A quick glance at the numbers seems to state the obvious: pre-kindergarten (pre-k) numbers are highest in areas with the highest population. However, a closer look shows in certain circumstances, this is not the case. Rather, the larger issue appears to be the gap that exists between the number of children enrolled in pre-k versus the number of children enrolled in kindergarten.

It should also be noted there are several school districts throughout the region that do not offer pre-kindergarten through the public school district. This occurs not only in the region, but throughout the state because Michigan does not mandate pre-k, despite the positive effects shown by participation in the program.

In this post we examine the number of students enrolled in pre-k classes and kindergarten classes across the region to show where gaps exist.

As noted above, there are several districts in the region that do not offer pre-kindergarten classes. The majority of these districts are located in the more rural areas, such as Monroe and Livingston counties. St. Clair County, which is also rural, has low participation in pre-k. The Port Huron and East China school districts are the only districts within St. Clair County with more than 50 children enrolled in pre-k. The Port Huron School District covers both the city of Port Huron and Port Huron Township, while the East China School District welcomes students from Marine City, the city of St. Clair, St. Clair Township, China Township, East China Township and Cottrelleville Township. Even though the East China district covers so many communities, it only had about 25 more children participate in pre-kindergarten than larger single community school districts like Dearborn City Schools. In Dearborn, 30 students were enrolled in pre-kindergarten from 2012-13 and in East China 56 students were enrolled.

The Village of New Haven, which has a smaller population than the City of Dearborn and many of the townships encompassed by the East China School District, had 85 students enrolled in pre-kindergarten. The Great Start Readiness Program, which is a larger feeder for pre-k programs, is based on income eligibility. According to the guidelines, households trying to enroll children in the pre-k through this program need to be at at least 100 percent of the poverty level. This shows why districts such as the New Haven Schools enroll more students per capita than places such as Anchor Bay School District (both are located in Macomb County).

Both the chart and map above show how large the gap is between pre-k and kindergarten enrollment. Even in the Detroit City Public Schools, which had the highest pre-k enrollment in the region at 409, kindergarten enrollment (4,144) was 90 percent higher.

The importance of pre-k enrollment cannot be overstated. Research has shown that it has effects on students’ readiness to learn in elementary school and beyond. According to the Center for Public Education, children who participated in pre-k, rather than being in daycare, scored better on math and reading exams later in life. As noted before, pre-k is not mandated in the State of Michigan.

In 2012, The Bridge Magazine wrote a series of stories for their feature piece “The Forgotten 30,000.” These articles detail the importance of pre-k education and discuss the gap between pre-school and kindergarten attendance. Even with Michigan’s $65 million reinvestment in the Great Start Readiness Program, it is clear that hundreds of children in Southeast Michigan are not receiving the early education that many feel is necessary for greater academic success later in life.

We noted in a previous post that students in Michigan and Detroit post weaker performances on the National Assessment of Educational Progress (NAEP) than states across the country, particularly Minnesota. For many years, researchers have attempted to identify factors associated with NAEP scores, which would be of considerable interest to stakeholders who want to address Michigan and Detroit’s NAEP performance. Here, we will briefly summarize some of these factors and selected research addressing them.

For several reasons, NAEP scores in mathematics and reading have been of primary interest to researchers. Much of the research on NAEP score predictors, therefore, focuses on performance in these two subject areas.

Given the primacy of demographic factors such as race, ethnicity, and gender in education research, researchers have also asked whether these variables might predict students’ NAEP performance. For example, Vanneman et al. (2009) and Hemphil & Venneman (2011) noted achievement gaps in NAEP mathematics scores between African-American and White students and between White and Hispanic students. A number of peer-reviewed studies also identify race as a factor in NAEP results (Tate, 1997; Fuchs & Reklis, 1994; Thomas & Stockton, 2003). Some studies explore this factor at a greater depth; for example, Card & Rothstein (2007) attribute the race/ethnicity gap (though using SAT, not NAEP scores) to racial segregation of particular geographic areas, while Lubienski (2006) finds that varying test modes for NAEP mathematics appears to have little or no impact on performance.

There is less evidence for the influence of gender on NAEP scores (Abedi & Lord, 2001; Tate, 1997; Hyde & Linn, 2006; Guthrie et al., 2001), though Thomas and Stockton (2003) identify a small positive relationship between female students and NEAP reading scores and McGraw and colleagues (2006) find a negative relationship between female students and NAEP mathematics scores.

The results are also fairly consistent for socioeconomic status (SES). Biddle (1997) and McQuillan (1998) find a negative relationship between poverty and NAEP scores while Abedi & Lord (2001) and Nelson et al. (2003) find a negative relationship between Free lunch/Aid to Families with Dependent Children (AFDC) status and NAEP scores. Byrnes (2003) and Fuchs & Reklis (1994) find a positive association between parental education levels and students’ 12th and 8th grade NAEP math scores, respectively. Using 1996 NAEP data, Lubienski (2002) finds that SES factors such as parent education and number of literary resources in the home do not explain the African-American/White achievement gap discussed above. Inherent in these studies is, of course, the selection and validity of individual-level or school-level (e.g., Title I designated school) definitions of SES (Thomas & Stockton, 2003).

