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Foreign Language Education in Louisiana: A Cluster Analysis

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Title: Foreign Language Education in Louisiana: A Cluster Analysis
Language: English
Authors: Erin Fell (ORCID 0000-0001-5667-8935)
Source: Foreign Language Annals. 2024 57(3):698-724.
Availability: Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us
Peer Reviewed: Y
Page Count: 27
Publication Date: 2024
Document Type: Journal Articles
Reports - Research
Education Level: High Schools
Secondary Education
Elementary Secondary Education
Descriptors: Second Language Learning, Second Language Instruction, High School Students, State Policy, Educational Policy, Required Courses, Educational Change, Minority Group Students, Equal Education, Access to Education, Federal Aid, Rural Areas, Educational Finance, Disadvantaged Schools, Language Enrollment, Educational Legislation, Federal Legislation, Elementary Secondary Education
Geographic Terms: Louisiana
Laws, Policies and Program Identifiers: Elementary and Secondary Education Act Title I
DOI: 10.1111/flan.12737
ISSN: 0015-718X
1944-9720
Abstract: Louisiana is currently the only state in the United States that requires foreign language (FL) study for some, but not all of their high school students, and these requirements are undergoing seismic changes. Considering that "geographic," "economic," and "integration" factors contribute to whether a school can provide FL at all--and biased counseling dissuades minoritized students from taking FL--this study asks: "has FL education been equitably accessible to all Louisiana high school students who want to pursue it, regardless of race or economic background?" To address this question, this paper presents results from two analyses. First, program-internal equity variables (e.g., [dis]similarity between school-wide and FL student demographics) were clustered to produce "profiles" of FL programs. Next, a multinomial logistic regression using program-external factors (e.g., federal funding status, desegregation orders) was conducted to isolate the factors impacting equity. Federal funding and rurality were both found to be significant, with schools receiving federal Title I funds and rural schools being much more likely to exhibit inequitable access to FL courses. In (a) identifying schools with enrollment inequities and (b) the outside factors associated with greater inequity, this paper aims to provide policymakers with empirically based tools to address (in)equity in Louisiana high school FL education. These findings can be especially helpful in light of the state's recent (2023-2024 school year) expansion of FL requirements to accept computer science courses.
Abstractor: As Provided
Entry Date: 2024
Accession Number: EJ1448002
Database: ERIC
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  Value: <anid>AN0180802689;fla01sep.24;2024Nov13.05:09;v2.2.500</anid> <title id="AN0180802689-1">Foreign language education in Louisiana: A cluster analysis </title> <p>Louisiana is currently the only state in the United States that requires foreign language (FL) study for some, but not all of their high school students, and these requirements are undergoing seismic changes. Considering that geographic, economic, and integration factors contribute to whether a school can provide FL at all—and biased counseling dissuades minoritized students from taking FL—this study asks: has FL education been equitably accessible to all Louisiana high school students who want to pursue it, regardless of race or economic background? To address this question, this paper presents results from two analyses. First, program‐internal equity variables (e.g., [dis]similarity between school‐wide and FL student demographics) were clustered to produce "profiles" of FL programs. Next, a multinomial logistic regression using program‐external factors (e.g., federal funding status, desegregation orders) was conducted to isolate the factors impacting equity. Federal funding and rurality were both found to be significant, with schools receiving federal Title I funds and rural schools being much more likely to exhibit inequitable access to FL courses. In (a) identifying schools with enrollment inequities and (b) the outside factors associated with greater inequity, this paper aims to provide policymakers with empirically based tools to address (in)equity in Louisiana high school FL education. These findings can be especially helpful in light of the state's recent (2023‐2024 school year) expansion of FL requirements to accept computer science courses.</p> <p>The Challenge: This study examines how geography, economic (dis)advantage, and racial integration impact enrollment in foreign language (FL) coursework in Louisiana—the only state to require high school FL for eligibility to 4‐year colleges. Louisiana high schools cluster into five "profiles" policymakers can use to increase access to FL and higher education.</p> <p>Keywords: advocacy; enrollment; language policy; secondary education</p> <hd id="AN0180802689-2">INTRODUCTION</hd> <p>Education is "primarily a State and local responsibility in the United States [and i]t is States and communities, as well as public and private organizations of all kinds, that establish schools and colleges, develop curricula, and determine requirements for enrollment and graduation" (US Department of Education [USDOE], 2020, para 1), resulting in a wide range of practices, standards, and priorities between states. Motivated in part by its multilingual history (Council on the Development of French in Louisiana [CODOFIL], [<reflink idref="bib9" id="ref1">9</reflink>], [<reflink idref="bib10" id="ref2">10</reflink>]; Louisiana Believes, [<reflink idref="bib32" id="ref3">32</reflink>]), the Louisiana Department of Education (LDOE) established in the 1980s that foreign language (FL) study would be a core value of education in Louisiana by implementing a FL mandate for elementary school students in fourth through eighth grades (see State Superintendent of Education, [<reflink idref="bib58" id="ref4">58</reflink>] for details on the 1984 elementary school mandate). The LDOE's robust support of FL study in early grades was later extended to the high school level in 2008, with the department citing the ability to speak an additional language as having a significant positive impact on student cognition, academic performance (including on standardized tests; e.g., ACT), intercultural competence, and eligibility for high‐paying jobs (Louisiana Believes, [<reflink idref="bib32" id="ref5">32</reflink>]).