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# Traditional and Academic Alternative Schools: Pupil-Teacher and Per Pupil Expenditure Differences for At-Risk Students

Summary: In this study, the researchers examined the extent to which student-to-teacher ratios and per pupil expenditures differed for at-risk students as a function of being enrolled either at a traditional school or being enrolled at an academic alternative education center. Data, collected from the Texas Education Agency Academic Excellence Indicator System, were analyzed across the State of Texas over the 2004–2005 and 2005–2006 academic school years. Statistically significant differences were yielded in student-to-teacher ratios and per pupil expenditures for both school years between the two groups of at-risk students. Alternative education campuses had higher per pupil expenditures than did traditional high schools with large at-risk populations. Implications of these findings are discussed.

## Introduction

In today’s knowledge-based economy, dropping out of high school drastically limits students’ employment options and significantly reduces the amount of income they can earn in their lifetimes (Stewart & Knaggs, 2005). A high school graduate in 2000 earned about 25% more a year than a student who dropped out of high school, and a college graduate made more than twice as much as a student who did not complete high school (Stewart & Knaggs). Therefore, it is critical to the future economic success of students that they complete high school.

As the United States moves toward a higher-skilled labor force (U.S. General Accounting Office, 2004), dropouts have fewer prospects for economic success later in life. The 2000 census data indicated that adults who have not completed high school earn an average income of $16,121 a year, compared to$24,572 for adults with a high school diploma or GED. This is a difference of $8,451 more per year or approximately one third more income than students who do not complete school (U.S. Bureau of the Census, 2000). The earning power of dropouts has declined over the past three decades. In 1971, male dropouts earned$35,087, but this amount decreased 35% to $23,903 in 2002. Earnings for female dropouts decreased from$19, 888 to $17,114 (U.S. Bureau of the Census, 2002). Dropouts contributed to state and federal tax reserves at only about one half the rate of high school graduates. Over a working lifetime, this amounts to approximately$60,000 less per dropout, or $50 billion annually for the 23 million high school non-completers, ages 18–67 (Rousse, 2005). Other negative consequences are associated with students dropping out of school. Dropouts are more likely to become dependent on public assistance, have health problems, and engage in criminal activity (U.S. Bureau of the Census, 2000). Three quarters of state prison inmates were high school dropouts, and 59% of federal inmates have been dropouts (Harlow, 2003). Dropouts are 3.5 times more likely than high school graduates to be incarcerated in their lifetime (Catterall, 1985). Harris (2006) stated that the financial and human costs of incarceration are astounding. When calculated over a prisoner’s lifetime, the cost for maintaining that prisoner is approximately one million dollars (The Sentencing Project, 2003). For each dollar spent on education, four are spent on incarceration (The Sentencing Project, 2004). The U.S. death rate for persons with fewer than 12 years of education is 2.5 times higher than for those persons with 13 or more years of education (Adair, 2002). In light of the negative consequences of dropping out of school for society and individuals, ensuring school completion for all students must be a priority for educators, administrators, and policymakers across the country. Between October 2005 and October 2006, about 444,000 young people dropped out of high school (U.S. Department of Labor, 2007). The labor force participation rate for these dropouts (51.4%) was considerably lower than the participation rate for recent high school graduates who had not enrolled in college (76.4%). The unemployment rate for recent high school dropouts (23.1%) was about the same as that for recent high school graduates who were not enrolled in college (25%). Among recent high school dropouts, young men were more likely than young women to be participating in the labor force in October 2006, 56% and 45.l%, respectively (U.S. Department of Labor, 2007). According to the National Center for Education Statistics (NCES, 2002), between the years 1993 and 1998, the proportion of all 16-to 24-year-olds who dropped out of school fluctuated between 11% and 12%. A decrease occurred in the proportion of all 16- to 24-year-olds who were dropouts between 1998 (12%) and 2003 (10%). However, large differences continued to be present in dropout rates between racial or ethnic groups. From the same study by the NCES, the proportion of 16-to 24-year-old Hispanic dropouts (24%) was higher than either the proportion of African American or White dropouts (11% and 