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Educational policy analysis archives.
n Vol. 10, no. 30 (June 13, 2002).
Tempe, Ariz. :
b Arizona State University ;
Tampa, Fla. :
University of South Florida.
c June 13, 2002
Teacher inequality : new evidence on disparities in teachers' academic skills / Andrew J. Wayne.
Arizona State University.
University of South Florida.
t Education Policy Analysis Archives (EPAA)
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1 of 13 Education Policy Analysis Archives Volume 10 Number 30June 13, 2002ISSN 1068-2341 A peer-reviewed scholarly journal Editor: Gene V Glass College of Education Arizona State University Copyright 2002, the EDUCATION POLICY ANALYSIS ARCHIVES .Permission is hereby granted to copy any article if EPAA is credited and copies are not sold. EPAA is a project of the Education Policy Studies Laboratory. Articles appearing in EPAA are abstracted in the Current Index to Journals in Education by the ERIC Clearinghouse on Assessment and Evaluation and are permanently archived in Resources in Education .Teacher Inequality: New Evidence on Disparities in Teachers' Academic S kills Andrew J. Wayne SRI InternationalCitation: Wayne, A. (2002, June 13). Teacher inequa lity: New evidence on disparities in teachers' academic skills. Education Policy Analysis Archives 10 (30). Retrieved [date] from http://epaa.asu.edu/epaa/v10n30/.AbstractWhen discussing the teacher quality gap, policy mak ers have tended to focus on teacher certification, degrees, and experi ence. These indicators have become key benchmarks for progress toward equa lity of educational opportunity, in part for lack of additi onal teacher quality indicators. This article turns attention to teacher s' academic skills. National data on teachers' entrance examination sco res and college selectivity reveal substantial disparities by schoo l poverty level. The findings commend attention to the gap in academic s kills in the formulation of future policy and research on the te acher quality gap.
2 of 13The teacher quality gap has received attention from a broad spectrum of policy makers (see, e.g., American Council on Education, 1999; Na tional Association of State Boards of Education, 1998; National Commission on Teaching and America's Future, 1996). Even federal legislators have recently proposed way s to close the gap in teacher qualifications between low-income and affluent chil dren (see Stedman, 1999; Wayne, 2000, 6-7). This article reports on the results of an examination of national data on disparities in teachers' academic skills. Analysts have thoroughly documented disparities in teachers' experience, certification, and degrees but very few studies assess differences using academic skills indicators such as teachers' college ratings or entrance examination scores. (Note 1) Opportunities to examine disparities in teachers' a cademic skills at the national level exist because of two national survey efforts, overs een by the National Center for Education Statistics. First, data from the Schools and Staffing Survey portray the public school teaching force as a whole. Second, data from the Baccalaureate and Beyond Longitudinal Study portray the contribution of a co hort of college graduates. This article presents analyses of both data sets. For organizati on, the article begins with a discussion of relevant theory. It then dedicates one section t o each of the two national data sources. Its conclusion briefly discusses implications for p olicy making and for future research.TheoryThe basic premise undergirding this investigation i s that students with lower quality teachers experience a disadvantage. Put into theore tical terms, student learning is a function of teacher quality. Theorists have concept ualized teacher quality as a set of specific knowledge areas and skills (see e.g., Shul man, 1987). But in order assess teacher quality disparities at the national level, one must rely on quality indicators that can be readily measured by teacher questionnaires, administered via sample surveys. Measures such as teachers' experience, certificatio n, and degrees are taken to be indicators of the theorized components of teacher q uality, such as pedagogical knowledge and content knowledge. Analysts have thor oughly documented disparities using questionnaire data containing these indicator s (see Henke et al., 1997; Ingersoll, 1996; Lewis et al, 1999).Less attention has been paid to differences in what Mayer, Mullens, and Moore (2000) refer to as indicators of teachers' academic skills such as teachers' college ratings or entrance examination scores. The case for such atte ntion is clear: studies of student achievement gains confirmÂ—more resolutely than for many other indicators of teacher qualityÂ—that students learn more from teachers with better academic skills (for reviews see Hanushek, 1997; Mayer, Mullens, and Moore, 2000 5-7). But it is important to point out that the relationship between academic skills i ndicators and the conceptualized elements of teacher quality has not received attent ion from theorists. Why might students learn more from teachers with better acade mic skills? Teachers who read faster may acquire new content knowledge more quickly. Tea chers with greater verbal facility may spend their preparation time more focused on le sson design than on deciding what exactly to say. Maybe teachers with better college entrance examination scores learned more in college, or maybe entrance examination scor es and college selectivity correlate with the quality of teachers' precollegiate educati onÂ—a much longer educational experience than undergraduate education.
