variance causes the portion of graduates receiving Regents diplomas to increase by 0.5%. This represents a 1.87% increase over pass rates before this strategy was pursued. However, the results also indicate that this curriculum policy is not as relevant for retention rates or the rates at which graduates sub-sequently attend 4-year colleges.
The estimates imply that increased portions of black and Latino students are associated with lower portions of Regents diplomas granted. Poverty considerations do not play a big role in any of the generic educational out-comes. Moreover, there is no substantial difference in the link between school profile and educational achievement before and after applying a variance. That is, curriculum standards policies do not seem to affect the relationship between race, poverty, and student outcomes.
By contrast, subject variances matter for pass rates (i.e. 65%) in Regents Global Studies, Regents English, and Regents Biology. Approved variances in those sub-jects resulted in an increase in the portion of students passing; this increase was by 1.68, 1.88, and 3.96% for global studies, English, and biology, respectively. The data in Table 5 are the estimates derived for subject-specific models of curriculum policy. Educational out-puts are defined as the percentage of enrolled students passing the Regents in a particular field. The standards are also categorized according to specific subjects, where biology variances are distinguished from English vari-ances, math varivari-ances, and so on.
To facilitate presentation, Table 5 does not specify the class size nor the education and the experience of the faculty for each Regents course. Instead, a generic
for-mat is adopted for the table where the average size of the Regents class culminating in the specified exam is represented by “Course class size”. Thus, when the dependent variable is the percentage of average 9–12 grade enrollment passing biology, “Course class size” is the average class size for Regents Biology. Similarly, the average size of the 10th grade class in a particular field is represented by “Average subject size”. When the dependent variable is the percentage of average 9–12 grade enrollment passing biology, “Average subject size” is the average size of a 10th grade science class in the district. The experience and education of the faculty teaching the Regents courses culminating in the specified exams are represented by “Average experience of course teacher” and “Course faculty with masters”, respectively. Thus, when the dependent variable is the percentage of average 9–12 grade enrollment passing biology, “Aver-age experience of course teacher” reflects the aver“Aver-age number of years of teaching experience of faculty teach-ing Regents Biology. “Course faculty with masters” rep-resents the portion of the faculty teaching Regents Biology that has a master’s education or above. Similar procedures were followed for all other subjects.
Given the percentage points noted above and the mean pass rates of global studies, English, and biology, the effect of awarding variances becomes more meaningful. Absence of approved variances in those subjects would likely result in 3% fewer students passing global studies, 3.4% fewer students passing English, and a striking 10.2% fewer students passing biology. These improved pass rates suggest that implementation of a standards policy can influence curricula mastery.
4. Discussion and implications for public policy
This 6-year study has traced the effect of awarding variances on average student performance in all public school districts in New York State, excluding New York City. It is presumed that pursuit of this policy is reflec-tive of broader standards-based plans. The results of this study yield mixed reviews on the impact of educational policies relying on using curriculum standards as a means of reforming education.