6%, respectively). Also, African American 16-to 24-year-olds were more likely to become dropouts than were White 16-to 24-year-olds. In addition, a higher proportion of males (11%) than females (8%) dropped out of high school. A fundamental measure of school success is the extent to which students are completing their secondary education. The national dropout statistics have also been reflective of the Texas dropout issue. According to the Secondary School Completion and Dropouts Report (TEA, 2005b), the number of dropouts in Grade 7 through Grade 12 from Texas public schools rose to 18,290 in 2005, an 11.3% increase compared to the 16,434 students who dropped out in 2004. From the same study, it was reported that the gap in Grade 7 through Grade 12 dropout rates between African American and White students increased in 2005 by 0.1%. The gap in dropout rates between Hispanics and Whites remained the same at 0.9%, the dropout rate for African Americans rose to 1.2%, the rate for Hispanics rose to 1.4%, and the rate for Whites rose to 0.5%. To address the problems of students leaving the traditional school system before completing their secondary education, school districts have developed many interventions to address the needs of at-risk students. One of these strategies has included the implementation of academic alternative schools. Historically, alternative schools have fulfilled the missing elements of traditional education institutions (Glass, 1995). Some educators have believed that when students are in nurturing and supportive environments, they are able to thrive academically (Frediana, 2002). The creation of alternative programs has related to increased pressures placed on high school students. McDill, Natriello, and Pallas (1985) attributed this trend to “reform movements in education resulting in increased testing, higher expectations, greater academic standards, and the ever present move to restructure school to meet the needs of all students, including those at-risk of school failure” (p. 416). With many new mandates, states began to search for alternative answers (Wolk, 2000). The state of Texas responded to the needs of at-risk students and developed alternative schools to help improve their success. According to the 2006 TEA Accountability State Summary, Texas had a total of 417 alternative education campuses serving students who were being educated in schools other than in the traditional high school setting for the 2005–2006 school year. The purpose of creating these alternative schools was to ensure that at-risk students graduate with a high school diploma. Unfortunately, even students who are served in alternative schools in Texas drop out of school. The number of dropouts for all students from alternative schools in Texas for the school year 2004–2005 was 2,470 or 3% (TEA, 2006). The number of African American dropouts from AECs was 568 or 2.9%. The Hispanic students who dropped out of alternative schools were 1,463 or 3.5%, and White dropouts numbered 427, or 2.1%. Economically disadvantaged students who dropped out of alternative schools represented 1,149 or 2.7%. Hispanic and economically disadvantaged students represented the demographic groups with the highest dropout rates from alternative schools in Texas during the 2004–2005 academic year. ## Statement of the Problem The percent of dropouts in Texas has not changed much in 20 years. In 1985–1986, Texas schools lost 33% of students to dropping out; in 2005–2006, Texas schools reported 35% as dropouts (Intercultural Development Research Association [IDRA], 2006). According to the IDRA’s first study conducted in the 1980s, more than 2.5 million students exited from Texas public schools. This is the equivalent of losing the populations of Austin, Dallas, and El Paso over the course of two decades. This has cost the state of Texas$730 billion in lost income, lost tax revenues, increased job training, welfare, unemployment, and criminal justice costs (Montecel-Robledo, 2005).

In Texas, 135,000, or 35% of the freshman class of 2002–2003, left school before graduating in the 2005–2006 school year (Montecel-Robledo, 2005). Several states, including Texas, have created academic alternative schools as options for at-risk students who have not been successful in traditional academic settings. Are academic alternative high school settings providing opportunities for the students they were intended to serve? How do at-risk students served in traditional high school settings fare compared to at-risk students served in academic alternative school settings?

## Significance of the Study

Texas has made attempts to reduce the number of students dropping out of school. Charter schools and academic alternative schools have been developed to create safe havens to enable at-risk students to complete their high school diplomas. However, little information was found in the research literature describing the academic success of at-risk students at academic alternative schools in the state of Texas as compared to at-risk students served in traditional high schools.

