Treffer: Alternative Route Programs within Special Education Teacher Preparation: A Systematic Literature Review

In the United States, there exists a chronic shortage of qualified special education teachers to provide instructional services to students with disabilities. One policy solution developed to increase the number of qualified teachers is "alternative routes," which are broadly defined as nontraditional and accelerated preparation paths to obtain a teaching license. In this systematic literature review, the authors synthesize empirical studies from 2005 to 2021, examining factors associated with alternative route programs specific to special education. First, an economic framework for the special education workforce is established. This is followed by a descriptive summary of alternative route research nested within the broader teacher labor markets. Then, a systematic literature search on alternative routes for special education teachers is analyzed. The authors found that most studies examined the role of teacher preparation cost (e.g., cost-effectiveness, federal spending), human capital (e.g., recruitment of culturally diverse teachers, location-specific capital), and program design (e.g., collaboration between stakeholders, technology). Furthermore, studies largely investigated alternative route program characteristics affiliated with institutions of higher education. The majority of researchers employed quantitative methods to analyze secondary state or survey data, while two studies used qualitative methods, and two utilized mixed methods. Implications for future research and policy recommendations needed within the recruitment and preparation of special education teachers are discussed.

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AN0189014792;[mmhi]01oct.25;2025Nov03.05:44;v2.2.500

Alternative Route Programs Within Special Education Teacher Preparation: A Systematic Literature Review 

In the United States, there exists a chronic shortage of qualified special education teachers to provide instructional services to students with disabilities. One policy solution developed to increase the number of qualified teachers is "alternative routes," which are broadly defined as nontraditional and accelerated preparation paths to obtain a teaching license. In this systematic literature review, the authors synthesize empirical studies from 2005 to 2021, examining factors associated with alternative route programs specific to special education. First, an economic framework for the special education workforce is established. This is followed by a descriptive summary of alternative route research nested within the broader teacher labor markets. Then, a systematic literature search on alternative routes for special education teachers is analyzed. The authors found that most studies examined the role of teacher preparation cost (e.g., cost-effectiveness, federal spending), human capital (e.g., recruitment of culturally diverse teachers, location-specific capital), and program design (e.g., collaboration between stakeholders, technology). Furthermore, studies largely investigated alternative route program characteristics affiliated with institutions of higher education. The majority of researchers employed quantitative methods to analyze secondary state or survey data, while two studies used qualitative methods, and two utilized mixed methods. Implications for future research and policy recommendations needed within the recruitment and preparation of special education teachers are discussed.

Keywords: education policy; multilingualism; special education; teacher preparation; teacher shortages

Across the United States, a chronic special education teacher shortage exists as there are not enough qualified teachers to provide instructional services to students with disabilities. These historical shortage concerns have been documented since the inception of the Individuals with Disabilities Education Act in 1975 ([3]) and currently exist as 98% of school districts report special education shortages ([36]). As a result, reasons for the shortage and potential policy solutions have been investigated. One potential policy solution to increase the teacher supply through recruitment and preparation initiatives is called Alternative Routes to Teaching Licensure. Alternative routes (ARs) are broadly defined as nontraditional and accelerated paths for individuals to obtain a state teaching license ([36]). AR preparation programs vary greatly across states regarding their program characteristics, requirements, and participants ([27]). However, these state licensure preparation programs generally attempt to increase the teacher supply by providing a variety of recruitment options for teachers to obtain licensure. AR programs leverage nontraditional recruitment techniques as one approach for attracting teachers. However, due to their heterogeneity, there is mixed evidence on the effect that ARs have on impacting general education teachers ([20]). Furthermore, there is limited research that illuminates how specific AR models contribute to the special education teacher population ([28]).

In this systematic literature review, we investigate AR preparation programs in special education. First, an economic supply and demand framework on the special education teacher pipeline is established. This is followed by a descriptive summary of AR research nested within general education and the synthesis of [28] review of AR programs in special education. Then, we describe our systematic literature search that we employed on recent AR research inclusive of special education teachers from 2005 to 2021. Implications for future research and policy recommendations needed within the special education teacher population to address the teacher shortage are also discussed.

An Economic Framework for Special Education Teacher Labor Markets

The primary framework for evaluating the special education teacher shortage comes from the economic conceptualization of teacher labor markets ([12]). Similar to other workforces, teacher labor markets refer to the supply and demand for labor. Teacher demand can be broadly defined as the number of positions a school offers at a given level of compensation. Teacher supply is the number of qualified teachers willing to fill the positions. Teacher shortages then occur when there is an excess demand for labor resulting from a lower supply of teachers within an existing wage rate ([20]). However, special education teacher shortages are defined specifically by the U.S. Department of Education. Since 2006, the shortage has been defined as the proportion of special education teachers who were not highly qualified ([36]). States are mandated to report annual special education shortages to the U.S. Department of Education. Therefore, to estimate shortage rates inclusive of teacher quantity, it is critical to develop a deeper understanding of the workforce pipeline specific to the supply and demand of special education teachers.

Supply and Demand

The special education teacher workforce is one subpopulation of the broad teacher population in the United States. It is especially pertinent to examine special education teachers as they instruct students with disabilities, whose academic and behavioral services are mandated by federal law ([36]). Special education teachers deliver instruction to students with disabilities, assess their academic and behavioral growth, and design evidence-based individualized education plans ([8]). Due to the critical shortage of special education teachers, it is imperative to examine the quantity of labor. Labor is defined as the amount of work a teacher is willing to give to produce an increase in student outcomes ([20]). Special education teachers are participants of broader labor markets with both a demand and a supply side. The supply side refers to the proportion of highly qualified special education teachers ([36]) and examines how special education teachers make occupational choices to enter, remain, or leave the teaching profession ([20]). The demand side determines the number of special education teachers a school can hire and considers the student with disability enrollment, per pupil expenditures, and special education teacher-to-student ratios ([23]).

The intersection of labor supply and demand establishes both market compensation (C*) and employment (E*). Special education teacher shortages occur when the teacher supply is lower than the demand at an existing compensatory rate (see Figure 1). Fortunately, there are various labor pipelines that contribute to the special education teacher supply, which include teachers who recently earned their state certification through preparation programs.

Graph: Figure 1: Note: The intersection of supply and demand determines both compensation (C*) and employment (E*). Both the supply and demand for special education teachers are influenced by federal, state, and local public policy. This figure is adapted from [20].

Alternative Routes to Teaching Licensure and Teacher Labor Markets

Teacher certification policies determine the supply of teachers by establishing licensure requirements. Their aim is to regulate the teacher population by requiring sufficient training and qualifications ([20]). Teaching certifications are set by each state and thus differ across the nation. Initial certification requirements may include a set level of education, completing a teacher preparation program, passing content certification exams, and finishing a specific number of preservice field hours mandatory to enter the teaching profession ([36]). While teaching certification policies aim to ensure a minimum teacher quality, there are some affiliated barriers that influence potential teachers' access to the profession. A major barrier is that it limits the recruitment of teachers and thus impacts the teacher supply. This is due to the high cost and the time it takes to become a licensed teacher in a traditional teacher preparation program ([26]). Traditional preparation programs are within institutes of higher education (IHEs) and often require 4 to 5 years to obtain licensure through a certified Bachelor or Master of Education program. Therefore, a broad teacher policy initiative has been developed to provide a cost- and time-effective option called AR preparation programs.