Some researchers have also considered other literacy-related factors and their possible effect on NAEP scores. For instance, Abedi et al. (2001) and Abedi and Lord (2001) find that English Language Learner (ELL) and Limited English Proficiency (LEP) statuses are negatively related to NAEP mathematics performance. Length of stay in the United States appears to be positively associated with NAEP mathematics performance (Abedi et al., 2001). Access to printed reading material (McQuillan, 1998) and access to school and public libraries (Krashen et al., 2012) also appear to be positively associated with NAEP reading scores.

In general, coursework and related preparation seem to be consistent predictors of NAEP scores. Tate (1997), Abedi & Lord (2001), and Abedi et al. (2001) find that advanced mathematics preparation and coursework are positive predictors of NAEP math scores. Guthrie et al. (2001) and Pinnell et al. (1995) find that reading opportunities and reading prosody, respectively, are positively associated with NAEP reading performance. Abedi et al. (2001) find evidence of a positive association between students’ overall grades since 6th grade and NAEP mathematics performance.

Some authors have considered more systemic or institutional factors in their NAEP research, though this research is less consistent and (less?) extensive. Lubienski (2006) finds a positive association between NAEP math scores and (1) collaborative problem-solving instruction, (2) teacher knowledge of National Council of Teachers of Mathematics (NCTM) standards, and (3) certain ‘reform-oriented’ teaching practices such as non-number math strands. Guthrie (2001) finds that balanced reading instruction is positively associated with Grade 4 NAEP Reading Comprehension in Maryland. Grissmer et al. (2000) and Fitzpatrick (2008) find that greater levels of Kindergarten and pre-Kindergarten participation are positively associated with NAEP scores. Carnoy & Loeb (2002) find a positive association between gains in NAEP mathematics results and strength of state accountability (based on high-stakes testing to sanction and reward schools), but no effect on 9th grade retention rates. In a study supported by the American Federation of Teachers (AFT), Nelson et al. (2003) find that charter school attendance, especially in autonomous charter schools in urban areas, are negatively associated with NAEP math and reading test scores. Nevertheless, institutional factors such as these are not definitive in the literature, and their results should be viewed with caution.

Those who are interested in understanding why Michigan and Detroit students lag behind the rest of the nation in NAEP scores might explore some of the variables discussed above. There is not, however, any one variable or combination of variables that appears to serve as a sole and consistent predictor of NAEP performance, and this will pose a challenge for both understanding and devising solutions to the matter.

Bibliography

Abedi, J. & Lord, C. (2001). The language factor in mathematics tests. Applied Measurement in Education 14(3), 219-234.

Abedi, J., Lord, C., & Hofstetter, C. (2001). Impact of selected background variables on students’ NAEP math performance. Center for the Study of Evaluation, University of California, Los Angeles.

Biddle, B.J. (1997). Foolishness, dangerous nonsense, and real correlates of state differences in achievement. Phi Delta Kappan 79(1), 8-13.

Byrnes, J.P. (2003). Factors predictive of mathematics achievement in white, black, and Hispanic 12th graders. Journal of Educational Psychology 95(2), 316-326.

Card, D. & Rothstein, J. (2007). Racial segregation and the black-white test score gap. Journal of Public Economics 91(11) 2158-2184.

Carney, M. & Loeb, S. (2002). Does external accountability affect student outcomes? A cross-state analysis. Educational Evaluation and Policy Analysis 24(4), 205-331.

Fitzpatrick, M.D. (2008). Starting school at four: The effect of universal pre-kindergarten on children’s academic achievement. The B.E. Journal of Economic Analysis & Policy 8(1) 1-38.

Fuchs, V.R. & Reklis, D.M. (1994). Mathematical achievement in eighth grade: Interstate and racial differences. National Bureau of Economic Research. Working Paper No. 4784.

Grissmer, D., Flanagan, A., Kawata, J., & Williamson, S. (2000). Improving Student Achievement: What state NAEP test scores tell us. RAND Corporation.

Guthrie, J.T., Schafer, W.D., & Huang, C.W. (2001). Benefits of opportunity to read and balanced instruction on the NAEP. Journal of Educational Research 94(3), 145-162.

Hemphil, F.C. & Vanneman, A. (2011.) Achievement gaps: How Hispanic and white students in public schools perform in mathematics and reading on the national assessment of educational progress. Statistical Analysis Report. NCES 2011-459. National Center for Education Statistics.

Hyde, J.S. & Linn, M.C. (2006) Gender similarities in mathematics and science. Science-New York Then Washington 314(5799), 599.

Krashen, S., Lee, S., & McQuillan, J. (2012). Is the library important? Multivariate studies at the national and international level. Journal of Language & Literacy Education 8(1), 27-36.

Lubienski, S.P. (2002). A closer look at the black-white mathematics gaps: Interactions of race and SES in NAEP achievement and instructional practices data. Journal of Negro Education 71(4), 269-287.