Most often, research on alternative schools has been focused on schools created for students who have been in serious violation of disciplinary codes. The goal of alternative

education has not always been to provide another opportunity for success for at-risk students (Raywid, 1999). Unfortunately, some persons believed that at-risk students were a menace to other students on a traditional campus and that alternative school students were more likely to participate in unsafe behavior (Escobar-Chaves, Tortolero, Kelder, & Kapadia, 2002). An alternative school could become a school full of undesirable students who have been separated from the mainstream population (McGee, 2001). Although literature is available describing the types of alternative programs in existence, few studies specify the relationship of such schools and student success indicators. Little is known about how that success compares to at-risk students in traditional high school settings.

## Research Questions

1. What is the difference between the student-to-teacher ratios of academic alternative high schools and traditional high schools with large at-risk populations in the state of Texas for 2004–2005?

2. What is the difference between the student-to-teacher ratios of academic alternative high schools and traditional high schools with large at-risk populations in the state of Texas for 2005–2006?

3. What is the difference between the expenditures per pupil of academic alternative high schools and traditional high schools with large at-risk populations in the state of Texas for 2004–2005?

4. What is the difference between the expenditures per pupil of academic alternative high schools and traditional high schools with large at-risk populations in the state of Texas for 2005–2006?

## Method

Participants

In 2005, over two million students, 45.8% of the total number of students in the state of Texas, met the definition of at-risk (TEA, 2005b). A comprehensive list of all high schools in Texas that fit the criterion of the study was obtained from TEA (TEA, 2005b). The sample in this study included academic alternative high schools in Texas as well as traditional high schools with 70% or larger at-risk populations. This percentage of at-risk was selected because academic alternative schools in Texas required an enrollment of at least 70% at-risk students for the academic years of 2004–2005 and 2005–2006 to be designated as an AEC (TEA, 2007a). The selection of these populations allowed for the comparison of the data for both settings.

Schools meeting the criteria of AECs in Texas in the school years 2004–2005 and 2005–2006 totaled 84. The data from these schools included student-to-teacher ratios and per pupil expenditures. Schools excluded from this study consisted of charter schools, residential facilities, and discipline alternative schools because they did not meet the criteria of academic AECs. Schools meeting the criteria of traditional high schools with large at-risk populations for the school year 2004–2005 and 2006-2006 in the state of Texas totaled 86.

Instrumentation

Archived information was acquired from the AEIS on each school in the state of Texas that met the criteria of the study for the school years 2004–2005 and 2005–2006. The TEA (2005a) provided data through the Academic Excellence Indicator System (AEIS) and other data reports using PEIMS data. Data were obtained from the AEIS database, which provided a broad range of information on the performance indicators of students in each school and district in Texas on an annual basis. The data were analyzed for statistically significant differences and relationships in the performance indicators selected for the study as they related to the school settings.

Dependent Variables

Student-to-Teacher Ratios. Student-to-teacher ratios reported the total number of students in membership divided by the total number of teachers (expressed as full-time equivalents) at the school (TEA, 2007b). This ratio is reported yearly in the campus AEIS report.

Per Pupil Expenditures. Per pupil expenditures was a value that reported actual 2004–2005 and 2005–2006 expenditures divided by the total number of 2005–2006 students. The expenditures per student were not the actual amount spent on each and every student, but rather a per pupil average of the total (TEA, 2007b).

Independent Variable

For the purpose of this study, a traditional high school setting was defined as a regular high school that was state accredited and followed the accountability guidelines set by the Academic Excellence Indicator System (AEIS).

## Procedures

All data in this study were based upon the accountability data reported by TEA for the 2004–2005 and 2005–2006 school year. Data were collected from archival information maintained by TEA and were accessed from the TEA website. The AEIS database provided an extensive selection of information on the performance indicators of students in each school in Texas on an annual basis. The AEIS report from each school identified in the population was accessed, and data points were collected and entered in to a spreadsheet. The performance indicators selected for this study were the student-to-teacher ratio and per pupil expenditures.