The Heterogeneity of AR Preparation Programs

While most states permit them, AR preparation programs are difficult to define due to their vast models of requirements, implementation, and participants ([28]). Because of their heterogeneity, an AR preparation program may look very different from one state to another and sometimes display the program characteristics of traditional preparation programs ([29]). ARs have been permitted by the federal government since the mid-1980s and are broadly defined as nontraditional and accelerated paths for individuals to obtain a state teaching license ([36]). While the federal government allowed for their initial existence, state licensure offices determine AR policies and define the pathways to a teaching credential.

Generally, the purpose of state ARs is to recruit teachers who do not have a traditional education preparation background to fill high-needed teaching areas ([20]). Policies vary by state, but the majority of ARs share the commonality of hiring individuals who have a bachelor's degree but lack education certification and training. AR teachers instead often participate in internship models under provisional licenses and earn a state teaching license after AR completion ([36]). Thus, teachers who are enrolled and complete preparation in ARs are part of a broader teacher pipeline that contributes to the overall supply of teachers ([19]).

The Conceptual Framework of Teacher Pipelines

To determine a labor shortage or surplus, AR teachers are conceptually part of a vast teacher pipeline that constructs the teacher supply ([19]). These various supply sources include (a) teachers retained from the previous year, (b) teachers migrating from out of state, (c) newly certified teachers from traditional preparation programs, (d) newly certified teachers from ARs, and (d) teachers who are certified to teach but are not currently in the workforce (e.g., family leave, medical leave). These teacher supply sources interact with teacher demand variables (e.g., student enrollment, per pupil expenditures, and teacher-to-student ratio) to then determine labor shortages or surpluses (see Figure 2). The teacher pipeline conceptual model can be used when empirically evaluating ARs and the special education teacher shortage. For the purposes of our literature investigation, we focus on one supply source, teachers from AR preparation programs.

Graph: Figure 2: Note: This figure is adapted from [19].

General Education AR Programs

Although there are approximately 463,200 special educators in the United States ([35]), there is a dearth of research conducted on ARs that is inclusive of special education. Recent AR research has instead been largely conducted on the general education teacher population (e.g., [5]; [6]; [12]; [14]; [30]; [39]). These findings reveal that approximately 20% of new teachers are entering the workforce through AR programs ([10]). However, investigations on ARs and general education teachers provide mixed evidence on the impact ARs have on teacher quantity and quality. Some posit that there is no statistical difference between AR teachers and those who are traditionally certified regarding their effectiveness ([39]), while others perpetuate that AR teachers produce stronger significant student outcomes in math or reading ([6]; [12]; [40]). Yet, these studies limit their research to Teach for America or Teaching Fellows, AR pathways that recruit graduates from prestigious universities to teach in urban schools. Teach for America and Teaching Fellow teachers only make up a small portion of alternatively prepared teachers and do not reflect the larger AR teacher population in various geographic settings.

To address this gap in research, [30] examined the effects of state alternative certification programs that have no special recruitment efforts. The AR programs in his investigation consisted of a variety of nontraditional options in Florida: the district alternative certification, the educator preparation institute, the American Board for Certification of Teacher Excellence passport, and the college-teaching experience options. Sass identified the district alternative certification program as the most common AR program in Florida. The district alternative certification program is drastically different than Teach for America or Teaching Fellows in that it does not involve specific recruitment procedures and participating teachers are not required to complete additional coursework. Instead, teachers are required to pass the standard general knowledge and professional education certification exams to become certified. Additionally, teachers are required to complete a competency-based alternative certification program that varies by school district but generally consists of an initial assessment of skills, an individualized training plan, mentoring, a training curriculum of research-based teacher practices, and a summative assessment that evaluates mastery of the practices. The AR district programs are typically web-based and often involve the collaboration of IHEs in addition to the local education agencies (LEAs). Regarding teacher supply, Sass found that these AR teachers have stronger preservice academic skills than traditionally prepared teachers, as evidenced by their higher initial pass rates on certification exams and higher college entrance exam scores. Additionally, these general AR teachers diversified the teacher workforce as it increased the number of males, minorities, and older teachers entering the teaching profession.

Furthermore, [30] employed a value-added model to evaluate the effectiveness of AR teachers compared to those traditionally certified. He found that teachers who receive state certification from these AR programs are more effective in producing student outcomes. This is evidenced by the sample of AR teachers producing 1% to 2% of standard deviation higher student achievement math and reading scores than those traditionally certified teachers. In both of Sass's empirical models, he investigated the impact of the aforementioned AR programs on general education teachers who did not teach in inclusive classrooms. As a result, his analyses did not encompass special education teachers or the outcomes of students with disabilities. Within the field of special education, [28] previously conducted a literature review on the proliferation of ARs within special education teacher preparation.

Empirical Literature Within Special Education

[28] conducted their search by analyzing 10 data-based studies of AR preparation in special education. These empirical studies were conducted from 1986 to 2004 and varied in their methodologies, selected AR program models, and participants. Specifically, Rosenberg and Sindelar summarized the efficacy of the various AR approaches and programs. The researchers found six studies that investigated AR program evaluations and four studies that compared AR features to traditional teacher preparation programs. Overall, it was concluded that there were a variety of AR programs but a shortage of reliable evidence in terms of their nature and efficacy. With the limited findings, Rosenberg and Sindelar suggested the need for (a) meaningful collaboration between IHEs and LEAs, (b) adequate AR program length with a variety of learning activities, (c) and IHE supervision and building-based mentor support for AR special education teachers. Therefore, this systematic literature review extends Rosenberg and Sindelar's investigation of the empirical evidence that exists on AR preparation programs in special education.

The aim of our investigation is to expand and update [28] literature review by synthesizing empirical research conducted on ARs and special education teachers within the last 16 years. The purpose of this systematic literature review is to analyze previous research conducted on AR preparation programs to evaluate any potential impacts on the special education teacher pipeline. Therefore, this study aims to investigate the following:

Method

A systematic literature search was performed to summarize primary research previously conducted on the impact of ARs on teaching licensure in the special education teacher pipeline. [11] conceptualize that the purpose of systematic literature reviews is to identify, appraise, and synthesize all the empirical evidence that meets predetermined eligibility criteria to answer the researchers' questions. Furthermore, [24] posits that a key component of systematic literature reviews includes equal coverage of all methodologies in a transparent and extensive search. Therefore, we purposefully selected a systematic literature review for our method because we wanted to comprehensively evaluate all empirical literature that employed various empirical investigations (e.g., quantitative, qualitative, and mixed methods) that were inclusive of special education teachers in ARs. Following [21], we created a transparent review protocol to outline search procedures and the coding framework.

Search Procedures

The systematic literature search was initially conducted by utilizing the electronic databases of Education Research Complete, PyscINFO, and Academic Search Complete. The search terms that were identified consisted of alternative routes and special education teachers or teacher licensure or nontraditional pathways and related terms. Using [28] review as a start date, the scope of the search ranged from 2005 to December 2021. The initial search yielded 617 results that used some combination of the selected terms chosen after duplicate studies were removed. This literature review focused on the empirical research specifically conducted on special education teachers in AR programs.