3 of 13The development of a formalized theory of teachers' academic skills is beyond the scope of this article. But such a body of theory will cle arly become necessary in the future as researchers and policy makers think about policies that might remedy disparities in academic skills.Evidence from the Schools and Staffing SurveyDescription of the DataThe 1993-94 Schools and Staffing Survey (Note 2) (SASS) encompasses several distinct surveys whose findings are readily linked. This inv estigation linked teacher quality indicators from SASS's teacher survey, the Public S chool Teacher Questionnaire, to school poverty levels obtained via the SASS Public School Questionnaire. The teacher survey is known for its detailed questi ons about teacher degrees and certification, but it also contained an indirect me asure of academic skills. The SASS asked teachers to identify their undergraduate inst itutions. Peterson's Guides (1995) rates institutions on its Entrance Difficulty Index, a re asonable albeit rough proxy for academic skills. (Note 3) Ratings include 'most difficult,' 'very difficult, 'moderately difficult,' 'minimally difficult,' and 'noncompetit ive.' To properly consider the relationship between these ratings and academic skills, one must ask exactly how Peterson's assigns ratings. In truth, Peterson's asks institutions to rate themselves. To guide responses, instruction bo oklets that accompany the Peterson's surveys specify thresholds on three optional criter ia: (1) entering students' high school class rank, (2) entering students' college entrance examination scores, and (3) the percentage of applicants accepted. The ratings prob ably do not predict academic skills perfectly, since applicants' decisions depend on ot her factors as well (e.g., wealth). Another SASS measure that requires some discussion reveals the prevalence of poverty at each teacher's school. The SASS collects, from e ach sampled school, the National School Lunch Program participation rate. This measu re comes with two notable flaws. First, although the income eligibility threshold va ries for different family sizes, it does not account for geographic differences in the costof-living. Therefore lunch program eligible students in rural areas, for instance, may be better off than eligible students in central cities.A second problem with this poverty metric is that e lementary school students exhibit higher lunch program participation rates than secon dary school studentsÂ—40 percent and 28 percent respectively according to the 1993-94 SA SS (Henke et al. 1997, 16). As a result, apparent teacher quality disparities by sch ool poverty level may actually represent differences between elementary and secondary teache rs. However, analyses of the disparities in teacher academic skills that disaggr egated elementary and secondary teachers revealed disparities substantially like th ose found for all teachers. The final source of uncertainty worth mentioning is nonresponse. Missing records, records without school poverty information, and rec ords that could not be associated with a Peterson's rating together conspired to redu ce the usable sample by 32 percent, to 37,874 teachers. On the one hand, the availability of so many observations ensures that
4 of 13sampling errors need not even be mentioned in the f igures presented below; all standard errors were less than two percentage points. On the other hand, the assumption that respondents and nonrespondents do not differ system atically is somewhat risky. Thus the true gap in academic skills may be somewhat larger or smaller than depicted. ResultsBefore presenting results, two analytic issues must be addressed. The first involves what specifically to compare. Because SASS observes pove rty at the school levelÂ—not the classroom levelÂ—all findings about disparities in t eacher quality really denote differences between teachers at low-poverty schools and teachers at high-poverty schools. In other words, the selection of compariso n categories is a matter of dividing up schools, not teachers. Analysts divide up the schoo ls using lunch program participation rates; the rates act as category boundaries. For ex ample, Ingersoll (1996) divided schools into 'low-poverty,' 'medium-poverty,' and 'high-pov erty' according to whether lunch program participation was less than 15 percent, bet ween 15 and 50 percent, or 50 percent or more.To best meet the needs of policy maker audiences, t he present investigation examines disparities using three separate category schemes. The first designates all schools as either low-poverty or high-poverty. It divides them according to whether their lunch program participation rates are above or below 28 p ercent. That threshold, determined via an analysis of SASS data on schools' enrollment s, divides schools such that the lowand high-poverty categories each enroll one half of all U.S. public school students. Similar computations yielded the remaining two cate gory schemes: one that divided U.S. students into fourths, and another that divided the m into eighths. (Note 4) The divisions remain divisions of schools, so the text refers to the individual categories as school poverty quartiles and school poverty octiles A second analytic note that must preface the findin gs involves the teacher quality variable. Even skeptics of the student achievement research would admit that students' opportunities are diminished when their teachers' a cademic skills fall below some minimum threshold. Therefore the analysis collapses the quality variable to focus on the percentage of teachers from institutions Peterson's rated either 'minimally difficult' or 'noncompetitive.' It subsumes such teachers under t he new analytic label, 'less selective.' (Note 5) For perspective, in 1993, these institutions confe rred only about one fifth of all bachelor's degrees, according to a weighted tabulat ion based on the Baccalaureate and Beyond Longitudinal Study.Having established the comparison categories and th e quality metric, the discussion can now turn to results. Figure 1 applies the three cat egory schemes to the SASS teachers to investigate disparities in the percentages from les s selective institutions. All three category schemes show disadvantages for higher pove rty schools. The juxtaposition of the three comparisons into a single graphic shows t hat the comparison of halves hides some important variation, evident in the comparison in quartiles. But the further breakdown into octiles does little. An additional b reakdown into sixteenths (not depicted) was also not fruitful. The quartile compa rison thus properly summarizes the disparities.