The AEC school settings in Texas that were excluded from this study were charter schools, residential placement facilities, and discipline alternative schools because they did not meet the criteria of AECs targeted for this study. Additionally, data from all traditional schools in Texas were not included in this study. Only the traditional high schools with at-risk populations of 70% or larger were used in the data analysis. This percentage of at-risk was selected because academic alternative schools in Texas for the academic years 2004–2005 and 2005–2006 required an enrollment of at least 70% at-risk students to be designated as an AEC (TEA, 2007a). The selection of these populations allowed for the comparison of the data for both settings. The AEIS database is not always comprehensive and therefore, some indicators were not reported for some schools. However, all data that were available from AEIS were utilized for the statistical analysis of the selected schools. All assumptions for use of inferential statistics were evaluated before data analysis was completed.

### Results

For each of the research questions, the dependent variables were student-to-teacher ratio and per pupil expenditures for the academic years 2004–2005 and 2005–2006. The independent variable was the school setting and included academic alternative high schools and traditional high schools with large at-risk populations. An assessment of the standardized skewness coefficients (i.e., the skewness value divided by the standard error of skewness) and the standardized kurtosis coefficients (i.e., the kurtosis value divided by the standard error of kurtosis) for both settings for both years revealed a serious departure from normality for both student-to-teacher ratios and for per pupil expenditures. The coefficients for per pupil expenditures were extremely non-normal for both traditional high schools (standardized skewness coefficient = 11.94; standardized kurtosis coefficient = 25.67) and academic alternative high schools (standardized skewness coefficient = 11.15; standardized kurtosis coefficient = 19.98). With values for normality ranging from +3.00 to -3.00, these values indicated that the student-to-teacher ratios and per pupil expenditures were not normally distributed data sets. Thus, it was determined that parametric statistical analyses were inappropriate because of these strong indicators of non-normality. Accordingly, a nonparametric (i.e., Mann-Whitney’s U) t-test was calculated to examine each of the research questions. Also reported in the data analysis was the Z score, which indicated how far, and in what direction, each item deviated from its mean, expressed in units of its standard deviation.

For the 2004–2005 academic year, the descriptive statistics for each of the variables for the traditional high schools and the academic alternative schools used in this study were listed in Table 1. The traditional high school student-to-teacher ratios were slightly more than three students less than the student-to-teacher ratios reported for AECs. The traditional high school per pupil expenditures was almost half the amount of the per pupil expenditures reported for the AECs. For the 2005–2006 academic year, the descriptive statistics for each of the variables for the traditional high schools and AECs examined in the study were listed in Table 1. In this school year, the traditional high school student-to-teacher ratio was about five students more than the student-to-teacher ratios for AECs. Similar to the previous year, the per pupil expenditures was more than twice as much in the traditional high school than in the AECs.

Table 1

Descriptive Statistics for Variables by School Year for Both Settings

 Variables n M SD Standardized Skewness Standardized Kurtosis 2004–2005 Student/Teacher Ratio 160 13.75 5.00 19.43 11.40 Per Pupil Expenditures 161 $7,899$7,723 23.54 63.67 2005–2006 Student/Teacher Ratio 160 13.12 4.74 -0.57 2.51 Per Pupil Expenditures 163 $8,189$8,095 25.00 69.71

A Bonferroni correction was used because there were multiple outcome measures. Without correcting for multiple statistical analyses, a Type I error is more likely to occur than would be indicated by an alpha level of .05. The Bonferroni correction is a method that allows many comparison statements to be made (or confidence intervals to be constructed) at the same time assuring an overall confidence coefficient is maintained. The Bonferroni correction, using an adjusted alpha level equal to the original alpha level (.05), was divided by the number of the outcome measures (four) as described in the research questions (Brown & Russell, 1997). Thus, the level of statistical significance that had to be reached in this study was .125 (i.e., .05 divided by 4 analyses). The Statistical Package for the Social Sciences-PC version 15.0 was used for the statistical analysis.