Next, a hand search was conducted in EBSCO for the education policy journals of Educational Policy, Educational Evaluation and Policy Analysis, and the Journal of Education Finance. These three peer-reviewed education policy journals were selected for a hand search due to their history of containing AR literature within general education (e.g., [5]; [6]; [12]; [14]; [30]; [39]). This yielded no additional results that were inclusive of special education teachers within their AR findings, as the studies focused on other teacher certification areas, did not specify certification areas, or the findings were not disaggregated by special education teacher certification status. Then, a hand search was conducted on the Office of Special Education's website "Attract, Prepare, Retain: Effective Personnel for All." This yielded no additional results, as the studies found were either (a) duplicate studies of the electronic search, (b) did not mention special education teachers, or (c) special education AR program recommendations and therefore were not empirical studies. The policy hand searches were purposefully conducted given that education policy researchers and policymakers also investigate ARs to teaching licensure.

Finally, ancestry and progeny searches were completed. The ancestry search was completed by investigating the reference section of each study that met the criteria, and 21 studies were identified as potential articles. A progeny search was also conducted with Google Scholar by reviewing the titles and abstracts that referenced [28] literature review, which yielded 35 additional studies that were identified. After removing duplicates, 478 articles remained and served as the sample for screening analysis.

Inclusion and Exclusion Criteria

The title, author, and abstract of each potential article (N = 478) were screened to see if they met the following criteria: (a) was an empirical study that consisted of quantitative, qualitative, or mixed methodology; (b) was peer-reviewed; (c) included special education teachers; (d) addressed ARs; (e) was conducted in the United States; and (f) was written in English. We included empirical work with and without student outcome measures (e.g., empirical work that focused on special education teachers). Additionally, we included empirical studies that only contained special education participants and included a study if the researchers investigated special education and non–special education participants with the requirement that the data for the special education was disaggregated. For example, [25] used the Schools and Staffing Survey and the National Teacher and Principal Survey to analyze newly certified teachers from state AR policies. Redding reported that new special education teachers were more likely to graduate from traditional preparation programs than AR preparation programs. However, the findings on the impact of AR policies on the composition of teachers in terms of their previously held knowledge signals (e.g., SAT scores), diversity (e.g., age, gender, race), and their participation in a particular AR program design (e.g., Teach for America, Grow Your Own) were not parceled out for special education teachers. As a result, this empirical study was not found eligible.

Additionally, articles that were nonempirical program evaluations for special education ARs were excluded. AR design recommendations intended for special education teachers were also excluded because the articles did not meet the empirical study criteria. For example, [38] publication, "Alternative Route Special Education Teacher Preparation Programs Guidelines," was not included because it was not an empirical study. Rather, the researchers provided research-based guidelines intended to assist teacher educators in the development of AR programs. Furthermore, if studies broadly investigated teachers and did not disaggregate by concentration area (i.e., special education), they were excluded from the findings. This resulted in 441 articles being excluded because they did not meet the inclusion criteria. Finally, full-text articles (n = 37) were then screened using the same criteria. This led to the selection of 11 articles to be included for data analysis (see Figure 3).

DIAGRAM: Figure 3: Note: This model is adapted from [21].

Data Analysis

To conduct a thematic content analysis of the articles, holistic summaries were written for each of the 11 studies included in the final review (Van Miegham et al., 2020). Holistic summaries consisted of each study's research questions/purposes, methodology, sample, and results. Afterward, an analysis table was developed to thoroughly examine each study's findings related to three factors: (a) cost, (b) human capital, and (c) program design. These deductive themes were based on prior AR literature reviews conducted in general education ([30]; [39]) and special education ([28]). Finally, the 11 studies were coded on all relevant variables identified a priori: outcomes on teacher quality or quantity associated with AR program models or AR state licensure policies. The 11 studies were coded separately, and subthemes were identified for each of the three deductive themes (e.g., cost, human capital, and program design) through the creation of time-intensive coding.

Results

First, a synopsis of the 11 studies is described in terms of their methodological approaches, data sources, samples, and their empirical aim of investigating how ARs contribute to the special education teacher supply and/or quality (see Table 1). This is followed by a synthesis of the three themes found in the AR research: (a) cost, (b) human capital, and (c) program design. The subthemes that were analyzed for each thematic category are also discussed.