</p> <hd id="AN0180802689-3">Tracking and FL study</hd> <p>Despite this position advocating for universal FL study, not all Louisiana students take an FL in high school (Louisiana Believes, [<reflink idref="bib28" id="ref6">28</reflink>]). Instead, as research in other states (e.g., Massachusetts in Ritz & Sherf, [<reflink idref="bib52" id="ref7">52</reflink>]; North Carolina in Baggett, [<reflink idref="bib4" id="ref8">4</reflink>]; Texas in Schoener & McKenzie, [<reflink idref="bib54" id="ref9">54</reflink>]) and nationwide research (e.g., Anya, [<reflink idref="bib1" id="ref10">1</reflink>]; Finn, [<reflink idref="bib16" id="ref11">16</reflink>]) has demonstrated, "language programs tend to be designed to weed out the academically weak students and act as a tracking mechanism to ensure that only the best and brightest are left in the class," rather than being offered to all interested students (Glynn & Wassell, [<reflink idref="bib18" id="ref12">18</reflink>], p. 22). Compounding this use of FL study as a tool for separation by (perceived) academic ability (Finn, [<reflink idref="bib16" id="ref13">16</reflink>]; Reagan & Osborn, [<reflink idref="bib48" id="ref14">48</reflink>]; Schoener & McKenzie, [<reflink idref="bib54" id="ref15">54</reflink>]) is research that demonstrates that students from racially minoritized backgrounds (e.g., Black, Latine, Indigenous, multiracial; cf., white) are frequently <emph>counseled out</emph> of taking an FL (Schoener & McKenzie, [<reflink idref="bib54" id="ref16">54</reflink>]) by "institutional gatekeepers (e.g., teachers, counselors, administrators) with deficit notions of their supposed linguistic and cultural disadvantages and their families' purported lack of value for education" (Anya, [<reflink idref="bib1" id="ref17">1</reflink>], p. 98). In other words, research has shown that the adults in charge of enrolling high school students in FL courses frequently perceive minoritized students as being somehow incapable of Anya ([<reflink idref="bib1" id="ref18">1</reflink>]), unready for Reagan and Osborn ([<reflink idref="bib48" id="ref19">48</reflink>]), or simply uninterested in Moore ([<reflink idref="bib41" id="ref20">41</reflink>]) FL study because of their racial background, resulting in fewer minoritized students enrolling in FL in high school (Baggett, [<reflink idref="bib4" id="ref21">4</reflink>]; Finn, [<reflink idref="bib16" id="ref22">16</reflink>]; Ritz & Sherf, [<reflink idref="bib52" id="ref23">52</reflink>]; Schoener & McKenzie, [<reflink idref="bib54" id="ref24">54</reflink>]).</p> <p>Wielding FL as a tool for academic tracking (Finn, [<reflink idref="bib16" id="ref25">16</reflink>]; Glynn & Wassell, [<reflink idref="bib18" id="ref26">18</reflink>]; Reagan & Osborn, [<reflink idref="bib48" id="ref27">48</reflink>]) or counseling students from certain racial backgrounds out of FL (Anya, [<reflink idref="bib1" id="ref28">1</reflink>]; Schoener & McKenzie, [<reflink idref="bib54" id="ref29">54</reflink>]) is always problematic, but for Louisiana students—particularly those from minoritized backgrounds—(not) taking a FL in high school can have <emph>lifelong</emph> consequences: it can potentially be a factor in whether a student is <emph>eligible</emph> to enroll in a 4‐year college or university after graduating from high school.</p> <hd id="AN0180802689-4">Tracking, diploma types, and postgraduation differences in opportunity</hd> <p>This positioning of FL study as a potential future‐defining course is due to differences in the requirements of Louisiana's two high school diploma types: the <emph>TOPS University Diploma</emph> and the <emph>Jump Start: Career Diploma</emph> (Board of Elementary and Secondary Education [BESE], [<reflink idref="bib6" id="ref30">6</reflink>]). If a student wants to enroll in a 4‐year college or university upon graduating from high school, they must earn a <emph>TOPS University Diploma</emph> (Louisiana Believes, [<reflink idref="bib34" id="ref31">34</reflink>]). Holders of a <emph>Jump Start: Career Diploma</emph>, by contrast, are only able to either enter directly into the workforce or complete an associate's degree (2‐year degree) at a community college or technical school (Louisiana Believes, [<reflink idref="bib33" id="ref32">33</reflink>]), after which they would then be eligible to enter a 4‐year degree program (BESE, [<reflink idref="bib6" id="ref33">6</reflink>]). As Table 1 shows, FL coursework is only required for high school students working toward a <emph>TOPS University Diploma</emph> (Louisiana Believes, [<reflink idref="bib33" id="ref34">33</reflink>], [<reflink idref="bib34" id="ref35">34</reflink>]). While students working toward a <emph>Jump Start: Career Diploma</emph> are welcome to take FL courses (which would count toward their Elective credits; see East Baton Rouge Parish School Board [EBRPSB], 2015), they are not <emph>required</emph> to take those classes. Therefore, should course capacity be restricted for any reason, <emph>Jump Start</emph> students could be at risk of being denied enrollment in FL courses in favor of <emph>TOPS</emph> students, for whom the FL course is required.</p> <p>1 Table Louisiana high school graduation requirements (Adapted from East Baton Rouge Parish School Board [EBRPSB], 2015b).