Table 2

Descriptive Statistics for 2005–2006 Variables by School Settings

 Variables n M SD Standardized Skewness Standardized Kurtosis Traditional Schools Student/Teacher Ratio 86 15.16 2.80 .442 10.04 Per Pupil Expenditures 86 $5,612$1,778 16.05 41.73 AECs Student/Teacher Ratio 74 10.10 5.41 3.06 2.70 Per Pupil Expenditures 77 $11,068$10,965 11.80 22.66

Table 3

Descriptive Statistics for 2004–2005 Variables by School Settings

 Variables N M SD Standardized Skewness Standardized Kurtosis Traditional Schools Student/Teacher Ratio 86 15.29 2.33 4.40 6.70 Per Pupil Expenditures 85 $5,402$1,476 11.94 25.67 AECs Student/Teacher Ratio 74 11.94 6.47 4.33 12.03 Per Pupil Expenditures 76 $10,693$10,480 11.15 19.98

Research Question 1

To determine whether a statistically significant difference was present between the student-to-teacher ratios of traditional high schools with large at-risk populations and academic alternative high schools in Texas for the school year 2004–2005, a nonparametric (i.e., Mann-Whitney’s U) t-test was calculated for each of the two academic years, with school setting being the variable under investigation. For the 2004–2005 academic year, the finding was U = 1732.00, p < 0.0001, indicating the presence of a statistically significant difference between the student-to-teacher ratios in the 2004–2005 school year of traditional high schools and academic alternative high schools. Student-to-teacher ratios were significantly lower in academic alternative high schools than in traditional high schools with large at-risk populations. The Z score (-4.96) confirmed this statistically significant finding reflecting a mean score (M = 15.30) of more than four standard deviations (SD = 2.33) below the mean. Using a 99.4% confidence interval, Cohen’s d (0.69) represented a moderate effect size (Cohen, 1988).

Research Question 2

To determine whether a statistically significant difference was present between the student-to-teacher ratios of traditional high schools with large at-risk populations and academic alternative high schools in Texas for the school year 2005–2006, a nonparametric (i.e., Mann-Whitney’s U) t-test was calculated for each of the two academic years, with school setting being the variable under investigation. For the 2005–2006 academic year, the finding was U = 1235.00, p < .0001, indicating the presence of a statistically significant difference between the student-to-teacher ratios in the 2005–2006 school year of the traditional high schools as compared to academic alternative high schools. Student-to-teacher ratios were significantly lower in academic alternative high schools than in traditional high schools with large at-risk populations. The Z score (-6.66) confirmed this statistically significant finding reflecting a mean score (M = 15.16) of more than six standard deviations (SD = 2.81) below the mean. Using a 99.4% confidence interval, Cohen’s d (1.02) represented a very large effect size (Cohen, 1988).

Research Question 3

To determine whether a statistically significant difference was present between the per pupil expenditures of traditional high schools with large at-risk populations and academic alternative high schools in Texas for the school year 2004–2005, a nonparametric (i.e., Mann-Whitney’s U) t-test was calculated for each of the two academic years, with school setting being the variable under investigation. For the 2004–2005 academic year, the finding was U = 1527.00, p < .0001, indicating the presence of a statistically significant difference between the traditional high school and academic alternative high school setting as they pertain to per pupil expenditures in the 2004–2005 school year. Per pupil expenditures of academic alternative schools was significantly higher than the per pupil expenditures of traditional high schools with large at-risk populations. The Z score (-5.77) confirmed this statistically significant finding reflecting a mean score (M = $10,692) of more than five standard deviations (SD =$10,480) below the mean. Using a 99.4% confidence interval, Cohen’s d (0.71) represented a moderate effect size (Cohen, 1988).

Research Question 4

To determine whether a statistically significant difference was present between the per pupil expenditures of traditional high schools with large at-risk populations and academic alternative high schools in Texas for the school year 2005–2006, a nonparametric (i.e., Mann-Whitney’s U) t-test was calculated for each of the two academic years, with school setting being the variable under investigation. For the 2005–2006 academic year, the finding was U = 1366.00, p < .0001, indicating the presence of a statistically significant difference between the per pupil expenditures in the 2005–2006 school year for traditional high schools compared to academic alternative high schools. Per pupil expenditures of academic alternative schools were significantly higher than the per pupil expenditures of traditional high schools with large at-risk populations. The Z score (-6.47) confirmed this statistically significant finding reflecting a mean score (M = $11,068) of more than six standard deviations (SD =$10,965) below the mean. Using a 99.4% confidence interval, Cohen’s d (0.69) represented a moderate effect size (Cohen, 1988).