Table 1: Study Characteristics

<table rules="groups"><colgroup span="1"><col align="left" valign="middle" span="1" /><col align="left" valign="middle" span="1" /><col align="left" valign="middle" span="1" /><col align="left" valign="middle" span="1" /><col align="left" valign="middle" span="1" /><col align="left" valign="middle" span="1" /></colgroup><thead><tr><th align="left" valign="bottom" rowspan="2" colspan="1">Citation</th><th align="left" valign="bottom" rowspan="2" colspan="1">Methodology</th><th align="left" valign="bottom" colspan="2" rowspan="1">Empirical aim</th><th align="left" valign="bottom" rowspan="2" colspan="1">Data Source</th><th align="left" valign="bottom" rowspan="2" colspan="1">Sample</th></tr><tr><th align="left" valign="bottom" rowspan="1" colspan="1">Teacher Supply</th><th align="left" valign="bottom" rowspan="1" colspan="1">Teacher Quality</th></tr></thead><tbody><tr><td align="left" valign="top" rowspan="1" colspan="1"><xref ref-type="bibr" rid="bibr2">Ault et al. (2019)</xref></td><td align="left" valign="top" rowspan="1" colspan="1">Mixed Methods</td><td align="left" valign="top" rowspan="1" colspan="1" /><td align="left" valign="top" rowspan="1" colspan="1">X</td><td align="left" valign="top" rowspan="1" colspan="1">Field Placement form; Survey protocol for observed teachers; Survey protocol for university observers</td><td align="left" valign="top" rowspan="1" colspan="1">4 SETs enrolled in an AR program; 2 SPED university supervisors</td></tr><tr><td align="left" valign="top" rowspan="1" colspan="1">Casey et al. (2011)</td><td align="left" valign="top" rowspan="1" colspan="1">Mixed Methods</td><td align="left" valign="top" rowspan="1" colspan="1">X</td><td align="left" valign="top" rowspan="1" colspan="1">X</td><td align="left" valign="top" rowspan="1" colspan="1">Online survey consisting of both rating scale and open-ended questions</td><td align="left" valign="top" rowspan="1" colspan="1">89 SET AR completers</td></tr><tr><td align="left" valign="top" rowspan="1" colspan="1"><xref ref-type="bibr" rid="bibr15">Hollo et al. (2019)</xref></td><td align="left" valign="top" rowspan="1" colspan="1">Quantitative Descriptive Analysis</td><td align="left" valign="top" rowspan="1" colspan="1">X</td><td align="left" valign="top" rowspan="1" colspan="1" /><td align="left" valign="top" rowspan="1" colspan="1">U.S. Department of Education's National Center for Education Statistics; State education agencies' websites; Phone calls to state licensure offices</td><td align="left" valign="top" rowspan="1" colspan="1">50 state licensure offices</td></tr><tr><td align="left" valign="top" rowspan="1" colspan="1"><xref ref-type="bibr" rid="bibr16">Jameson et al. (2019)</xref></td><td align="left" valign="top" rowspan="1" colspan="1">Quantitative Descriptive Analysis</td><td align="left" valign="top" rowspan="1" colspan="1">X</td><td align="left" valign="top" rowspan="1" colspan="1" /><td align="left" valign="top" rowspan="1" colspan="1">Census data; AR cohort data from the University of Utah</td><td align="left" valign="top" rowspan="1" colspan="1">73 SETs enrolled in an AR program</td></tr><tr><td align="left" valign="top" rowspan="1" colspan="1"><xref ref-type="bibr" rid="bibr18">Kurtts et al. (2007)</xref></td><td align="left" valign="top" rowspan="1" colspan="1">Qualitative</td><td align="left" valign="top" rowspan="1" colspan="1">X</td><td align="left" valign="top" rowspan="1" colspan="1" /><td align="left" valign="top" rowspan="1" colspan="1">Focus group data</td><td align="left" valign="top" rowspan="1" colspan="1">34 SETs enrolled in an AR program</td></tr><tr><td align="left" valign="top" rowspan="1" colspan="1"><xref ref-type="bibr" rid="bibr26">Robertson & Singleton (2010)</xref></td><td align="left" valign="top" rowspan="1" colspan="1">Quantitative Descriptive Analysis</td><td align="left" valign="top" rowspan="1" colspan="1">X</td><td align="left" valign="top" rowspan="1" colspan="1" /><td align="left" valign="top" rowspan="1" colspan="1">University of Memphis's alternative or traditionally certified program data; Memphis school districts employment data</td><td align="left" valign="top" rowspan="1" colspan="1">183 SET AR completers; 190 SET TPP completers</td></tr><tr><td align="left" valign="top" rowspan="1" colspan="1"><xref ref-type="bibr" rid="bibr27">Rosenberg et al. (2007)</xref></td><td align="left" valign="top" rowspan="1" colspan="1">Quantitative Descriptive Analysis</td><td align="left" valign="top" rowspan="1" colspan="1">X</td><td align="left" valign="top" rowspan="1" colspan="1" /><td align="left" valign="top" rowspan="1" colspan="1">State departments of education; Survey on AR characteristics</td><td align="left" valign="top" rowspan="1" colspan="1">235 SPED AR programs, of which 101 program directors responded to the survey</td></tr><tr><td align="left" valign="top" rowspan="1" colspan="1"><xref ref-type="bibr" rid="bibr31">Scott (2019)</xref></td><td align="left" valign="top" rowspan="1" colspan="1">Qualitative</td><td align="left" valign="top" rowspan="1" colspan="1">X</td><td align="left" valign="top" rowspan="1" colspan="1" /><td align="left" valign="top" rowspan="1" colspan="1">Semi-structured interviews</td><td align="left" valign="top" rowspan="1" colspan="1">9 SETs enrolled in ARs; 6 SET AR completers</td></tr><tr><td align="left" valign="top" rowspan="1" colspan="1"><xref ref-type="bibr" rid="bibr32">Scott et al. (2019)</xref></td><td align="left" valign="top" rowspan="1" colspan="1">Quantitative</td><td align="left" valign="top" rowspan="1" colspan="1">X</td><td align="left" valign="top" rowspan="1" colspan="1" /><td align="left" valign="top" rowspan="1" colspan="1">Surveys derived from national standards (e.g., Council for Exceptional Children) and state preparation standards (e.g., Virginia Standards of Learning)</td><td align="left" valign="top" rowspan="1" colspan="1">93 SET AR completers</td></tr><tr><td align="left" valign="top" rowspan="1" colspan="1"><xref ref-type="bibr" rid="bibr33">Sindelar et al. (2012)</xref></td><td align="left" valign="top" rowspan="1" colspan="1">Quantitative Descriptive Analysis</td><td align="left" valign="top" rowspan="1" colspan="1">X</td><td align="left" valign="top" rowspan="1" colspan="1" /><td align="left" valign="top" rowspan="1" colspan="1">Semi-structured interviews; Surveys; Department of Labor; Bureau of Labor Statistics</td><td align="left" valign="top" rowspan="1" colspan="1">224 SET AR completers; 31 SPED AR directors</td></tr><tr><td align="left" valign="top" rowspan="1" colspan="1"><xref ref-type="bibr" rid="bibr34">Sutton et al. (2014)</xref></td><td align="left" valign="top" rowspan="1" colspan="1">Quantitative Descriptive Analysis and Quasi-experimental design</td><td align="left" valign="top" rowspan="1" colspan="1">X</td><td align="left" valign="top" rowspan="1" colspan="1" /><td align="left" valign="top" rowspan="1" colspan="1">South Carolina Department of Education Labor Statistics; U.S. Department of Agriculture Statistics</td><td align="left" valign="top" rowspan="1" colspan="1">638 SET AR completers</td></tr></tbody></table>

Note: AR = alternative routes; SET = special education teachers; SPED = special education; TPP = traditional preparation program.

Study Characteristics

Methodologies of the studies varied, with the most frequent being quantitative descriptive analysis (64%), followed by qualitative (18%) and mixed methodologies (18%). Additionally, one study ([34]) that initially employed a quantitative descriptive analysis also conducted a quasi-experimental study to evaluate the impact of AR licensure on teacher placement. Experimental methodologies were not conducted on the special education teacher population, as the majority of studies (90%) were largely descriptive or exploratory.

Data Sources

In six studies, researchers used secondary databases that consisted of state employment records, university enrollment data, or federal databases to explore AR program design by geographic region ([15]; [16]; [26]; [27]; [33]; [34]). Three studies employed open-ended and Likert scale surveys ([2]; Casey et al., 2011; [27]; [33]) to assess the impact and characteristics of ARs in special education. Whereas three studies employed semistructured interviews to assess perceptions of AR special education teachers ([31]; [32]; [33]), one conducted a focus group ([18]) to evaluate the experiences of AR special education teachers.

Samples

Studies varied in terms of the samples that were investigated. Most studies (82%) included PK–12 special education teachers who were enrolled in ARs or had recently completed AR preparation. This consisted of five studies examining special education teachers (n = 120) who were currently enrolled in an AR program (e.g., [2]; [16]; [18]; [31]; [33]) and six studies that investigated special education teachers (n = 1,233) in the field who had previously completed an AR program (e.g., Casey et al., 2011; [26]; [31]; [33]; [34]). Some studies (27%) utilized university faculty (n = 134) in their sample, which consisted of teacher preparation supervisors and program directors (e.g., [2]; [27]; [33]). One study (e.g., [15]) did not examine human participants but rather investigated AR program trends by accessing data from state licensure offices (N = 50). Furthermore, six of the empirical studies (50%) investigated specific AR program models and provided detailed characteristics. The reported AR program models were heterogeneous in terms of their state settings, participants, and preparation requirements. However, all studies that investigated a specific AR program in detail reported program provider collaboration between IHE, SEA, and LEA partners (see Table 2).