</p> <p> <ephtml> <table><thead valign="bottom"><tr valign="bottom"><th /><th>TOPS University Diploma</th><th>Jump Start: Career Diploma</th></tr><tr valign="bottom"><th /><th>4‐year university degree</th><th>2‐year com. college/tech. degree</th></tr></thead><tbody valign="top"><tr><td>English</td><td>4 credits<ext-link href="a" /></td><td>4 credits</td></tr><tr><td>Math</td><td>4 credits</td><td>4 credits</td></tr><tr><td>Science</td><td>4 credits</td><td>2 credits</td></tr><tr><td>Social studies</td><td>4 credits</td><td>2 credits</td></tr><tr><td>Health</td><td>½ credit</td><td>½ credit</td></tr><tr><td>Physical education</td><td>1 ½ credits</td><td>1 ½ credits</td></tr><tr><td>Foreign language<sup>b</sup></td><td>2 credits</td><td>—</td></tr><tr><td>Arts</td><td>1 credit</td><td>—</td></tr><tr><td>Electives</td><td>3 credits</td><td>9 credits</td></tr><tr><td>Total</td><td>24 credits</td><td>23 credits</td></tr></tbody></table> </ephtml> </p> <p>1 a The term "credit" indicates one course (one credit = one course; EBRPSB, [<reflink idref="bib13" id="ref36">13</reflink>]).</p> <p>2 b As of the 2023‐2024 school year, Computer Science can be used to fulfill the FL requirement, and "Computer Coding as a Foreign Language" courses are now available and accepted as FL coursework. (Louisiana Believes, [<reflink idref="bib35" id="ref37">35</reflink>]).</p> <p>This practice of requiring FL coursework of only university‐bound Louisiana high school students (Louisiana Believes, [<reflink idref="bib33" id="ref38">33</reflink>], [<reflink idref="bib34" id="ref39">34</reflink>]) seems incongruous with the state's public commitment to FL education (Louisiana Believes, [<reflink idref="bib32" id="ref40">32</reflink>]; State Superintendent of Education, [<reflink idref="bib58" id="ref41">58</reflink>]). Adding another dimension to the issue is the state's recent decision (Louisiana Believes, [<reflink idref="bib35" id="ref42">35</reflink>]) to expand FL requirements to allow incoming freshmen (9th grade) starting in the 2023‐2024 year to use computer science courses to satisfy the TOPS University Diploma's FL requirement. Because data on enrollments in computer science courses are not yet publicly available as of the writing of this piece, it is unclear how the new policy will impact FL enrollment more broadly. The author hopes that providing a snapshot of FL program equity prior to the adoption of the requirement changes will enable policymakers, researchers, and other stakeholders to evaluate the impact of the changes on enrollment patterns and equity more broadly.</p> <p>Though Louisiana is not the only state to track high school students into a range of diploma types (Macdonald et al., [<reflink idref="bib37" id="ref43">37</reflink>])—Arkansas, Indiana, Mississippi, North Dakota, and Oklahoma also track their high school students into diploma types with varying postgraduation opportunities—Louisiana is the <emph>only</emph> state that has FL study as a defining difference in the requirements between diploma types (Macdonald et al., [<reflink idref="bib37" id="ref44">37</reflink>]; O'Rourke et al., [<reflink idref="bib43" id="ref45">43</reflink>]). As a result, if, for whatever reason, a student is unable to take FL, Louisiana is the only state in the United States where not taking an FL in high school can have potential life‐long consequences.</p> <hd id="AN0180802689-5">The problem</hd> <p>The unique positioning of FL coursework as a course requirement for only university‐bound Louisiana students means that (not) taking high school FL courses can have far‐reaching impacts on a student's post‐high school opportunities. This reality, coupled with prior research demonstrating widespread racial and economic disparities in FL education in other parts of the United States (e.g., Anya, [<reflink idref="bib1" id="ref46">1</reflink>]; Schoener & McKenzie, [<reflink idref="bib54" id="ref47">54</reflink>]), raises the question: <emph>is FL education equitably accessible to all Louisiana high school students who want to pursue it, regardless of race or economic background?</emph> If Louisiana is anything like the rest of the country (Anya & Randolph, [<reflink idref="bib2" id="ref48">2</reflink>]) the answer is probably not.</p> <p>Motivated by a desire to answer this question empirically and supply policymakers with actionable, data‐informed tools to address any inequity in Louisiana FL programs, this study aimed to accomplish two goals. The first was to create "profiles" of FL programs throughout Louisiana that would enable policymakers to easily identify the groups of schools that have relatively higher and lower degrees of inequity in FL enrollment. These program "profiles" are concise descriptions of FL programs generated by statistical amalgamation (i.e., cluster analysis) of program‐internal variables (e.g., the existence of an FL program at all; the degree of [dis]similarity between school‐wide and FL student demographics; Anya & Randolph, [<reflink idref="bib2" id="ref49">2</reflink>]) based on the most up‐to‐date data available at the time of writing of this piece. The second goal was to compare these FL program profiles with relevant program‐external factors—discussed in the next section—to determine the extent to which each external factor impacted program profile assignment. In other words, identifying the forces outside a school that research has already demonstrated impact a school's <emph>overall</emph> degree of racial and economic (in)equity and how those factors might be impacting how equitable its <emph>FL</emph> program is, in particular.