Table 3
Definitions and simple statistics of the variables used in this study Variable N Mean weighted Standard Definitions
by 9th–12th deviation grade student
population Curriculum policy
STDS 3864 0.0664 0.249 dummy variable representing receipt of any subject variance; coded 1 if received; 0 otherwise
BISTD 3864 0.0174 0.131 dummy variable representing receipt of variance in Regents Biology; coded 1 if received; 0 otherwise
ENSTD 3864 0.0528 0.223 dummy variable representing receipt of variance in Regents Comprehensive English; coded 1 if received; 0 otherwise
GSSTD 3864 0.0544 0.226 dummy variable representing receipt of variance in Regents Global Studies; coded 1 if received; 0 otherwise
MASTD 3864 0.0550 0.228 dummy variable representing receipt of variance in Regents Mathematics I; coded 1 if received; 0 otherwise
USSTD 3864 0.0584 0.235 dummy variable representing receipt of variance in Regents US History; coded 1 if received; 0 otherwise
Management
AVGEN 3822 21.41 3.072 average number of students in a 10th grade English class AVGMA 3616 20.65 3.57 average number of students in a 10th grade mathematics class AVGSC 3793 20.81 3.21 average number of students in a 10th grade science class AVGSOC 3833 21.78 3.09 average number of students in a 10th grade social studies class HIGHT 3893 1657 1884 number of public school children in 9–12 grades in the district SIZEBI 3718 22.9 89.32 average number of students in a Regents Biology class
SIZEEN 3755 21.3 87.79 average number of students in a Regents Comprehensive English class SIZEGS 3793 22.1 82.20 average number of students in a Regents Global Studies class SIZEMA 3857 22.5 82.13 average number of students in a Regents Math I class SIZEUS 3812 21.9 84.99 average number of students in a Regents US History class
PCENT 4450 0.076 0.081 portion of administrative personnel assigned to the central office relative to total administrative personnel; used as a proxy for degree of centralization Faculty
EXPTOT 3893 19.4 2.67 average number of years of teaching experience of faculty teaching 9th–12th grade in the district
EXPBI 3695 19.87 6.29 average number of years of teaching experience of faculty teaching Regents Biology
EXPEN 3740 20.88 5.30 average number of years of teaching experience of faculty teaching Regents level English
EXPGS 3773 19.49 6.25 average number of years of teaching experience of faculty teaching Regents Global Studies
EXPMA 3855 20.41 4.68 average number of years of teaching experience of faculty teaching Regents Math I
EXPUS 3807 21.65 5.81 average number of years of teaching experience of faculty teaching Regents US History
MASTER 3755 0.74 0.15 dummy variable representing the portion of teachers with a master’s degree or above; coded 1 for teachers with at least a masters; 0 otherwise
MASTBI 3718 0.777 0.300 dummy variable representing the portion of faculty teaching Regents Biology with a master’s degree or above
MASTEN 3755 0.763 0.287 dummy variable representing the portion of faculty teaching Regents Comprehensive English with a master’s degree or above
MASTGS 3793 0.734 0.297 dummy variable representing the portion of faculty teaching Regents Global Studies with a master’s degree or above
MASTMA 3857 0.756 0.246 dummy variable representing the portion of faculty teaching Regents Math I with a master’s degree or above
MASTUS 3812 0.77 0.291 dummy variable representing the portion of faculty teaching Regents US History with a master’s degree or above
Table 3 (continued)
Variable N Mean weighted Standard Definitions by 9th–12th deviation
grade student population Diversity
PADV 3893 0.022 0.016 portion of courses that are devoted to an advanced curriculum, including Advanced Placement and college credit courses; used as a proxy for portion of high achievers
PMIN 3893 0.073 0.153 portion of district’s 9th–12th grade student population comprised of black and Latino students
SRPMA 4087 91.34 4.47 percentage of students scoring above the NYS reference point for mathematics; used as a proxy for degree of preparedness
SRPRD 4087 96.71 7.41 percentage of students scoring above the NYS reference point for reading; used as a proxy for degree of preparedness
WELF 3893 0.127 0.125 portion of children from families whose primary means of support is a public welfare program. Data originally coded in ranges; study uses midpoint of that range; used as a proxy for poverty
Outcomes
P4YR 3868 0.429 0.16 portion of high school graduates planning to attend a 4-year college PAGBI 3919 39.13 19.08 percent of average grade enrollment (9–12) passing Regents Biology PAGEN 3919 55.20 18.78 percent of average grade enrollment (9–12) passing Regents Comprehensive
English
PAGGS 3919 55.87 17.20 percent of average grade enrollment (9–12) passing Regents Global Studies PAGMA 3919 40.26 20.43 percent of average grade enrollment (9–12) passing Regents Mathematics PAGUS 3919 42.05 20.49 percent of average grade enrollment (9–12) passing Regents US History PRDIP 3873 0.301 0.078 portion of regular degree graduation candidates that received the more
prestigious Regents Diploma
RETENT 3954 0.97 0.022 the portion of students currently in school as a portion of previous 9–12 enrollment; calculated as 12the dropout rate
there is more that can be done to improve student per-formance in that area.