Table 2: Alternative Route Program Characteristics

<table rules="groups"><colgroup span="1"><col align="left" valign="middle" span="1" /><col align="left" valign="middle" span="1" /><col align="left" valign="middle" span="1" /><col align="left" valign="middle" span="1" /><col align="left" valign="middle" span="1" /><col align="left" valign="middle" span="1" /></colgroup><thead><tr><th align="left" valign="bottom" rowspan="1" colspan="1">Source</th><th align="left" valign="bottom" rowspan="1" colspan="1">Participants</th><th align="left" valign="bottom" rowspan="1" colspan="1">Setting</th><th align="left" valign="bottom" rowspan="1" colspan="1">Progarm Providers</th><th align="left" valign="bottom" rowspan="1" colspan="1">Requirements</th></tr></thead><tbody><tr><td align="left" valign="top" rowspan="1" colspan="1"><xref ref-type="bibr" rid="bibr2">Ault et al. (2019)</xref></td><td align="left" valign="top" rowspan="1" colspan="1">Provisionally licensed SETs serving as a teacher of record</td><td align="left" valign="top" rowspan="1" colspan="1">Kentucky </td><td align="left" valign="top" rowspan="1" colspan="1">IHE, SEA, LEA</td><td align="left" valign="top" rowspan="1" colspan="1">2-year, graduate distance preparation program with the University of Kentucky; online synchronous coursework, observations from IHE supervisor every semester, and mentor provided from LEA</td></tr><tr><td align="left" valign="top" rowspan="1" colspan="1"><xref ref-type="bibr" rid="bibr16">Jameson et al. (2019)</xref></td><td align="left" valign="top" rowspan="1" colspan="1">Preservice SETs not serving as a teacher of record</td><td align="left" valign="top" rowspan="1" colspan="1">Utah</td><td align="left" valign="top" rowspan="1" colspan="1">IHE, SEA, LEA</td><td align="left" valign="top" rowspan="1" colspan="1">2-year, graduate distance preparation cohort with the University of Utah; asynchronous and synchronous coursework, IHE and LEA supervised field experiences, produced comprehensive portfolio, Special Education and Elementary Education PRAXIS</td></tr><tr><td align="left" valign="top" rowspan="1" colspan="1"><xref ref-type="bibr" rid="bibr18">Kurtts et al. (2007)</xref></td><td align="left" valign="top" rowspan="1" colspan="1">Preservice SETs not serving as a teacher of record and were identified as underrepresented groups and limited finances: completed high school or GED, 24 years or older and recently enrolled in a community college, or were employed paraprofessionals</td><td align="left" valign="top" rowspan="1" colspan="1">North Carolina</td><td align="left" valign="top" rowspan="1" colspan="1">IHE, SEA, LEA</td><td align="left" valign="top" rowspan="1" colspan="1">127-semester-credit undergraduate program at the University of North Carolina at Greensboro in high incidence disabilities; in-person coursework, 100 hours of fieldwork experience, two IHE mentors to support observations, research skills, writing skills, interview skills, and provided PRAXIS workshops</td></tr><tr><td align="left" valign="top" rowspan="1" colspan="1"><xref ref-type="bibr" rid="bibr26">Robertson and Singleton (2010)</xref></td><td align="left" valign="top" rowspan="1" colspan="1">Provisionally licensed SETs serving as a teacher of record</td><td align="left" valign="top" rowspan="1" colspan="1">Tennessee</td><td align="left" valign="top" rowspan="1" colspan="1">IHE, SEA, LEA</td><td align="left" valign="top" rowspan="1" colspan="1">2-year, Master of Education program at the University of Memphis; 43&#8211;46 credit hours of coursework completed in summers</td></tr><tr><td align="left" valign="top" rowspan="1" colspan="1"><xref ref-type="bibr" rid="bibr32">Scott et al. (2019)</xref></td><td align="left" valign="top" rowspan="1" colspan="1">Provisionally licensed SETs serving as a teacher of record</td><td align="left" valign="top" rowspan="1" colspan="1">Virginia</td><td align="left" valign="top" rowspan="1" colspan="1">IHE, SEA, LEA</td><td align="left" valign="top" rowspan="1" colspan="1">27-credit licensure program delivered online with synchronous and asynchronous coursework with various in-state universities</td></tr><tr><td align="left" valign="top" rowspan="1" colspan="1"><xref ref-type="bibr" rid="bibr34">Sutton et al. (2014)</xref></td><td align="left" valign="top" rowspan="1" colspan="1">Paraprofessionals with AA degrees</td><td align="left" valign="top" rowspan="1" colspan="1">South Carolina</td><td align="left" valign="top" rowspan="1" colspan="1">IHE, SEA, LEA</td><td align="left" valign="top" rowspan="1" colspan="1">Participate in regional teacher reeducation centers at universities across the state to obtain licensure; specific amount of coursework, fieldwork requirements, and length of program are not disclosed</td></tr></tbody></table>

Note: AA = associate's degree; BA = Bachelor of Arts; GED = General Educational Development; IHE = institution of higher education; LEA = local education agency; SEA = state education agency; SET = special education teacher.

Empirical Aims

As shown in Table 1, special education teacher supply (91%) was the most common empirical focus when investigating the impact of ARs. Studies ranged from examining special education teacher frequencies produced by ARs (e.g., [15]; [16]; [27]; [33]; [34]), teacher retention through ARs (e.g., [26]), and perceptions of AR programs on the special education teacher population (Casey et al., 2011; [18]; [31]; [32]). Two studies (e.g., [2]; Casey et al., 2011) focused on how AR programs contribute to teacher quality. [2] examined the classroom observation scores of AR special education teachers, whereas Casey et al. (2011) investigated the perceived additional support AR teachers need to improve their quality. Both studies were descriptive, and the authors did not find any causal data.

Cost of AR Programs

In terms of themes present in the empirical literature, the cost of AR programs influenced the special education teacher supply through recruitment efforts (e.g., [2]; Casey et al., 2011; [15]; [16]; [18]; [26]; [27]; [31]; [33]). We evaluated the role that cost has on teachers choosing to enter AR programs due to its cost-effectiveness, and the role of the federal government allocating funds to AR preparation programs. The two common subthemes of cost in AR programs included (a) special education teachers choosing an AR program over a traditional preparation program due to its efficient affordability and (b) the importance of federal funding for ARs to increase special education teacher recruitment.

Cost-Effectiveness

AR programs increase the special education teacher supply due to their cost-effectiveness compared to traditional teacher preparation programs ([26]). Cost-effectiveness can be defined as the efficiency of increasing teacher supply due to the lower cost and time it takes to become licensed ([33]). For example, a 1-year teacher preparation program with tuition waivers would be more cost-effective for a prospective teacher candidate compared to a 4-year teacher preparation program at an expensive ivy league university. Time was reported as a major contributor to ARs' cost-effectiveness (Casey et al., 2011; [18]; [27]). Special education teachers valued that they were able to become licensed in a shorter time and/or become licensed while working as a provisional special education teacher while serving as a teacher of record. For example, Casey et al. (2011) investigated the role of ARs on novice bilingual and special education teachers (n = 89) already in the workforce in an urban southwestern state. While the participants reported completing a variety of AR state programs, 92% of the AR teachers reported that the length of time it took to become certified was important and 86% reported that the cost of tuition was important when choosing AR preparation over traditional teacher preparation programs. Moreover, [31] reported that the participants in his study would not have become special education teachers if they had not simultaneously worked as provisional teachers while completing an AR program. The abbreviated time it took to become licensed in ARs and the flexibility both contributed to special education teachers entering the workforce.