</p> <hd id="AN0180802689-6">FACTORS THAT IMPACT EQUITY IN SCHOOLS AND DISTRICTS</hd> <p>The following section explores persistent issues education research has identified as contributing to racial and economic inequality in US schools. While other important equity issues exist—including access to education for students with disabilities (e.g., McLaughlin, [<reflink idref="bib38" id="ref50">38</reflink>]; in FL, Wight, [<reflink idref="bib73" id="ref51">73</reflink>]), limited English proficiency (e.g., Brown, [<reflink idref="bib7" id="ref52">7</reflink>]; in FL, Klingner et al., [<reflink idref="bib26" id="ref53">26</reflink>]), and so on—these two were selected because of the robust education and applied linguistics literature investigating them, as summarized below.</p> <hd id="AN0180802689-7">Racial segregation</hd> <p>In addition to US‐based research showing that private advising sessions frequently result in minoritized high school students being counseled <emph>out</emph> of taking an FL (Anya, [<reflink idref="bib1" id="ref54">1</reflink>]; Schoener & McKenzie, [<reflink idref="bib54" id="ref55">54</reflink>]), structural issues like lingering racial segregation within and among schools might also be differentially impacting FL enrollment. When the Supreme Court ruled in <emph>Brown v. Board of Education</emph> in 1954 that "de jure" (i.e., state‐sanctioned) school segregation was unconstitutional, the decision triggered a wave of federal investigation and oversight that led to court‐mandated desegregation orders (Reardon et al., [<reflink idref="bib49" id="ref56">49</reflink>]). As of 2014 (the most recent available school segregation data; Hannah‐Jones, [<reflink idref="bib19" id="ref57">19</reflink>]; Qiu & Hannah‐Jones, [<reflink idref="bib47" id="ref58">47</reflink>]; US Department of Justice, [<reflink idref="bib71" id="ref59">71</reflink>]), over 1000 school districts have either undergone a <emph>voluntary</emph> (<emph>n</emph> = 333, across 43 states) or <emph>involuntary</emph> (i.e., court‐mandated; <emph>n</emph> = 769 across 36 states) desegregation order.</p> <p>In Louisiana alone, there were still 38 <emph>open</emph> desegregation orders in 2014, meaning that in the 60 years since <emph>Brown v Board of Education</emph>, nearly 40 cases had yet to be resolved such that federal integration oversight would no longer be necessary (Qiu & Hannah‐Jones, [<reflink idref="bib47" id="ref60">47</reflink>]; US Department of Justice, [<reflink idref="bib71" id="ref61">71</reflink>]). Unfortunately, school districts do not generally discuss their desegregation status with the public (Hannah‐Jones, [<reflink idref="bib19" id="ref62">19</reflink>]; Meatto, [<reflink idref="bib40" id="ref63">40</reflink>]), so it is unclear how many of these 38 orders are still open as of the writing of this paper in the absence of additional journalistic (Qiu & Hannah‐Jones, [<reflink idref="bib47" id="ref64">47</reflink>]) or government (US Department of Justice, [<reflink idref="bib71" id="ref65">71</reflink>]) reporting. These long‐standing integration issues have been exacerbated by reversion to neighborhood‐based school assignment (Reardon et al., [<reflink idref="bib49" id="ref66">49</reflink>]) and the advent of break‐away school districts (Renzulli & Evans, [<reflink idref="bib50" id="ref67">50</reflink>]) due to "white flight" (Harris, [<reflink idref="bib20" id="ref68">20</reflink>])—white families relocating to form majority‐white neighborhoods (Renzulli & Evans, [<reflink idref="bib50" id="ref69">50</reflink>]). For example, in the case of Baton Rouge, Louisiana's state capital, all high school students are assigned to a particular "neighborhood school" based on where they live (EBRPSB, [<reflink idref="bib12" id="ref70">12</reflink>]). However, certain "magnet" schools—so‐called because they attracted the best and brightest students to the school from around the district, regardless of where they live—also exist in the district, though magnet schools as a practice have also been criticized in other parts of the country as enabling "higher status families driven by a desire to avoid schools populated by students they consider to be of lower race or class status" (Saporito, [<reflink idref="bib53" id="ref71">53</reflink>], p. 181). Additionally, the majority‐white areas of Zachary and Central have both broken away from the Baton Rouge parish‐level school district to form their own community school districts (Harris, [<reflink idref="bib20" id="ref72">20</reflink>]; Louisiana Believes, [<reflink idref="bib28" id="ref73">28</reflink>]), taking their education funding dollars along with them and leaving the original school district with a higher proportion of low‐income students to serve (Louisiana Believes, [<reflink idref="bib27" id="ref74">27</reflink>]).</p> <hd id="AN0180802689-8">Race‐based income inequality and school funding</hd> <p>Resegregation within school districts and the creation of new districts due to white flight have drastically impacted school funding (Louisiana Budget Project, [<reflink idref="bib36" id="ref75">36</reflink>]; Stand for Children: Louisiana, [<reflink idref="bib57" id="ref76">57</reflink>]) due to pervasive race‐based income inequality (Center on Budget and Policy Priorities & Economic Policy Institute, [<reflink idref="bib8" id="ref77">8</reflink>]; Hertz & Silva, [<reflink idref="bib21" id="ref78">21</reflink>]; McNichol et al., [<reflink idref="bib39" id="ref79">39</reflink>]; Thurow, [<reflink idref="bib60" id="ref80">60</reflink>]; Wolfson, [<reflink idref="bib74" id="ref81">74</reflink>]). Recent efforts by the US federal government to stimulate economic investment in high‐poverty areas—or "qualified opportunity zones (QOZs)," so‐called because they are eligible for "Qualified Opportunity Funding" (IRS, [<reflink idref="bib24" id="ref82">24</reflink>], [<reflink idref="bib23" id="ref83">23</reflink>])—have done little except create detailed maps of where poor individuals reside in the United States (Wendel & Jones, [<reflink idref="bib72" id="ref84">72</reflink>]). An inspection of the US Census Bureau's 2019 median income data ([<reflink