The results are promising with regard to the effect on curricula mastery of receiving a variance. In three of the five subjects examined, awarding a subject-specific vari-ance appears to have a positive and significant associ-ation with the portion of enrolled students passing selec-ted subjects. This positive association is there even after district-specific attributes are controlled for. Neverthe-less, the typical pass rates in these subjects remain low— ranging from 39.1% of students for Regents Biology to 55.9% for Global Studies. If it is assumed that it is the discipline and other intangible interpersonal skills trans-ferred during high school that is important to post-sec-ondary activities, then these low rates may not be very important. If, however, it is assumed that the skills trans-ferred during classes have more practical and direct implications, then this limitation becomes more substan-tial.
As currently designed, standards policies do not appear to affect the relationship between race, poverty, and student outcomes. There remains substantial associ-ations between student achievement and the portion of poor and/or minority students attending school. These
findings suggest that the level of preparedness provided to students in schools with high portions of disadvan-taged students is inadequate to meet the challenges of the new standards. Consequently, the transfer costs involved in switching from the status quo to the new system are potentially higher for these schools, suggest-ing the need for a higher level of initial commitment to these areas. This strategy seems to be consistent with a recent conceptual proposal for state aid to schools made by the Board of Regents. In that document, it was rec-ommended that Basic Operating Aid be calculated in a manner that provides more aid to medium and low-wealth districts with demonstrated high-tax effort. The recommendations also included the provision of sup-plementary funding for the purchase of additional instructional materials required to implement new cur-riculum standards (NYS Department of Education, 1996, pp. 1–2).
Table 4
The relationship between imposition of at least one variance and generic educational outcomes, 1989/90 through 1994/95a
Four-year college Portion of regents Retention rate
attendance diplomas
Award of any subject variance 0.004 0.005* 20.0006
(0.006) (0.003) (0.001)
Portion advanced 0.386*** 0.477*** 0.013
(0.148) (0.078) (0.031)
Portion minority 20.046 20.080** 20.002
(0.065) (0.034) (0.014)
Portion poor 0.002 20.003 0.009
(0.022) (0.011) (0.004)
Degree of centralization 20.037 20.034** 20.003
(0.031) (0.016) (0.006)
Average size of English/100 0.232 0.065 0.033
(0.003) (0.001) (0.067)
(Average size of English/100)2 20.004 20.002 20.0007
(0.008) (0.004) (0.002)
Average size of math/100 20.12 0.117 0.010
(0.194) (0.102) (0.041)
(Average size of math/100)2 0.003 20.002 20.00005
(0.005) (0.003) (0.001)
Faculty with masters 20.015 0.006 0.001
(0.024) (0.012) (0.005)
Average faculty experience 0.013** 0.006* 0.003***
(0.006) (0.003) (0.001)
(Average faculty experience)2
20.0004** 20.0002** 20.00009
(0.0002) (0.00009) (0.00003)
High school enrollment per 100 0.005** 0.001 20.0003
(0.002) (0.001) (0.0004)
(High school enrollment per 100,000)2
20.0005*** 20.0001 0.0001***
(0.000) (0.0000) (0.000)
Degree of preparedness/MA 20.0003 0.0001 20.000005
(0.0003) (0.0002) (0.00007)
Degree of preparedness/Eng 20.00008 20.00004 0.00006
(0.0002) (0.0001) (0.00004)
Year 0.012*** 0.004*** 0.0017
(0.007) (0.0004) (0.0002)
Portion minority×award of any variance 20.014 20.011 0.002
(0.021) (0.011) (0.004)
Portion poor×award of any variance 20.033 20.011 0.009
(0.036) (0.019) (0.007)
n (DF) 3470 (652, 2818) 3471 (652, 2819) 3474 (2822, 652)
R2 0.916 0.902 0.808
F value 47.14 39.58 18.25
Prob.F 0.0001 0.0001 0.0001
a Key: *: significant ata=0.1; **: significant ata=0.05; ***: significant ata=0.01. Standard errors are in parentheses.
standards policies appears to improve overall student output, it does not seem to address equity concerns quite as well. Finally, the results of this study imply that there is a role for standards in the educational arena; state pol-icy-makers need to decide the nature of that role. Below are three recommendations to consider.