Lubienski, S.P. (2006). Examining instruction, achievement, and equity with NAEP mathematics data. Education Policy Analysis Archives 14(14), 1-33.

Mcgraw, R., Lubienski, S.P., & Strutchens, M.E. (2006). A closer look at gender in NAEP mathematics achievement and affect data: Intersections with achievement, race/ethnicity, and socioeconomic status. Journal for Research in Mathematics Education 37(2), 129-150.

McQuillan, J. (1998). The literacy crisis: False claims and real solutions. Portsmouth, NH: Heinemann.

Nelson, F.H., Rosenberg, B., & Van Meter, N. (2003). Charter school achievement on the 2003 National Assessment of Educational Progress. American Federation of Teachers, AFL-CIO.

PInnell, G.S., Pilulski, J.J., Wixson, K.K., Campbell, J.R., Gough, P.B., & Beatty, A.S. (1995). Listening to children read aloud: Data from NAEP’s integrated reading performance record (IRPR) at grade 4. National Center for Education Statistics.

Thomas, J. & Stockton, C. (2003). Socioeconomic status, race, gender, & retention: Impact on student achievement. Essays in Education 7.

Tate, W.F. (1997). Race-ethnicity, SES, gender, and language proficiency trends in mathematics achievement: An update. Journal for Research in Mathematics Education 28(6), 652-679.

Vanneman, A., Hamilton, L., Anderson, J.B., & Rahman, T. (2009). Achievement gaps: How black and white students in public schools perform in mathematics and reading on the National Assessment of Education Progress. Statistical Analysis Report. NCES 2009-455. National Center for Education Statistics.

Last week, we examined the MEAP scores for students in grades three through five in the intermediate school districts located within the seven-county region as well as the Detroit Public Schools. This week, we will examine the MEAP scores for students grades six through nine within the same region. As our earlier post showed, progress for students in grades three through five was slow. This week’s post, however, shows more promising results, with the exception of seventh-grade reading and sixth- and ninth-grade social studies.

In St. Clair County, the St. Clair Regional Education Service Agency (RESA)  experienced a decrease in MEAP math scores for sixth- and seventh-grade students from fall 2012 to fall 2013. For the sixth-graders, there was a 2.8 decrease in the percent of students proficient in math, and for the seventh-graders, it was a 0.2 percent decrease.

Overall in the region, the percent of students proficient in math increased the most among sixth-graders. The Oakland ISD had both the highest percent of sixth-graders proficient in math (55%) and the highest percent increase of sixth-graders proficient in math (7.3%). Washtenaw County had the highest percent of seventh- and eighth-graders proficient in math (53.7 and 53.9%, respectively) while Detroit Public Schools had the lowest percent proficient across all three grade levels (6th: 14.8%, 7th: 11.8%, 8th: 12.2%).

The eighth-grade map is currently not available; please check back soon. 

As can be seen by one of the maps above, seventh-graders across the seven-county region struggled to increase their reading proficiency as not one ISD or DPS experienced an increase in the percent of proficient students. The Monroe ISD experienced the largest decrease among seventh-graders from fall 2012 to fall 2013 at 4.9 percent. It was DPS, though, that had the lowest percent of students proficient in seventh grade reading, 29.1 percent. Even though there were decreases in proficiency across the region, some ISDs, like Livingston and Washtenaw, did have more than 70 percent of their students prove to be proficient on the test (74.2 and 70.9 percent, respectively).

Except for Wayne County and DPS, all the other ISDs in the region had more than 70 percent of their sixth- and eighth-graders test as proficient in reading, and post an increase from the prior year. For the sixth grade, the Livingston ISD had the highest percent of students proficient in reading (84.5%) while the Monroe ISD had the highest increase from the year prior (5.4%). For the eighth-graders, the Washtenaw ISD (82.8%) had the highest percent of students proficient in reading while the St. Clair RESA (8.3%) had the largest increase.

As the maps show, only one ISD and DPS posted an increase in the percent of students proficient in social studies from fall 2012 to fall 2013. For the sixth-graders, DPS had a 6 percent increase in the percent of students proficient in social studies. However, DPS still had the lowest percent of students proficient, 14.8 percent. The Livingston ISD (42.5%) had the highest percent of sixth-graders proficient in social studies and the Macomb ISD (4.5%) experienced the largest decrease.

For the ninth-grade social studies MEAP exam, only the Washtenaw ISD experienced an increase in the percent of students proficient (0.4 percent); it also had the highest percent of students proficient (43.4%). The Monroe ISD experienced the largest decrease from fall 2012 to fall 2013 (7.8%), though it should be noted that the specific numbers for DPS could not be reported, according to the Michigan Department of Education, because less than 10 percent of students in the ninth grade were proficient on their social studies MEAP exam.

All ISDs and DPS in the region showed an increase in the percent of students proficient on the eighth-grade science test. The St. Clair RESA had the highest percent increase from fall 2012 to fall 2013 (8.3%) and Washtenaw ISD had the highest percent of eighth-graders proficient (30.3%). DPS had fewer than 10 percent of its eight-graders test proficient on the science exam last fall.