idref="bib62" id="ref85">62</reflink>]) reveals a striking degree of income inequality in Louisiana, with white families earning an average of 31.33% more than minoritized families (i.e., families who identify as "American Indian/Alaska Native, Asian, Black, Native Hawaiian/Pacific Islander, Hispanic, or Two or More Races," Louisiana Believes, [<reflink idref="bib27" id="ref86">27</reflink>], p. 4). Considering how racially segregated Louisiana's schools and neighborhoods are (Harris, [<reflink idref="bib20" id="ref87">20</reflink>]; US Department of Justice, [<reflink idref="bib71" id="ref88">71</reflink>]) and how most schools in Louisiana are about 43% funded by local property taxes (Louisiana Believes, [<reflink idref="bib31" id="ref89">31</reflink>]; Stand for Children: Louisiana, [<reflink idref="bib57" id="ref90">57</reflink>]), it is unsurprising that schools across the state end up with different budgets based on what their local communities can contribute to education funding (Louisiana Budget Project, [<reflink idref="bib36" id="ref91">36</reflink>]).</p> <p>To attempt to fill these budget shortfalls, Louisiana schools can also receive federal funding through Title I, a fund established by the <emph>Elementary and Secondary Education Act</emph> (ESEA; US Congress, [<reflink idref="bib66" id="ref92">66</reflink>]) to "provid[e] financial assistance to local educational agencies (LEAs) and schools with high numbers or high percentages of children from low‐income families to help ensure that all children meet challenging state academic standards" (US Department of Education, [<reflink idref="bib67" id="ref93">67</reflink>]). To be eligible for Title I funding, a school must meet a number of criteria, the most important of which is whether a school meets the threshold of 40% of their students coming from low‐income families (US Department of Education, [<reflink idref="bib67" id="ref94">67</reflink>]). The apportionment of Title I funds is not a simple matter of passing a demographic threshold, however, because funds are allocated not to schools directly, but to states (US Department of Education, [<reflink idref="bib68" id="ref95">68</reflink>]). Unfortunately, the LDOE's funding decisions have recently been among the most inequitable in the country (Snyder et al., [<reflink idref="bib55" id="ref96">55</reflink>]). In the 2019 fiscal year alone, the LDOE allocated its funds such that over one‐fifth (<emph>N</emph> = 87; 22.77%) of high schools were allocated fewer Title I funds than they qualified for, with small and rural districts disproportionately likely to be among those who were either unfunded or underfunded (Bajak et al., [<reflink idref="bib5" id="ref97">5</reflink>]; US Department of Education, [<reflink idref="bib68" id="ref98">68</reflink>]). Exacerbating this funding crisis is the issue of teacher retention; rural schools are more likely to struggle to maintain their teaching faculty from year to year (see Tran & Smith, [<reflink idref="bib61" id="ref99">61</reflink>]), which forces districts to either spend substantial amounts of money recruiting, on‐boarding, and mentoring new teachers (Papay et al., [<reflink idref="bib44" id="ref100">44</reflink>]) or simply offer digital, asynchronous and noninteractive equivalents, as Catahoula Parish did when they offered Spanish courses to their 150 high school students (Edgenuity, [<reflink idref="bib14" id="ref101">14</reflink>]).</p> <p>While mostly white, affluent, and suburban schools are usually insulated from persistent (under)funding (e.g., Louisiana Believes, [<reflink idref="bib31" id="ref102">31</reflink>]; Louisiana Budget Project, [<reflink idref="bib36" id="ref103">36</reflink>]; Stand for Children: Louisiana, [<reflink idref="bib57" id="ref104">57</reflink>]), urban, rural, mostly minoritized, and high‐poverty areas do not necessarily have that same luxury and frequently reckon with funding‐related issues like teacher recruitment and retention for so‐called "core" courses like Math and English Language Arts (Aragon, [<reflink idref="bib3" id="ref105">3</reflink>]; Ingersoll, [<reflink idref="bib22" id="ref106">22</reflink>]; Swanson & Mason, [<reflink idref="bib59" id="ref107">59</reflink>]; USDOE, [<reflink idref="bib69" id="ref108">69</reflink>], [<reflink idref="bib70" id="ref109">70</reflink>]), let alone FL (Pufahl & Rhodes, [<reflink idref="bib46" id="ref110">46</reflink>]; Rhodes, [<reflink idref="bib51" id="ref111">51</reflink>]).</p> <p>In sum, the factors discussed in this section have demonstrated a sustained, deleterious impact on education in general across the United States. Racial segregation (Harris, [<reflink idref="bib20" id="ref112">20</reflink>]; Qiu & Hannah‐Jones, [<reflink idref="bib47" id="ref113">47</reflink>]; Renzulli & Evans, [<reflink idref="bib50" id="ref114">50</reflink>]; US Department of Justice, [<reflink idref="bib71" id="ref115">71</reflink>]) and (under)funding (Louisiana Budget Project, [<reflink idref="bib36" id="ref116">36</reflink>]; Stand for Children: Louisiana, [<reflink idref="bib57" id="ref117">57</reflink>]) continue to plague U.S. schools. Because FL programs are not always necessary for high school graduation (Macdonald et al., [<reflink idref="bib37" id="ref118">37</reflink>]; O'Rourke et al., [<reflink idref="bib43" id="ref119">43</reflink>]), districts in low‐income communities offering FL risk being forced to make tough decisions, including simply eliminating their programs altogether (in Massachusetts, Pufahl & Rhodes, [<reflink idref="bib46" id="ref120">46</reflink>]; in a nationwide study of <emph>N</emph> = 5000 schools, Rhodes, [<reflink idref="bib51" id="ref121">51</reflink>]) or limiting access (Anya, [<reflink idref="bib1" id="ref122">1</reflink>]; Finn, [<reflink idref="bib16" id="ref123">16</reflink>]; Reagan & Osborn, [<reflink idref="bib48" id="ref124">48</reflink>]). As a result, these issues have informed the program‐external factors included in the analysis of equity in access to FL education in Louisiana.