There are three universities in the state of Michigan that make up the University Research Corridor, an alliance committed to transforming and diversifying the state’s economy. These three universities are the only public universities in the state to have their governing bodies appointed by the voters of the State of Michigan. These universities are Wayne State University (WSU), the University of Michigan (UofM) and Michigan State University (MSU). This post aims to show where students who attend these universities come from within the state, country and across the nation.

In looking at all three maps, it becomes obvious that WSU’s population is largely representative of residents from the tri-county area (Wayne, Oakland and Macomb Counties). As WSU is historically a commuter school centered in Detroit, this reflects what one would expect. In fall of 2013, about 7,900 of the students who enrolled at WSU lived within Wayne County. During that same time, there were about 6,000 students from Oakland County and about 4,900 from Macomb County. Although Washtenaw County is still within the Southeastern Michigan region, only 507 students were from there; Washtenaw County residents represented the fourth largest population in the state.

Just as geographic representation decreased the farther away one got from Wayne County within the state, the same continued for states outside of Michigan. Ohio and California were the two states mostly highly represented in fall of 2013 with 107 and 97 students, respectively, coming from each. These two states, individually, had more representation at WSU than some counties in Michigan, such as Jackson and Ionia to name a few.

When looking at the geographic makeup of WSU on a global scale, aside from the United States, Canada had the largest population with 576 students and China had the second largest representation with 332 students. There are 26,020 students, including both graduate and undergraduate students, who attended Wayne State in fall of 2013 who were from the U.S.

Overall, enrollment in fall of 2013 was recorded at 27,897 students. Of that, 25,043 (89%) were from within the state of Michigan, 977 (4%) were from another state and 1,877 (7%) were from another country.

Similar to Wayne State University, much of the University of Michigan’s student population came from Wayne, Oakland, Washtenaw or Macomb Counties. For UofM, however, the representation of Washtenaw County residents, where UofM is located, was five times higher than those who attend WSU. Conversely, WSU had more than twice the number of Wayne County residents than UofM.

Although both universities largely drew from the same geographic locations in state, UofM had a much greater overall representation of students from across the state. At WSU, there were some counties with no representation, but at UofM, every Michigan county was represented. Keeweenaw and Oscoda Counties had the lowest in-state representation at 1 student.

When looking at the representation from across the country, UofM out-did both WSU, and as you will see below, Michigan State University. In fall of 2013, UofM enrolled 15,704 students from across the country (not including Michigan); this represented 36 percent of the student population. Illinois was the state with the largest representation; 1,918 students from there attended UofM in fall of 2013. Only nine students from the state of North Dakota enrolled in UofM at the state time, making it the state with the least representation.

On an international scale, China was the most represented with 2,334 students enrolled at UofM for fall of 2013. The international population at UofM during this time represented about 14 percent of the student body.

Overall enrollment at UofM during this time was 43,710; 37,651 of those students were from the U.S.

Unlike UofM and WSU, where the largest geographic representation comes from the universities’ home counties, Michigan State University drew the majority of students from outside of the region it is located in (Ingham County). Like its sister schools, Wayne, Oakland, Macomb and Washtenaw counties were heavily represented. From in-state, Oakland County was the most represented with 8,558 students. There were 4,937 students from Wayne County who attended MSU in fall of 2013, 2,764 from Macomb County and 1,364 from Washtenaw County. There were 3,130 students from Ingham County, where MSU is located, who attended the university; this was more than those sent from Macomb and Washtenaw Counties. Kent County was also highly represented with 2,348 students attending MSU in fall of 2013.

When looking at enrollment from out-of-state residents, Illinois again had the highest representation with 1,308 students. West Virginia had the lowest with one student. Overall, the out-of-state student population at MSU in fall of 2013 represented 11.6 percent of the student body.

In 2013, 4,419 students from China attended MSU, making it the country with the highest representation, aside from the U.S. The international population at MSU during fall of 2013 represented about 15 percent of the student body.

Overall, in fall of 2013 enrollment at MSU was 49,292; the number of full-time students from the U.S. was 41,950.

For this data set, MSU only counted all full-time students.

In comparison, above is a map that shows where students who attended Temple University in Philadelphia, Pennsylvania in fall of 2013 originally resided. This university was chosen because it is located in a similar environment as WSU and typically has similar enrollment numbers.

Temple University had 38,148 students enrolled in fall of 2013, of whom 22,318 were from Pennsylvania. The state of New York had the highest out-of-state representation with 564 students.