5 of 13 Figure 1. The percentages of teachers from less sel ective institutions, compared using three school poverty categorizations: halves, quartiles, and octiles. Weighted tabulations from the 1993-94 Schools and Staffing S urvey.Evidence from the Baccalaureate and Beyond Longitud inal StudyDescription of the DataThe Baccalaureate and Beyond Longitudinal Study (Note 6) (B&B) differs markedly from the SASS. Rather than portray only teachers, B &B allows a unique look at the qualities of graduates from the college class of 19 93 who had entered teaching by 1997. The full B&B sample actually includes many nonteach ers as well; it is representative of the entire college class of 1993. But B&B followed the sample members over time, and the 1994 and 1997 interviews determined which gradu ates had become teachers.
6 of 13B&B's teacher academic skills measure and school po verty measure are almost identical to those described above for the SASS. Respondents' undergraduate institutions were linked to Peterson's ratings to yield a measure of academic skills. And the school poverty measure is again the National School Lunch Program participation rate for each teacher's school. (Note 7) B&B does contain one additional academic skills me asure, however. B&B's authors drew records from several sources to determine each graduate's college entrance examination scores. (Note 8) Two final, important characteristics of B&B are its response rate and the number of observations. A remarkably high 90 percent of the o riginal B&B sample completed the 1997 interview. However, many interviews failed to obtain the quality and school poverty information identified above. Thus, althoug h the 1997 interview located some 967 respondents who reported having entered public school teaching jobs, (Note 9) school poverty information was obtained for only 64 6 of them. And among those with school poverty information, 630 had Peterson's rati ngs, and only 530 had entrance examination scores. These problems limited the conc lusions substantially, as discussed below.ResultsAs in the analysis of SASS, some preliminary remark s are needed regarding the school poverty categories and the collapse of the teacher quality variables. The school poverty thresholds used in the B&B analysis were exactly th ose identified above in the SASS analysis. Unless the distribution of students acros s schools of different poverty levels changed substantially between the SASS measurement in 1993-94 and the B&B interviews in 1997, the SASS-derived thresholds pro vide a sufficient approximation. Like the analysis of SASS, the B&B analysis present ed here collapses the quality variables to focus on the proportions of teachers f alling below what might be considered a minimum threshold. The label 'less selective' thu s retains the meaning established earlier. For the other quality variable, entrance e xamination scores, the analysis focuses on the percentages of teachers with bottom quartile college entrance examination scores, where bottom quartile is defined in reference to th e examination score distribution of all class of 1993 graduates.Turning to the results, the weaknesses of the B&B d ata set constrained the conclusions such that, ultimately, replicating Figure 1 for B&B was not possible. Besides nonresponse, the principal barrier was the very lim ited number of observations; estimates for school poverty quartiles and octiles were simply not reliable, yielding standard errors as high as nine percentage points. (Note 10) Figures 2 and 3 therefore each compare teacher quality in the low-poverty and high-poverty halves only. Substantial disparities are evident in these compar isons. In Figure 2, the proportions of teachers from less selective institutions were 22 p ercent at low-poverty and 37 percent at high-poverty schools. In Figure 3, the proportions with bottom quartile entrance examination scores were less disparate: 26 percent and 34 percent, respectively. This disparity was significant only at the .15 level, wh ile all other differences were significant at the .05 level.