</p> <hd id="AN0180802689-9">RESEARCH QUESTIONS</hd> <p>Informed by prior research in educational equity in general (see above section), together with a desire to produce actionable tools for policymakers addressing (potential) inequity in Louisiana FL high school programs, this study addresses the following research questions:</p> <p>RQ1: <emph>Do Louisiana FL programs cluster into meaningful program profiles based on the following (program‐internal) factors?</emph></p> <p></p> <ulist> <item> total school site enrollment,</item> <p></p> <item> total site demographics (percent minority students [per the definition in Louisiana Believes, [<reflink idref="bib27" id="ref125">27</reflink>]] and economically disadvantaged students),</item> <p></p> <item> FL enrollment rate, and</item> <p></p> <item> disparity between total site demographics and FL enrollment demographics.</item> </ulist> <p>Because site demographics, FL enrollment, and disparity information were all available only as percentages, not raw counts (to preserve student anonymity, particularly in small schools and programs; Louisiana Believes, [<reflink idref="bib27" id="ref126">27</reflink>]), total site enrollment was added as a factor for the cluster analysis. This ensured that very small programs had additional data that would offset what might seem to be large disparities in enrollment due to low numbers (e.g., a 25% racial disparity in enrollment seems large, but in a four‐person FL program, this represents a single student).</p> <p>RQ2: <emph>If meaningful program profile clusters exist, to what extent do the following program‐external factors impact cluster membership?</emph></p> <p></p> <ulist> <item> income inequality based on race,</item> <p></p> <item> involuntary desegregation orders,</item> <p></p> <item> voluntary desegregation orders,</item> <p></p> <item> school site Title I status,</item> <p></p> <item> special district status (e.g., "break‐away" districts)</item> <p></p> <item> school district rurality, and</item> <p></p> <item> school site QOZs status.</item> </ulist> <p>As explained above, while any number of program‐external factors could have been selected for inclusion in this study, these racial and economic factors were selected because they emanate from well‐established education research on school (de)segregation, funding, and equity.</p> <hd id="AN0180802689-10">METHODOLOGY</hd> <p>The data for this analysis were sourced from publicly available data emanating from government records and legally binding documents to minimize the impact of bias present in other FL program accessibility research that has relied primarily on surveys (e.g., Pufahl & Rhodes, [<reflink idref="bib46" id="ref127">46</reflink>]; Ritz & Sherf, [<reflink idref="bib52" id="ref128">52</reflink>]). All data relating to student demographics, enrollment, and special district status come from data on the 2019–2020 academic year and published on the LDOE website, as mandated by state law (Louisiana Believes, [<reflink idref="bib28" id="ref129">28</reflink>], [<reflink idref="bib30" id="ref130">30</reflink>]). Though, of course, the 2019–2020 academic year was profoundly impacted by the COVID‐19 pandemic, the student enrollment data used in this study were reported by schools in October 2019 and tabulated by the LDOE over the course of several subsequent months and, as a result, represent a snapshot of prepandemic schooling and typical enrollment for the state. Data on median income and school rurality come from the U.S. Census Bureau from the corresponding time frame, as this entity is tasked with monitoring U.S. demographics and population changes (US Census Bureau [USCB], [<reflink idref="bib64" id="ref131">64</reflink>], [<reflink idref="bib65" id="ref132">65</reflink>], [<reflink idref="bib62" id="ref133">62</reflink>]). Finally, desegregation orders (Qiu & Hannah‐Jones, [<reflink idref="bib47" id="ref134">47</reflink>]), qualified opportunity zone status (IRS, [<reflink idref="bib24" id="ref135">24</reflink>]), and Title I funding status (National Center for Education Statistics [NCES], [<reflink idref="bib42" id="ref136">42</reflink>]) came from the US Department of Justice, Internal Revenue Service, and National Center for Education Statistics, respectively, again for the 2019–2020 academic year.</p> <p>To address the first research question, cluster analysis was selected as the statistical approach. Cluster analysis has become increasingly popular in second language acquisition (SLA) and applied linguistics research in recent years (see Crowther et al., [<reflink idref="bib11" id="ref137">11</reflink>], for a synthesis) because it offers a way of grounding groupings of cases based on observed variation rather than predetermined categorical boundaries selected by a researcher (e.g., age, language[s] spoken, proficiency). Additionally, cluster analysis can be used to reduce variables (e.g., in motivation and individual differences research; Papi & Teimouri, [<reflink idref="bib45" id="ref138">45</reflink>]; Sparks et al., [<reflink idref="bib56" id="ref139">56</reflink>]). However, factor analysis (FA) is generally preferred in variable reduction, as the results of FA are scalable, while cluster analyses are not (Crowther et al., [<reflink idref="bib11" id="ref140">11</reflink>]). Cluster analysis is an appropriate tool for this study because it deals with observed cases (i.e., schools with FL programs) and how they can be distilled into manageable program "profiles" to be used by policymakers.