1. Foster partnerships between secondary schools
and tertiary institutions. This is suggested by the
insignificant association between college attendance and the receipt of a variance.
2. Address the issue of social promotions. This is sug-gested by the low mean pass rates in the selected sub-jects, as well as the insignificant effect of awarding variances on the pass rates in Math I and US History. 3. Target funding to high-minority/high-poverty
dis-tricts. This is suggested by the insignificant
Table 5
The relationship between subject-specific variance and the percentage of enrolled students passing regents exams in selected subjects, 1989/90 through 1994/95a
Percent of enrolled students passing
Global studies English Mathematics I Biology US History Imposition of subject variance 1.685** 1.867** 20.423 3.96*** 21.54
(0.752) (0.821) (1.07) (1.77) (0.967)
Portion advanced 29.66 218.22 2132.02 2137.7*** 296.58***
(26.2) (28.44) (37.22) (35.12) (34.7)
Portion minority 24.34 234.17*** 27.97* 6.24 240.56***
(11.43) (12.34) (16.39) (15.32) (15.12)
Portion poor 0.946 22.5 20.363 1.8 2.91
(3.75) (4.04) (5.43) (5.1) (4.98)
Degree of centralization 25.69 24.70 26.60 25.3 24.92
(5.2) (5.66) (7.60) (7.04) (7.0)
Course class size 0.962 0.542 0.021 2.74*** 1.91**
(0.816) (0.462) (0.285) (0.628) (0.689)
(Course class size)2 20.014 20.0048 0.00036 20.057*** 20.0425
(0.019) (0.011) (0.0039) (0.014) (0.016)
Average subject size 0.838 0.369 20.502 20.328 20.073
(0.826) (0.595) (0.487) (0.614) (0.642)
(Average subject size)2 20.022 20.013 20.018 0.011 20.0006
(0.019) (0.014) (0.012) (0.015) (0.015)
Faculty with masters 1.64 1.13 6.03 0.571 3.3
(4.24) (4.55) (6.18) (5.65) (5.56)
Course faculty with masters 21.07 20.627 1.15 2.20 21.11
(1.11) (1.25) (2.32) (1.53) (1.63)
Average faculty experience 20.648 20.14 21.46 23.31** 1.91
(1.10) (1.22) (1.61) (1.49) (1.45)
(Average faculty experience)2 0.010 0.009 0.032 0.076
20.055
(0.029) (0.032) (0.042) (0.039) (0.038)
Average experience of course teacher 0.554*** 20.232 20.867* 0.109 20.342
(0.173) (0.237) (0.46) (0.258) (0.268)
(Average experience of course 20.014*** 0.004 0.022* 20.003 0.005
teacher)2 (0.005) (0.006) (0.057) (0.007) (0.007)
District high school population 20.0002 0.005 20.002 0.004 0.010*
(0.004) (0.004) (0.005) (0.005) (0.005)
(District high school 0.034 20.008 0.03 20.02 20.092**
population)2×10,000 (0.032) (0.035) (0.046) (0.043) (0.043)
Degree of preparedness/MA 20.025 0.285*** 20.003 20.072 20.071
(0.059) (0.064) (0.97) (0.078) (0.078)
Degree of preparedness/EN 0.038 20.075* 0.028 0.024 0.037
(0.038) (0.041) (0.596) (0.050) (0.050)
Year 1.492*** 0.733*** 22.71*** 22.98** 23.68***
(0.135) (0.152) (0.195) (0.179) (0.180)
n (DF) 3671 (654,3017) 3606 (650,2956) 3517 (653,2864) 3564 (653,2911) 3669 (654,3015)
R2 0.754 0.757 0.661 0.655 0.694
F value 14.11 14.13 8.56 8.45 10.48
Prob.F 0.0001 0.0001 0.0001 0.0001 0.0001
outcomes, as well as the continued significant association between ethnicity and student achievement.
Acknowledgements
This paper is a modified version of the policy brief prepared for the 1997 Educational Finance Symposium sponsored by the New York State Board of Regents, Albany, NY on 28 October 1997. Some revisions were done while at Florida Atlantic University.
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