Furthermore, AR programs were found to be financially affordable compared to traditional preparation programs. [33] calculated cost tables of obtaining special education teacher licensure in AR programs compared to traditional preparation programs. Costs varied regarding the type of alternative program, with the average cost per completer ranging from $5,567 for local programs to $14,522 for internship programs and $14,318 for step-up programs. Compared to licensure programs in traditional programs, all AR options were cheaper as traditional preparation programs' costs exceeded $31,000. Therefore, ARs are deemed cost effective to the extent that they contribute to teacher supply by providing access to potential teachers who may not otherwise join the special education workforce due to time or financial restraints.

Federal Spending

Findings in cost also posited the importance of federal funding to AR programs in various participating states ([15]). In [27] investigation of AR characteristics, 31.6% of AR program directors mentioned the impact of federal government funds from the Office of Special Education Programs (OSEP). [16] found that federal funding significantly increased special education teacher recruitment and retention through established cohorts. Initially, the researchers found that special education teachers recruited in both OSEP- and state-funded programs benefited from the grant stipends as the participants would not have been able to manage the financial burden without them. An in-depth analysis then revealed that the OSEP grants had additional resources and a more robust mechanism for tracking the completion of special education teachers fulfilling their working obligation contracts compared to state grants. Thus, federal funding stimulated an increased special education teacher recruitment and licensed supply remaining in the workforce.

Human Capital

Human capital, or workers' individual attributes that have value in labor markets, influence the special education workforce ([20]). The goal of the education policy reforms is to efficiently invest in special education teachers who will bring personal assets to the classroom that will increase student outcomes. All 11 studies (100%) investigated the role human capital theory plays in AR programs. The subthemes that were found consisted of (a) cultural and linguistic diversity, (b) location-specific capital, and (c) observed knowledge signals of special education teachers in ARs.

Cultural and Linguistic Diversity

Findings from eight studies (73%) revealed that AR programs diversified the special education teacher supply in terms of recruiting and employing culturally and/or linguistically diverse teachers (e.g., Casey et al., 2011; [18]; [26]; [27]; [31]; [32]; [33]; [34]). For example, [34] examined building special education teacher capacity in rural areas through Grow Your Own programs. Their sample of AR special education teachers (n = 638) were largely provisionally licensed and composed of 26% individuals identifying as a racial minority (e.g., African American, American Indian, Asian American, and Hispanic). In their analysis, the researchers found this rate to exceed the minority presence (15%) in the national special education teacher population. These findings are in line with recent calls from the field to diversify the special education teacher workforce ([36]).

Compared to traditional preparation programs, [26] found that AR programs recruited and employed more racially diverse special education teachers. The researchers investigated the effectiveness of an IHE AR program in terms of supplying certified teachers to school districts and their retention within the special education teacher workforce. The particular IHE AR program was a Master of Arts in Teaching with 43 to 46 hours of summer coursework at the University of Memphis. In their sample of special education teachers who completed ARs (N = 373), 59% of AR teachers who identified as African American (n = 72) were employed after 5 years, whereas only 50% of teachers who were identified as African American (n = 42) were still employed. Furthermore, [32] expanded on previous research positing that culturally diverse teachers are an asset to their student populations and AR programs are more appealing to Black males interested in becoming special education teachers. The researchers found that AR program personnel successfully recruited Black male special education teachers due to cheaper tuition, flexible work schedules, and on-the-job training. One participant expressed the importance of his AR program, declaring,

As a Black man, I could not see myself enrolling in a teacher preparation program where I [as a Black man] was not represented. . . this program allowed me to be represented and I would recommend this route to any Black guy that wants to teach special education.

([31], p. 340)

Regarding linguistic diversity, Casey et al. (2011) was the only study that investigated multilingual teachers in ARs. In their comparison study, the researchers evaluated the experiences of novice special education teachers and bilingual education teachers who were prepared in an AR program. However, their research was not inclusive of multilingual special education teachers, as it was only investigated for the bilingual certified teacher sample.

Location

Teachers have location-specific capital in that they often live where they are trained and then employed ([9]). Seven studies (70%) in our search concluded that AR programs increase special education teacher supply in hard-to-staff locations (e.g., [2]; Casey et al., 2011; [15]; [16]; [26]; [33]; [34]). Due to the flexibility of AR programs, special education teacher supply was increased in rural (n = 4 studies) and urban areas (n = 1 study). Additionally, AR programs capitalized on special education paraprofessionals advancing their careers, who have local community ties (n = 4 studies).

Rural Areas

School and district leaders in rural areas have historically reported special education teacher shortages as special education teachers are geographically remote and often far from traditional preparation programs. Additionally, the unique economic characteristics of rural communities often make it difficult for PK–12 schools to attract teachers who typically are trained in urban and suburban areas ([16]). Four studies emphasized the importance of high-quality AR pathways to support rural teacher training in special education (e.g., [2]; [15]; [16]; [34]). All four studies found that AR programs that had online distance training provided location flexibility for rural special education teachers. For example, [2] examined an AR program affiliated with the University of Kentucky to prepare special education teachers working with students with moderate to severe disabilities. A key component of this IHE AR program was the incorporation of virtual observations in field placements. From survey analysis, the researchers found that ARs that utilized virtual observations enabled lower cost benefits for both the student and the university. There were no significant differences found in observation scores from online to face-to-face observations. AR programs thus increased special education teacher supply through local education agency partnerships in rural settings.

Urban Areas

Although AR research in urban settings is abundant for the general education teacher population, (e.g., [6]; [12]; [39]; [40]) only one special education study examined ARs in urban areas (e.g., Casey et al., 2011). Casey et al. (2011) explored novice special education teachers' perceptions of the additional supports they needed after AR preparation. Findings from the participants (n = 89) included additional support requested on navigating (a) parent communication (90.4%) and (b) understanding cultural differences (84.6%) within their urban school setting. Author implications included recommended mentor training within AR preparation specific to urban school settings.

Paraprofessionals and Local Communities

Supporting paraprofessionals to become certified special education teachers through ARs has its known advantages. First, paraprofessionals have job-specific human capital in PK–12 public schools because they already have experience working with children and navigating the school system on the job ([9]). Second, many paraprofessionals also have location-specific human capital due to their preexisting relationships with students, families, and local communities due to their role ([9]). This is especially crucial in preparing teachers to work in hard-to-fill school locations and teacher roles, such as special education.