</p> <p>Agglomerative clustering analysis was chosen to create program profiles based in bottom‐up (cf. divisive clustering) clustering that did not rely on the researcher to select the number of clusters a priori (cf. partitioning clustering, e.g., k‐means clustering). Agglomerative clustering (also called "AGNES" for "Agglomerative Nesting") is a type of hierarchical clustering that is frequently used in genetics research and is especially useful for this data set because it contains outliers (i.e., evidence of inequities in the system), which can pose a problem for other analyses like k‐means clustering (Kassambara, [<reflink idref="bib25" id="ref141">25</reflink>]).</p> <p>Multinomial logistic regression analysis was identified as the appropriate tool to address the second research question because it enables identification of the impact of a number of independent variables that are both <emph>continuous</emph> (income inequality based on race) and <emph>categorical</emph> in nature ([in]voluntary desegregation orders, school site Title I status, special district status, school district rurality, and school site QOZs status) on a single categorical dependent variable (i.e., cluster profile membership).</p> <hd id="AN0180802689-11">ANALYSIS AND RESULTS</hd> <p></p> <hd id="AN0180802689-12">RQ1: Do Louisiana FL programs cluster into meaningful program profiles?</hd> <p>To address the first research question, each of the aforementioned program‐internal factors was selected for each school site (Louisiana Believes, [<reflink idref="bib27" id="ref142">27</reflink>]) and the data were input into RStudio, with each row representing a single school site and each column representing one of the factors.</p> <p>Before running the agglomerative clustering analysis, the data were first subjected to a number of tests to determine its clustering tendency (i.e., whether any meaningful clusters exist and, if they do, how many there are). A random subset of the data was selected, and the scatterplots, dendrograms, and cluster plots were visually inspected for clusters; while the random subset resulted in no clusters, the complete data set did result in clusters. Next, to statistically assess the data's clustering tendency, the Hopkin's statistic was computed for both the random subset and the complete set, resulting in <emph>H</emph> values of 0.51 and 0.19, respectively. Because the random subset exceeded the 0.50 Hopkin's statistic threshold while the complete data set did not, it can be concluded that the complete data set does indeed contain meaningful clusters. Finally, an ordered dissimilarity image (ODI) was generated for both the random subset and the complete data set, which confirmed the complete data set's clustering tendency and suitability for cluster analysis.</p> <p>With the clustering tendency verified, the data were next submitted to tests used to determine the number of clusters in the data. The elbow, silhouette, and gap statistic methods resulted in recommendations of four, two, and five clusters, respectively. To resolve the inconsistency between the recommendations, the data were next analyzed with the R package <emph>NbClust()</emph>, a package that assesses the data using 30 indices of clustering tendency and produces a recommended number of clusters based on "majority rule" (wording from the console text generated as a result of the <emph>NbClust()</emph> analysis) (Table 2).</p> <p>2 Table Recommended number of clusters based on NbClust () analysis.</p> <p> <ephtml> <table><thead valign="bottom"><tr valign="bottom"><th align="left"><italic>N</italic> indices</th><th align="left">Proposed best number of clusters</th></tr></thead><tbody valign="top"><tr><td>6</td><td>2</td></tr><tr><td>5</td><td>3</td></tr><tr><td>6</td><td>5</td></tr><tr><td>2</td><td>7</td></tr><tr><td>1</td><td>9</td></tr><tr><td>3</td><td>10</td></tr></tbody></table> </ephtml> </p> <p>Because the <emph>NbCluster()</emph> analysis resulted in a tie between two and five clusters, the researcher had to make the final decision between the number of clusters. Ultimately, because the goal is to provide nuanced profiles to Louisiana policymakers, it was decided that two clusters would result in too few usable distinctions between FL programs. As a result, five was chosen as the cut point for the dendrogram analysis.</p> <p>Next, we turn to the actual agglomerative cluster analysis. Using the average linkage method and the <emph>hclust()</emph> function resulted in a 0.74 correlation coefficient, meaning that there was a strong correlation between cophenetic distances (generated by the average linkage method) and the original distances (<emph>r</emph>‐values above 0.75 are generally preferred; Kassambara, [<reflink idref="bib25" id="ref143">25</reflink>]). The dendrogram resulting from the agglomerative clustering can be seen in Figure 1 below.</p> <p> <img src="https://imageserver.ebscohost.com/img/embimages/rdk/FLA/01sep24/flan12737-fig-0001.jpg?ephost1=dGJyMNXb4kSepq84yOvqOLCmsE6epq5Srqa4SK6WxWXS" alt="flan12737-fig-0001.jpg" title="1 Dendrogram of the agglomerative clustering analysis." /> </p> <p></p> <p>Figure 2 presents the geographic distribution of all the schools in the analysis. An interactive digital version of this map is available on <emph>Google My Maps</emph> (Fell, [<reflink idref="bib15" id="ref144">15</reflink>]). Table 3 provides additional geographic context for each cluster's dispersion for those unfamiliar with Louisiana's regions and population centers.</p> <p> <img src="https://imageserver.ebscohost.com/img/embimages/rdk/FLA/01sep24/flan12737-fig-0002.jpg?ephost1=dGJyMNXb4kSepq84yOvqOLCmsE6epq5Srqa4SK6WxWXS" alt="flan12737-fig-0002.jpg" title="2 Geographic dispersion of the school clusters." /> </p> <p></p> <p>3 Table Contextualizing the clusters' dispersions.</p> <p> <ephtml> <table><thead valign="bottom"><tr valign="bottom"><th align="left">Cluster dispersion map</th><th>Context</th></tr></thead><tbody valign="top"><tr><td><graphic href="" />Cluster 1</td><td>Schools in Cluster 1 are primarily found in rural, sparsely populated portions of the state where there are few public high schools. For example, the rural community of Singer has one high school, Singer High School.