Overall, the Temple student population of only undergraduate students was 26,454 and the overall student undergraduate population was 27,514.

For the purpose of this post, Temple was the only school to only count undergraduates for its student population.

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.

Currently, the Michigan Legislature is considering moving the oversight of the Michigan Education Assessment Program (MEAP) testing from the Michigan Department of Education (MDE) to the Treasury Department through House Bill 5581 and Senate Bill 0945. This suggested move, as proposed by Rep. Bob Genetski (R-Saugatuck), is so a “more responsive” department can control the state’s performance testing mechanism. The lack of responsiveness Genetskialleges refers to the decision by MDE to cease using the MEAP test as the MDE’s standardized test. Rather, the MDE plans to implement the Smarter Balance Assessment, standardized tests based on the Common Core Standards. The math and language arts portions of the MEAP test were no longer supposed to be given after this past fall. The upcoming state school aid budget, however, could require schools to use MEAP tests to receive funding, if the bills pass. While the Senate bill passed, House Republicans have not passed their version as yet.

Despite this ongoing debate, the MEAP has recently indicated some progress. Here we  MEAP results from the 2013-14 school year.

In this post, we examine the MEAP results for third, fourth and fifth graders. Overall, these maps show there has been a decline in MEAP scores in at least one county at each grade level for each subject tested for, with one exception. The science scores for fifth-graders saw an increase across the seven county region this past school year. In each case we present the scores for the Intermediate School District in the county, which represents the aggregate scores for the students across the county. In addition, we present scores for Detroit Public Schools (DPS).

Of the three grades examined in this post, third-graders experienced the least growth in MEAP proficiency from the fall 2012 to fall 2013. In math, the Washtenaw Intermediate School District (ISD) was the only one in the region to experience growth (1.3 percent). In Monroe County, the Monroe ISD had the largest decline in the percent of students proficient in math from 2012 to 2013 (4 percent), but it was Wayne County’s Regional Education Service Agency (RESA) that had the lowest percent of students proficient in math (31 percent), of the counties. The Detroit Public Schools had the lowest percent of students proficient in third grade math (14.6 percent) and third grade reading (35.3 percent).

No county in the region had an increase in the percentage of students proficient in third grade reading between fall 2012 and fall 2013. Again, the public schools in Monroe County experienced the largest decrease in the percent proficient (7.9 percent) but Wayne RESA had the lowest percent of third-grade students proficient in reading (49.2 percent).

There were four ISDs (Livingston, Oakland, Washtenaw and Wayne), along with Detroit Public Schools, that had a decrease in the percent of fourth-graders proficient in math.  Of those, Livingston ISD had the largest decrease (3.3 percent) but it also had the highest percent of students proficient in math (59.6 percent) in the region.

All intermediate school districts, and the public schools in the City of Detroit, experienced an increase in the percent proficient in reading, with the exception of the intermediate school district in Livingston County. Livingston ISD experienced a 4.1 percent decrease. Even with the decrease, the Livingston ISD had the region’s highest percentage of students proficient in reading (80.3%), as well as in math (59.6%) and writing (80.3%). The Monroe ISD had the largest percent increase of third-graders proficient in all three subjects.

Similar to the percent changes of students proficient in math, the Livingston ISD also had the largest decrease in fourth-graders proficient in science (3.3 percent) but the highest percent of students proficient in the subject (80.3 percent). In terms of overall percent increase, Monroe County had the largest (11.8 percent)

From fall 2012 to fall 2013, there was a decrease in the percent of students proficient in both math from fall 2012 to fall 2013  for the St. Clair RESA and DPS. St. Clair RESA had the largest decrease in students proficient from fall 2012 to fall 2013 (7.3 percent). Wayne and Oakland intermediate school districts, along with Detroit Public Schools also experienced a decrease.

For reading, the St. Clair RESA experienced a 1 percent decrease, which was the largest decrease of the counties. The Detroit Public Schools experienced a 1.4 percent decrease in the percent of fifth-graders proficient in reading though.  Overall, Livingston ISD had the highest increase in the percent students proficient in reading (2.7 percent) and the highest percentage of students proficient (85.3 percent).

For fifth-grade students in the region, all intermediate school districts experienced an increase in the percent of students proficient in science; the intermediate school districts in St. Clair and Washtenaw counties had the largest percent increase (4.8 percent). The Washtenaw ISD also had the highest percent of students proficient in science (24.7 percent).

Next week we will examine the MEAP scores for sixth through ninth graders in the region.