Four studies illuminated the significance ARs had on advancing the careers of paraprofessionals to become certified special education teachers (e.g., [18]; Rosenberg et al., 2017; [33]; [34]. For example, [18] reported that 35% of their AR participants (n = 34) consisted of paraprofessionals, which represented the largest workforce group compared to other career options (e.g., retired employees, military personnel, etc.). [34] investigated the impact an AR program designed for paraprofessionals (n = 638) had on projected special education teacher employment capacities. The AR program titled the South Carolina Initiative, aimed to recruit paraprofessionals through Grow Your Own programs. Their results showed that there were significant disproportionalities in estimated teacher job employment by special education licensure area, χ<sups>2</sups>(4, _I_N =_i_ 638) = 19.20, p = .001, meaning that the observed frequency of 17 program completers in emotional disabilities was only half as many as the expected frequency of 34. In addition, the observed frequency of 14 program completers in multi-categorical special education was two thirds more than the expected frequency of 8.4. Therefore, while all four studies categorized ARs successfully filling special education teacher vacancies with interested paraprofessionals, disparities exist within varying special education teacher roles.

Knowledge Signals

Education employers lack information about each teacher candidate's actual productivity during the hiring process ([20]). Therefore, to maximize education production for their school, employers look for estimated signals of teaching productivity. These observed signals on a teacher candidate's application are the type of education (e.g., attended an elite university, obtained a master's degree) and/or the observed quality of that education act (e.g., SAT scores, teacher certification exams, etc.). In human capital theory, these observed characteristics function as a proxy for the unobserved traits of productivity for teaching content ([20]). As a result, teachers' observed knowledge has been largely studied in preparation program research within the general education teacher population who teach science, technology, engineering, and math (e.g., [17]; [12]; [30]). Researchers posit that AR programs recruit teachers with stronger observed academic knowledge traits than traditional preparation programs. For example, in [30] study, AR programs in Florida recruited teachers who had significantly higher academic scores. This was exemplified by AR teachers coming from more prestigious universities, having higher SAT scores, and having stronger scores on their teacher entrance exams across math, reading, and writing. These initial findings suggest that ARs recruit teachers with strong observed knowledge signals. Yet, there is limited research on this human capital phenomenon for special education teachers in ARs. Therefore, it is unclear how such findings would apply within the special education teacher workforce.

Only one study in the search ([18]) examined knowledge differences found in special education teachers by preparation path (n = 34). Findings showed that AR special education teachers had significantly higher grade point averages (GPAs) in their undergraduate degrees than others. AR special education teachers reported to have an average GPA of 3.39 compared to an average GPA of 3.24 for non-AR teachers. Yet, it is unclear if this difference in GPA is the norm or unique to the [18] sample. Additionally, it is unknown if GPA serves as an appropriate knowledge proxy for the special education teacher population due to their multifaceted role. For example, the GPA of an AR high school science teacher who earned their Bachelor of Science is strongly correlated to their content knowledge for the subject they teach ([30]). However, special education teachers may teach a variety of content areas and service students' behavioral goals, so there may not be an undergraduate degree equivalent outside special education. Without causal evidence, inferences cannot yet be made regarding knowledge differences in special education teachers and preparation paths.

Program Design

AR program design consists of the structure, characteristics, and guidelines that aim to assist AR special education teachers in nontraditional preparation settings ([38]). The two AR program design subthemes of collaboration between partners in education agencies (n = 7 studies) and technology (n = 6 studies) were ubiquitous throughout empirical special education studies. The research emphasized the vital importance of these factors for successful special education AR program design in multiple geographic settings (e.g., rural and urban, different states). The following sections highlight the findings and implications found regarding the roles of collaboration and technology in AR program design.

Collaboration Between Partners

Partners of different education agencies play an active role in developing AR programs. The reported agencies consisted of IHEs, LEAs, and SEAs. While previous studies used the term "stakeholders," we opted to use the term "partners" to represent interested parties in ARs (see https://www.cdc.gov/healthcommunication/Preferred%5fTerms.html). For example, in the [27] study, AR program directors (n = 101) identified IHEs as the primary agency responsible for program design (75.8%). Additionally, the researchers identified SEAs also sharing responsibility for AR program design (71.7%) as well as LEAs contributing to it (48.5%). The majority of AR program directors responded that these multiple agencies collaborated in building an AR structure and refuted the notion that one agency was solely responsible. The other studies (e.g., [2]; [16]; [18]; [26]; [31]; [33]; [34]) cited the significance of collaboration in program design with particular focus on LEAs involvement with IHEs. This consisted of LEAs creating mentorships and training structure while IHEs provided evidence-based standards, observation protocol, and online curriculum. A prime example of partner collaboration occurred in [34] analysis of the South Carolina Initiative. Created to curb the special education teacher shortage, the South Carolina Initiative included the SEA (e.g., South Carolina Department of Education) to cover tuition and textbook costs, the LEAs to recruit and mentor AR teachers, and the IHEs to deliver the licensure coursework.

Technology

Researchers also captured the importance of technology in preparing special education AR teachers. Technology in AR programs was specific to online distance coursework and integrated technology to provide feedback to teachers.

Online Distance Programs

Six studies examined AR programs that were administered in online formats to provide teacher preparation coursework (e.g., [2]; [15]; [16]; [31]; [33]; [34]). These online distance programs were often administered to practicing but uncertified special education teachers who were working on obtaining certification. Online distance programs were reported to be cost-effective for both the education agencies and special education teachers as they permitted more AR teachers to be enrolled in a cohort at a cheaper price. For example, [33] demonstrated the relative efficiency of online distance learning over face-to-face instruction in their analysis of cost-effectiveness with AR program directors. Average student enrollment for online distance learning (M = 41.6, SD = 30.3) was higher compared to face-to-face programs (M = 30.8, SD = 22.9). In addition, costs were cheaper per student in online distance (M = $10,537) versus face-to-face programs (M = $14,522).

Integrated Feedback Technology

Studies also investigated the role of additional integrated technology embedded in online AR programs. For instance, [2] specifically explored the impact of web cameras that were utilized for classroom observations of AR special education teachers. Special education teachers (n = 3) and university observers (n = 2) reported no difference in preference over virtual web camera observations versus face-to-face observations. Integrated web cameras were cited to minimize interruptions for students, cost-effective for university observers, and efficient to provide frequent teacher feedback. With the proliferation of technology integrated into teacher education, these studies provided an exploratory foundation for future research with AR program design.

Discussion

The purpose of our systematic literature review was to explore empirical research from 2005 to 2021 that was conducted on special education teachers in AR programs. This review analyzed how AR programs impact special education teacher labor markets with a focused examination of teacher quantity and quality. Eleven peer-reviewed publications were included that focused on special education teachers and AR programs. In the following sections, the two factors most strongly identified with special education AR program research are clarified, methodological strengths and weaknesses are explained, and implications for policy are discussed.