### Discussion

Critically at-risk students served in AECs often have to meet district-developed criteria to be admitted to the alternative setting. The nature of the needs of these students often leads AECs to compare themselves to intensive care units, similar to those found in hospital settings. Just as in the hospital intensive care units, AECs are able to offer more intense adult intervention due to the lower student-to-teacher ratios that may lead to enhanced academic achievement. Researchers (e.g., Finn, 2002; Raywid & Oshiyama, 2000) have found that academic achievement should be positively impacted by significantly lower student-to-teacher ratios. However, the findings from this study, based upon two years of school district data for the 84 AECs and the 86 traditional high schools with large at-risk populations, indicated incongruent findings with the literature reviewed.

Regarding per pupil expenditures in AECs and traditional high schools, statistically significant differences were present, with AECs having higher per pupil expenditures than traditional high schools with large at-risk populations. The cost difference is likely due to the smaller number of students served in AECs. Researchers (e.g., Gregory, 1992; Howley, 1996) reported that the relationship between size and costs varied depending on individual school circumstances. If options such as AECs to keep students enrolled in school are not available, there is really no cost effectiveness, considering the economic consequences of students dropping out of school. Researchers (e.g., Alspaugh, 1998; Croninger & Lee, 2001; Fine, 1986; Rumberger, 1983) reported that dropouts have a direct impact on the educational system, and also negatively affect the American economy. Moretti (2005) reported that increasing the high school completion rate by 1% for all men ages 20-60 would save the United States \$1.4 billion annually in reduced costs associated with crime. Therefore, the costs associated with interventions for school- age students may be less than the cost associated with adult dropouts.

### Implications

To the extent that the findings are generalizable, there are implications for stakeholders who work with at-risk students in both educational settings. Although the costs of serving at-risk students at AECs are usually higher than the cost of serving at-risk students in traditional high schools, the benefits may translate into savings to the individual student, and to society, as a whole. Further implications include the need to redefine at-risk criteria and consider categorizing the criteria in terms of academic and/or psychosocial factors. The current criteria are ineffective due the vagueness and ambiguity of the current definition. This new definition and categorization could help schools of all settings better identify students. Moreover, service options for students with multiple at-risk criteria could be developed.

The findings regarding student-to-teacher ratios were incongruent with the review of the literature that indicated smaller class size should transfer to higher academic achievement. Findings regarding per pupil expenditures indicated a statistically significant difference with AECs spending almost double the amount per pupil than traditional high schools. However, even though AECs costs more, the student completion rates are higher in this setting than in the traditional setting. Therefore, the results of higher completion rates at AECs suggest that this setting has value to a particular group of students who may be dropouts without this option. The data regarding per pupil expenditures revealed large differences in the spending per pupil for each of the academic AECs in the study. The large standard deviations found in the per pupil expenditure data analysis for academic AECs indicated a need for further study to explore this pattern and implications for educational programs offered at AECs.

Based upon this research, recommendations for practitioners include changing the definition of at-risk and categorizing factors by academic and/or psychosocial descriptors. Redefining and clarifying the definition could result in more effective interventions and improved completion rates for students served in both settings. Further studies are needed to identify the effects of interventions specific to academic issues associated with being at-risk for dropping out of school and psychosocial reasons for being at-risk. Qualitative studies exploring the expectations and efficacy beliefs of teachers of at-risk students in both traditional high schools and academic AECs could yield important information about teacher behaviors that help students succeed.

Additional studies might be conducted to describe the characteristics of students who choose to attend an academic alternative school. This investigation would offer insight for both traditional school and academic AEC administrators and teachers. The results of such a study might indicate other reasons for enrolling in an AEC, which would enable educators at traditional high schools to better address the psychosocial needs of at-risk students. Further studies are also necessary to explore the implications of the large standard deviations found in the per pupil expenditure data discovered during the data analysis.

### References

Adair, V. C. (2002). Poverty and the (broken) promise of education. Harvard Educational Review, 71, 217-239.

Alspaugh, J. (1998). The relationship of school-to-school transitions and school size to high school dropout rates. The High School Journal, 81, 154-161.