7 of 13 Figure 2. The percentages of teachers from less sel ective institutions, compared using school poverty halves. Weighted tabulations f rom the 1993 Baccalaureate and Beyond Longitudinal Study, Second Follow-up. Figure 3. The percentages of teachers with bottom q uartile entrance examination scores, compared using three school poverty halves. Weighted tabulations from the 1993 Baccalaureate and Beyond Longitudinal Study, S econd Follow-up. In interpreting the B&B results, readers must remem ber that B&B does not portray disparities among the entire teaching force. It por trays disparities in the flows of teachersÂ—particularly the flow from undergraduate i nstitutions, within four years of degree receipt. Other flows also play a role in det ermining the quality of low-income students' teachers, such as teacher attrition and m obility. In some state-level analyses researchers have found that such departures are esp ecially prevalent among those with relatively high academic skills (see e.g., Murnane, Singer, and Willett, 1989).ConclusionImplications for policy makingWhen discussing the teacher quality gap, policy mak ers have tended to focus on certification, degrees, and experience. These indic ators have been the benchmarks for progress toward equality of educational opportunity in part for lack of other indicators. This article shows clearly that policy makers need to consider teachers' academic skills. According to the evidence presented here, an academ ic skills gap exists, and it is quite
8 of 13large. The proportions of teachers who graduated fr om institutions rated either 'minimally difficult' or 'noncompetitive' were 21 p ercent and 39 percent in lowand high-poverty schools, respectively. Closing this ga p would require the replacement or upgrade of about one sixth (18 percent) of the teac hing force at high-poverty schools. Disparities of similar size have appeared in the li terature for only two other teacher quality indicators. One is graduate degree holding, which is of debatable importance (see Ballou and Podgursky, 1999, 2000; Darling-Hammond, 1999). The other is in-field degree holding, which, though probably important, c annot benchmark those teachers who lack subject-specific assignments (e.g., genera l elementary teachers). Thus academic skills indicators are a relatively powerfu l tool for understanding how far away the nation is from providing equally qualified teac hers to schoolchildren from different income groups.Consideration of policy options that might affect a cademic skills disparities would require clearer theories about the role of teachers academic skills. Some policy options discussed in the literature thus far relate to scho ol finance (Figlio and Reuben, 1999), school choice (Hoxby, 2000), teacher licensure exam inations (Ferguson, 1998; Gitomer, Latham, and Ziomek, 1999), and the use of academic skills indicators in determining eligibility for teacher scholarships and loan forgi veness (Wayne, 2000). Implications for future researchData quality limited the certainty of the findings; due to nonresponse and sampling error, the true gap in academic skills may be somewhat lar ger or smaller than depicted. But data quality sufficed insofar as it showed (1) that a gap exists and (2) that the gap is substantial. Those findings have a clear implicatio n for future research: efforts are needed to bring data on academic skills up to par w ith data for other quality indicators. In the short-term, some additional investment to in corporate academic skills indicators into ongoing data collection efforts seems worthwhi le. The cost may be substantial. Most quality indicators can be reliably measured vi a one or two items on a pencil-and-paper questionnaire for teachers (e.g., experience). The same is not true for academic skills measures, as additional effort is r equired to match entrance examination score records or to code institutional identifiers. Presumably costs and competing priorities explain why recent federally funded teac her surveys (e.g., Lewis et al., 1999) have not measured teachers' academic skills.Over the long-term, as academic skills indicators r eceive greater attention, the need to improve the indicators will become obvious. College selectivity ratings offer a fairly rough proxy. And if researchers could administer st andardized tests to teachers, it is not clear that they would choose to administer college entrance examinations. Thus, although the disparities reported here are very rea l, serious thought will be required about what measures could better represent academic skills.ReferencesAmerican Council on Education. (1999). To touch the future: Transforming the way teachers are taught Washington, DC: Author.