Strongest Factors in AR Research: Quantity Versus Quality

The majority of researchers drew conclusions about how AR programs contribute to the special education teacher supply (n = 9 studies). Specifically, researchers concluded that AR programs increased the local supply of special education teachers due to their cost-effectiveness, diversity recruitment efforts, and digital program design. Studies from this review varied in terms of participants and program characteristics, which is consistent with previous research in this area ([7]; [27];). In fact, within this review, we found that ARs service a wide range of special education participants ranging from (a) provisionally licensed teachers of record who serviced students with disabilities to (b) preservice teachers enrolled in Master programs who were not current teachers of record. Furthermore, AR programs displayed a wide range of characteristics as they varied in their course-hour requirements, course delivery, testing requirements, fieldwork, and mentorship. Therefore, we posit that it is difficult to draw conclusions about the effectiveness of AR programs given the broad application of one term, alternative, to mean many things. There may be specific attributes that are essential to AR program success, but it is not clear which aspects of the program characteristics have the greatest impact. Logistical aspects of these programs including affordability and flexibility are reported to contribute to the popularity of AR programs and thus to the special education teacher supply, but much more is not clear. Furthermore, quantity is not a proxy for quality. The notion of teacher quality is complex and typically beyond the scope of AR program descriptions and evaluations.

Only two studies in this review broached the topic of teacher quality in relation to AR programs. The two relevant studies were largely exploratory in nature that consisted of special education teachers' perceptions of ARs (e.g., [2]; Casey et al., 2011). Regarding teacher quality, these studies concluded that AR special education teachers may benefit from ongoing feedback and additional support to navigate noninstructional duties (e.g., paperwork, collaboration with colleagues, communication with families, etc.), but causal evidence was not found. This emphasizes the notable gap that remains when discussing the form and function of AR programs. Researchers from the studies within this review did not investigate the degree to which AR program completion resulted in high-quality special education teachers. This may be due to the disconnect between the aim of AR programs and the push in the field for elevating rigor within the profession. If the goal of AR programs is to increase the number of teachers in the supply chain, such programs may not be intended to be assessed for quality of program completer in relation to quantifiable factors such as content expertise, student achievement, or teacher retention. If quantity is not a proxy for quality, retention is likely not either. Yet, the question remains around retention rates for AR program completers who do indeed take positions as special education teachers. Other aspects of teacher quality such as student achievement are explored in AR program research for general education teacher populations (e.g., [1]; [5]; [6]; [12]; etc.), but the same is not true for special education teachers in the AR programs investigated within this review. Assessing the degree to which AR programs address both quantity and quality of the special education teacher workforce, without clarification of the intended goals of AR programs beyond addressing supply chain issues, may be unobtainable. To fully understand the impact of ARs, additional research is needed that captures the impact of preparation programs on student achievement. This call for additional research is especially pertinent within the field of special education, as student outcomes are multifaceted consisting of academic and/or behavioral progress.

Methodological Strengths and Weaknesses

The strengths of the included studies were that researchers explored a wide range of studies and postulated different types of questions. This included examining different variables among the participants in ARs including their gender (e.g., [31]), age ([34]), race ([18]; [27]; [31]; [34]), career path ([18]; [33]; [34]), and those special education teachers currently in the field (Casey et al., 2011; [26]; [31]; [33]). Additionally, small-scale studies utilized purposeful samples to examine perceptions and experiences within AR programs (e.g., [2]; Casey et al., 2011; [18]; [31]). Whereas others used larger samples to capture trends of AR program design (e.g., [15]; [16]; [26]; [27]; [33]; [34]). Additional research can build off such strengths by examining the causal effects of ARs on special education teacher labor markets.

Therefore, we recommend that experimental and quasi-experimental experiments be employed to evaluate the special education teacher labor market and ARs. These studies were largely exploratory, and future models may include robust methodologies to exert any causal findings of ARs on special education teacher quality and quantity. To examine teacher quality, it is suggested that rich state panel data be utilized to employ a value-added analysis of preparation pathways on student outcomes. Additionally, policies vary by state, so we also recommended that research is conducted on the national trends of ARs and the special education supply. By utilizing rich panel data within a specific state, AR program characteristics and special education teacher supply can be reported. This would help examine the efficacy of the varying AR policies within the United States.

Finally, there appeared to be an emerging trend nested within the studies' theoretical frameworks in this systematic literature review. With the proliferation of AR state policies in the 1990s, research that was conducted in the early 2000s utilized economic frameworks (e.g., [27]). As a result, analyses were performed on teacher supply and demand variables, cost-effectiveness, and program infrastructure. Whereas newer research conducted from 2016 to 2021, focused on theoretical frameworks grounded in cultural and linguistic diversity (e.g., [31]). Their aim was to evaluate the impact ARs had on recruiting, preparing, and retaining a culturally diverse special education workforce. We recommend that future research synchronizes the two empirical aims to capture a more holistic analysis of intended policy partners.

Implications for Policy

Federal and state policymakers share responsibility in finding as many viable teacher preparation paths that lead to special education licensure. Supporting AR programs that meet and exceed the minimum requirements for adequate preparation of special education teachers is one option. Therefore, implications from this review are critical for all partners to attract, prepare, and retain a skilled special education teacher workforce ([36]). The following are recommendations intended for federal and state policymakers to address this urgent need.

First, federal policymakers should provide greater funding to AR program infrastructure. This includes increased federal funding for AR cohorts to recruit more qualified and culturally and/or linguistically diverse special education teachers. Findings from this review supported the notion that increased federally funded AR cohorts successfully increased novice teacher supply and later teacher retention. Additionally, federal policymakers should fund robust research to analyze ARs' effectiveness in alleviating the national special education teacher shortage. This is especially critical in addressing the OSEP's initiative of increasing effective personnel for all students with disabilities in the United States ([36]). Funding distributed to teacher preparation researchers in universities may increase empirically based findings that drive future licensure policies.

Second, we recommend that state policymakers adapt high-quality AR preparation policies if they have not already. While most states allow for some form of AR program, further investigation would improve state licensure policies to increase the special education teacher supply. We recommend that state policymakers collaborate with local education agencies and institutions of higher education to support AR implementation. From the [27] examination, only 33% of AR program directors reported collaboration from state education agencies. AR programs must be better supported with increased funding and program design input from state education agencies. As stated in the findings from this review, collaboration between SEAs, IHEs, and LEAs are critical in sustaining AR infrastructure.

Conclusion

Special education teachers who participate in ARs are one pipeline source to various special education teacher labor markets. As researchers, we can help education partners make informed decisions about the impact AR programs have on producing a skilled special education teacher workforce. Therefore, while the field has made progress evaluating AR design in special education, it is critical that (a) robust research is conducted on analyzing national trends for ARs and the special education teacher labor market, (b) economic and culturally/linguistically diverse frameworks drive future inquiry for a holistic analysis, and (c) findings are disseminated to all partners: policymakers, IHEs, and LEAs. Additional AR research is valuable to fully evaluate recruitment policy solutions aimed to decrease the special education teacher shortage. This is an urgent matter, as teacher shortages endanger special education services to students with disabilities.

By Jamie Day and Sarah A. Nagro

Reported by Author; Author

jamie day , PhD, is an assistant professor of special education at The University of Missouri. Her research revolves around strategies to attract, prepare, and retain effective personnel for all multilingual students with disabilities.

sarah a. nagro , EdD, is an associate professor of special education at George Mason University where her research focuses on preparing profession-ready teachers through meaningful field-based experiences that emphasize reflection, video analysis, self-evaluation, and professional buy-in. Sarah is interested in understanding how to help teacher candidates and novice teachers find success when educating students with disabilities with the goal of retaining high quality professionals.