Baker, J., Bridger, R., Terry, T., & Winsor, A. (1997). Schools as caring communities: A relational approach to school reform. School Psychology Review, 26, 586-602.

Brown, B. W., & Russell, K. (1997). Methods of correcting for multiple testing: Operating characteristics. Statistics in Medicine, 16, 2511-2528.

Catterall, J. (1985). On the social costs of dropping out of schools. (Report No. 86-SEPT-3). Stanford, CA: Stanford University, Center for Educational Research.

Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale, NJ: Lawrence Erlbaum.

Croninger, R. G., & Lee, V. E. (2001). Social capital and dropping out of high school: Benefits to at-risk students of teachers' support and guidance. Teachers College Record, 103, 548-581.

Escobar-Chaves, S. L., Tortolero, C. M., Kelder, S. H., & Kapadia, A. (2002). Violent behavior among urban youth attending alternative schools. Journal of School Health, 72, 357-362.

Fine, M. (1986). Why urban adolescents drop in and out of public high school. Teachers College Record, 87, 393-409.

Finn, J. D. (2002). Class-size reduction in grades K-3. In A. Molnar (Ed.). School reform proposals: The research evidence (pp. 15–24). Tempe: Arizona State University Education Policy Research Unit.

Frediana, T. M. (2002). Meeting students’ individual needs. Leadership, 31, 24-25.

Glass, R. S. (1995, January). Alternative schools help kids succeed. Education Digest, 60(5), 21-24.

Gregory, T. (1992) Small is too big: Achieving a critical anti-mass in the high school. Source Book on School and District Size, Cost, and Quality. Minneapolis: Minnesota University, Hubert H. Humphrey Institute of Public Affairs. (ERIC Document Reproduction Service No. ED361159)

Harlow, C. W. (2003). Education and correctional populations: Bureau of Justice statistics special report. Washington, DC: U.S. Department of Justice.

Harris, A. (2006). When doing nothing becomes a viable life and career option: A growing trend among African American youth. Retrieved October 7, 2007, from http://enx.org/content/m14116/latest/

Howley, C. (1996). Compounding disadvantage: The effects of school and district size on student achievement in West Virginia. Journal of Research in Rural Education, 12, 25-32.

Intercultural Development Research Association. (2006, October). Texas public school attrition study. Retrieved August 13, 2007, from http://www.idra.org/IDRA_Newsletters/October_2007_School_Holding_Power/Texas_Public_School_Attrition_Study_2006_07/

Jordan, W. J., McPartland, J. M., Legters, N., & Balfanz, R. (2000). Creating a comprehensive school reform model: The talent development high school with career academies. Journal of Education for Students Placed At-Risk, 5(1,2), 159-181.

McDill, E. L., Natriello, G., & Pallas, A. M. (1985). Raising standards and retaining students: The impact of the reform recommendations on potential dropouts. Review of Educational Research, 55, 415-433.

McGee, J. (2001). Reflections of an alternative school administrator. Phi Delta Kappan, 82, 558-591.

Montecel-Robledo, M. (2005, November). Quality school action framework: Framing systems change for student success. IDRA Newsletter. San Antonio, TX: Intercultural Development Research Association, 10, 21-24.

Moretti, E. (2005). Does education reduce participation in criminal activities? Paper presented at The Symposium on the Social Cost of Inadequate Education, Teacher College, Columbia University, New York. Retrieved October 22, 2007, from http:www.tc.Columbia.edu/centers/EquityCampaign/symposium/speakers.asp?SpeakerID=9

National Center for Educational Statistics. (2002). Public alternative schools and programs for at risk of education failure: 2000-2001. Retrieved January 24, 2008, from http://nces.ed.gov/pubs2002/2002004.pdf

Pianta, R. C., & Walsh, D. J. (1996). High-risk children in schools: Contrasting sustaining relationships. New York: Routledge.

Raywid, M. A. (1999). Current literature on small schools. Charleston, WV: ERIC Clearinghouse on Rural Education and Small Schools.

Raywid, M., & Oshiyama, L. (2000). Musings in the wake of Columbine: What can schools do? Phi Delta Kappan 81, 444-449. Available: www.pdkintl.org/kappan/kray0002.htm

Rogers, C. (1969). Freedom to learn. New York: McMillan/Merrill.