9 of 13Ballou, D., & Podgursky, M. (2000). Reforming teach er preparation and licensing: Continuing the debate. Teachers College Record, http://www.tcrecord.org ID: 10524. Ballou, D., & Podgursky, M. (1999). Reforming teach er preparation and licensing: What is the evidence? Teachers College Record, http://www.tcrecord.org ID: 10418. Darling-Hammond, L. (1999). Reforming teacher prepa ration and licensing: Debating the evidence. Teachers College Record, http://www.tcrecord.org ID: 10419. Ehrenberg, R. G., & Brewer, D. J. (1995). Did teach ers' verbal ability and race matter in the 1960s?: Coleman revisited. Economics of Education Review, 14 (1), 1-21. Ferguson, R. F. (1998). Can schools narrow the blac k-white test score gap? In C. Jencks, & M. Phillips (eds.), The black-white test score gap (pp. 318-374). Washington, DC: Brookings Institution.Figlio, D. N., & Rueben, K. S. (1999). Tax limits a nd the qualifications of new teachers. University of Florida, Gainesville.Gitomer, D. H., Latham, A. S., & Ziomek, R. (1999). The academic quality of prospective teachers: The impact of admissions and licensure testing Princeton, New Jersey: Educational Testing Service.Hanushek, E. A. (1997). Assessing the effects of sc hool resources on student performance: An update. Educational Evaluation and Policy Analysis, 19 (2), 141-164. Henke, R. R., Chen, X., & Geis, S. (2000). Progress through the teacher pipeline: 1992-93 college graduates and elementary/secondary school teaching as of 1997 Washington, DC: U.S. Department of Education, Natio nal Center for Education Statistics.Henke, R. R., Choy, S. P., Chen, X., Geis, S., & Al t, M. N. (1997). America's teachers: Profile of a profession, 1993-94 Washington, DC: U.S. Department of Education, National Center for Education Statistics.Hoxby, C. (2000). Would school choice change the teaching profession? Paper presented at the annual meeting of the Association for Public Policy Analysis and Management, Seattle, WA.Ingersoll, R. M. (1996). Out-of-field teaching and educational equity Washington, DC: U.S. Department of Education.Kain, J. F., & Singleton, K. (1996). Equality of ed ucational opportunity revisited. New England Economic Review, May/June 87-111. Lankford, H., Loeb, S., & Wyckoff, J. (2002). Teach er Sorting and the Plight of Urban Schools: A Descriptive Analysis. Educational Evaluation and Policy Analysis 24 (1), 37Â–62.Lewis, L., Parsad, B., Carey, N., Bartfai, N., Farr is, E., & Smerdon, B. (1999). Teacher quality: A report on the preparation and qualificat ions of public school teachers
10 of 13Washington, DC: U.S. Department of Education, Natio nal Center for Education Statistics.Mayer, D. P., Mullens, J. E., & Moore, M. T. (2000) Monitoring school quality: An indicators report Washington, DC: U.S. Department of Education. Murnane, R. J., Singer, J. D., & Willett, J. B. (19 89). The influence of salaries on teachers' career choices: Evidence from North Carol ina. Harvard Educational Review 59 (3), 325-346. National Association of State Boards of Education. (1998). The numbers game: Ensuring quantity and quality in the teaching workf orce. Alexandria, VA: Author. National Commission on Teaching and America's Futur e. (1996). What matters most: Teaching for America's future. New York: Author. Peterson's Guides, Inc. (1995). Peterson's guide to four-year colleges: 1996 (26 ed.). Princeton, NJ: Author.Shulman, L. S. (1987). Knowledge and teaching: Foun dations of the new reform. Harvard Educational Review 57 (1), 1-22. Stedman, J. B. (1999). Elementary and secondary school teachers: Action by the 106th Congress Washington, DC: Congressional Research Service. Wayne, A. J. (2000). Federal policies to improve teacher quality for low -income students Unpublished doctoral dissertation, University of Maryland, College Park, MD.About the AuthorAndrew WayneCenter for Education PolicySRI InternationalArlington, VA 22209Email: firstname.lastname@example.orgAndrew Wayne works at SRI International in the Cent er for Education Policy, where his work addresses teacher quality, educational technol ogy, and standards-based reform. Before coming to SRI, he served as a policy analyst for two national efforts to improve teacher quality. He holds degrees in social policy, physics, and education, and has worked as a teacher of science and computers. He ca n be reached at (703) 247-8491 or by email (above). Correspondence may be sent to the author at SRI International, 1611 North Kent Street, Arlington, VA 22209.NotesSome of the findings presented in this article appe ar in an introductory chapter of the author's doctoral dissertation. Although the respon sibility for errors belongs solely to the author, I would like to thank Daniel Goldhaber and Willis Hawley for very helpful comments on an earlier draft. I would also like to thank David Figlio for generously