Rousse, C. E. (2005, October). The labor market consequences of an inadequate education. Paper presented at the symposium on the Social Costs of Inadequate Education, New York. Retrieved October 19, 2007, from Teachers College, Columbia University Web site: http://www.tc.columbia.edu/centers/Equity Campaign/symposium/speakers.asp?SpeakerId+=11

Rumberger, R. W. (1983). Dropping out of high school: The influence of race, sex, and family background. American Educational Research Journal, 20, 199-220.

The Sentencing Project. (2003). Comparative international rates of incarceration: An examination of causes and trends. Retrieved October 20, 2007, from http://www.soros.org/initiatives/justice_ articles-publications/publications/intl-incareration-20030620/intl-rates.pdf

The Sentencing Project. (2004). New incarceration figures: Rising population despite falling crime rates. Retrieved October 20, 2007, from http://www.sentencingproject.org/pdfs/1044.pdf

Stewart, K., & Knaggs, B. (2005). The Texas High School Project: An innovative partnership to reinvent high schools. Texas Association of Secondary School Principals News Highlights, 45(1), 10-12.

Texas Education Agency. (2005a). Secondary school completion and dropouts (2004–2005). Austin, TX: Author. Retrieved October 15, 2007, from http://www.tea.state.tx.us/research/pdfs/dropcomp_2004-03.pdf#xml=http:///www.tea.state.tx.us/cgi/texis/webinator/search/xml.txt?query=com

Texas Education Agency. (2005b). Academic Excellence Indicator System. Austin, TX: Author. Retrieved September 17, 2006, from http://www.tea.state.tx.us/perfreporp/aeis/2005/state.html

Texas Education Agency. (2006). 2004–2005 State accountability: State Summary. Austin, TX: Author. Retrieved December 8, 2007, from http://www.tea.state.tx.us/perfreport/account/2004/statesummary.html

Texas Education Agency. (2007a). August 2007 alternative education accountability state table. Austin, TX: Author. Retrieved October 7, 2007, from http://www.tea.state.tx.us/aea/2007/index.html

Texas Education Agency. (2007b). Glossary for the Academic Excellence Indicator System 2006–2007. Retrieved October 9, 2007, from http://www.tea.state.tx.us/perfreport/aeis/2007/glossary.html

U.S. Bureau of the Census. (2000). Poverty in the United States. Retrieved October 20, 2007, from http://www.census.gov/hhes/www/poverty.html

U.S. Bureau of the Census. (2002). Educational attainment in the United States: Table 9. Retrieved October 29, 2007, from http://www.census.gov/hhhes/www/education/html

U.S. General Accounting Office. (2004). Highlights of a GAO forum: Workforce challenges and opportunities for the 21st century: Changing labor force dynamics and the role of government policies. Retrieved January 30, 2008, from http://www.gao.gov/htext/d04845sp.html

U.S. Department of Labor. (2007). Wages by area and occupation. Retrieved December 3, 2007, from http://www.dol.gov/dol/topic/wages/indes.htm

Wolk, R. (2000). Alternative answers. Teacher Magazine, 11(7), 6-10.

## Content actions

PDF | EPUB (?)

### What is an EPUB file?

EPUB is an electronic book format that can be read on a variety of mobile devices.

My Favorites (?)

'My Favorites' is a special kind of lens which you can use to bookmark modules and collections. 'My Favorites' can only be seen by you, and collections saved in 'My Favorites' can remember the last module you were on. You need an account to use 'My Favorites'.

| A lens I own (?)

#### Definition of a lens

##### Lenses

A lens is a custom view of the content in the repository. You can think of it as a fancy kind of list that will let you see content through the eyes of organizations and people you trust.

##### What is in a lens?

Lens makers point to materials (modules and collections), creating a guide that includes their own comments and descriptive tags about the content.

##### Who can create a lens?

Any individual member, a community, or a respected organization.

##### What are tags?

Tags are descriptors added by lens makers to help label content, attaching a vocabulary that is meaningful in the context of the lens.

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