11 of 13sharing the data he and his colleagues entered on c ollege selectivity. 1. Disparities were evident in an analysis of very old national data, from 1966 (Ehrenberg & Brewer, 1995), and in recent data from the states of New York and Texas (Lankford, Loeb, and Wyckoff, 2002; Ferguson, 1998; Kain and Singleton, 1996). 2. A variety of technical and methodological reports o n the Schools and Staffing Survey are available online at http://www.nces.ed.gov/surv eys/sass. 3. Other researchers have completed the requisite data entry to link each institution to its rating in the 1995 edition of Peterson's. The use o f the 1995 ratings is defensible if, as Hoxby (2000) has claimed, ratings are sufficiently stable over time. To better reflect teacher characteristics, each teacher would need to be linked to the ratings issued approximately four years before his or her college graduation. 4. The lunch program participation thresholds for all three category schemes were computed via weighted tabulations of the data yield ed by the SASS Public School Questionnaire. The threshold that divided them into halves was 28.37 percent. The exact thresholds that divided U.S. students into quarters were as follows: 12.59, 28.37, and 51.80 percent. And the seven thresholds that divide d them into eighths were as follows: 6.35, 12.59, 19.67, 28.37, 38.58, 51.80, and 71.80 percent. 5. Richard Ingersoll (1996, 4-5) used a very similar r ationale to justify his particular construction of out-of-field teaching. He opted to treat teachers as in-field even if they held only a minor related to the subject taught, an d even if that minor was in a subject-related education field (e.g., mathematics education). 6. A variety of technical and methodological reports o n the Baccalaureate and Beyond Longitudinal Study are available online at http://w ww.nces.ed.gov/surveys/B&B. 7. The architects of B&B foresaw that knowledge about the schools at which the B&B teachers taught could be useful. Therefore responde nts who taught were asked to identify the school at which they taught, and responses were coded so that basic school characteristics could be obtained via the Common Co re of DataÂ—another data set created by the National Center for Education Statis tics. 8. Sources included records from the Educational Testi ng Service and higher education institutions' records of sample members' SAT and AC T scores. See Henke et al. (2000, 83). 9. This total does not include teachers who first tau ght at private schools. It also excludes teachers who had taught before graduation or had received their certification more than one year before graduation. This latter g roup is often excluded in analyses of the B&B cohort's contribution to the teaching force See Henke et al. (2000, 9). 10. Further disaggregation of teachers into school pov erty quartiles and octiles yielded estimates with standard errors as high as nine perc entage points. Some interesting spikes occurred in the percentages of teachers with low ac ademic skills in the fifth octile, and to a lesser extent in the third quartile. But given that the spikes may be largely an artifact of sampling error or nonresponse bias, it was judge d that the best summary would be the comparisons that focus on the low-poverty and highpoverty halves.
12 of 13 Copyright 2002 by the Education Policy Analysis ArchivesThe World Wide Web address for the Education Policy Analysis Archives is epaa.asu.edu General questions about appropriateness of topics o r particular articles may be addressed to the Editor, Gene V Glass, email@example.com or reach him at College of Education, Arizona State University, Tempe, AZ 8 5287-2411. The Commentary Editor is Casey D. Cobb: firstname.lastname@example.org .EPAA Editorial Board Michael W. Apple University of Wisconsin Greg Camilli Rutgers University John Covaleskie Northern Michigan University Alan Davis University of Colorado, Denver Sherman Dorn University of South Florida Mark E. Fetler California Commission on Teacher Credentialing Richard Garlikov email@example.com Thomas F. Green Syracuse University Alison I. Griffith York University Arlen Gullickson Western Michigan University Ernest R. House University of Colorado Aimee Howley Ohio University Craig B. Howley Appalachia Educational Laboratory William Hunter University of Calgary Daniel Kalls Ume University Benjamin Levin University of Manitoba Thomas Mauhs-Pugh Green Mountain College Dewayne Matthews Education Commission of the States William McInerney Purdue University Mary McKeown-Moak MGT of America (Austin, TX) Les McLean University of Toronto Susan Bobbitt Nolen University of Washington Anne L. Pemberton firstname.lastname@example.org Hugh G. Petrie SUNY Buffalo Richard C. Richardson New York University Anthony G. Rud Jr. Purdue University Dennis Sayers California State UniversityÂ—Stanislaus Jay D. Scribner University of Texas at Austin Michael Scriven email@example.com Robert E. Stake University of IllinoisÂ—UC Robert Stonehill U.S. Department of Education David D. Williams Brigham Young University
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