</td></tr><tr><td><graphic href="" />Cluster 2</td><td>Schools in Cluster 2 are found in both rural areas as well as larger population centers.</td></tr><tr><td><graphic href="" />Cluster 3</td><td>Many of the schools in Cluster 3 are alternative schools located in rural areas or on the outskirts of larger population centers. Several schools, such as Dixon Correctional Institute, are located in or near prisons and other correctional facilities.</td></tr><tr><td><graphic href="" />Cluster 4</td><td>Schools in Cluster 4 are primarily located in large, urbanized areas (e.g., Baton Rouge, New Orleans, Shreveport), except for a string of rural schools in south‐central Louisiana.</td></tr><tr><td><graphic href="" />Cluster 5</td><td>All schools in Cluster 5 are in urbanized areas (e.g., Baton Rouge, New Orleans, Shreveport) or in "break‐away" school districts in suburban areas immediately outside of larger cities (e.g., Zachary High School outside of Baton Rouge).</td></tr></tbody></table> </ephtml> </p> <p>As can be seen in Table 4 below, the largest clusters are Clusters 1 and 4, with Clusters 2 and 3 representing mid‐sized clusters and Cluster 5 being the smallest cluster. The wide SDs of each cluster's total student count indicate considerable variation among each cluster's total enrollments. In terms of demographics of the students enrolled at the schools in each cluster, Clusters 3 and 4 had high percentages of minority and economically disadvantaged students. Clusters 2 and 5 had similarly mid‐range percentages of minorities, and Cluster 1 had a relatively low percentage of minority students. With regard to economic disadvantage, Clusters 3 and 4 had very high percentages of students who are economically disadvantaged, while Clusters 1 and 2 had relatively high percentages of students who are economically disadvantaged. Cluster 5 had the lowest percentage of students who are economically disadvantaged, though it is still much higher than the state‐wide percentage of residents in poverty (19%; USCB, [<reflink idref="bib62" id="ref145">62</reflink>]).</p> <p>4 Table Descriptive statistics of the five foreign languages (FLs) program profiles generated by agglomerative clustering.</p> <p> <ephtml> <table><thead valign="bottom"><tr valign="bottom"><th align="left">Cluster</th><th align="left"><italic>N</italic> high schools</th><th /><th align="left"><italic>N</italic> Total students (<italic>SD</italic>)</th><th align="left">% Minority at site (<italic>SD</italic>)</th><th align="left">% Economically disadvantaged at site (<italic>SD</italic>)</th><th align="left">% Students enrolled in FL (<italic>SD</italic>)</th><th align="left">% Disparity in minority enrollment in FL (<italic>SD</italic>)</th><th align="left">% Disparity in economically disadvantaged enrollment in FL (<italic>SD</italic>)</th></tr></thead><tbody valign="top"><tr><td>1</td><td>116</td><td>M (SD)</td><td>498.05 (240.36)</td><td>27.71 (18.84)</td><td>61.58 (11.85)</td><td>22.59 (13.87)</td><td>0.14 (4.60)</td><td>−3.83 (8.22)</td></tr><tr><td>2</td><td>64</td><td>788.36 (482.17)</td><td>39.52 (18.35)</td><td>62.67 (13.23)</td><td>20.51 (12.95)</td><td>−8.85 (6.29)</td><td>−10.60 (7.03)</td></tr><tr><td>3</td><td>62</td><td>163.27 (188.24)</td><td>75.41 (19.78)</td><td>89.32 (10.84)</td><td>6.96 (10.79)</td><td>2.84 (5.15)</td><td>1.51 (3.94)</td></tr><tr><td>4</td><td>102</td><td>634.96 (347.25)</td><td>88.57 (12.85)</td><td>84.43 (8.58)</td><td>28.18 (16.56)</td><td>−0.95 (2.44)</td><td>−0.44 (3.77)</td></tr><tr><td>5</td><td>38</td><td>1634.63 (471.18)</td><td>41.39 (18.32)</td><td>46.57 (17.27)</td><td>41.42 (19.89)</td><td>−0.94 (2.10)</td><td>−1.52 (3.22)</td></tr></tbody></table> </ephtml> </p> <p>In terms of enrollment in FL courses, none of the clusters reached a 50% threshold, which would indicate that all students at the site either were taking (or had already taken) at least two years of FL coursework. In other words, at a high school serving grades 9–12, if all freshmen and sophomores were enrolled in FL coursework (to qualify them to earn a <emph>TOPS University Diploma</emph>), approximately 50% of the students would be reported as the FL enrollment rate. However, as demonstrated below, none of the clusters reached this rate, with Cluster 5 reaching the highest rate of enrollment at a mean of 41.43% (<emph>SD</emph> = 18.32) and Cluster 3 reporting the lowest rate at a mean of 6.96% (<emph>SD</emph> = 10.79).</p> <p>Perhaps most notable among the disparity statistics is that the majority of clusters did <emph>not</emph> demonstrate large negative percentage disparities (i.e., there were not large differences between the minority and economically disadvantaged groups at the whole school site vs. in the FL program). In fact, both minoritized and economically disadvantaged students were slightly overrepresented in Cluster 3, and minoritized students were overrepresented in Cluster 1. However, Cluster 2 demonstrates a notable disparity, with a mean disparity of −8.85% (<emph>SD</emph> = 6.29) between minority enrollment overall and in FL coursework and a mean disparity of −10.60% (<emph>SD</emph> = 7.03) for economically disadvantaged students. While performing additional analyses on the disparity data would be useful to determine whether any of the clusters' enrollments approach statistically significant differences, the data were not normally distributed enough to do so. Skewness and kurtosis levels, histograms, box plots, and tests of normality all indicated that the data were abnormally distributed and that the data violated assumptions of relevant nonparametric tests (i.e., Friedman's test; data do not represent a random sample).</p> <p>Cluster validation measures were taken to assess the internal cluster validity of the five FL program profiles generated by the analysis. The measures taken were calculating the silhouette coefficient and the Dunn index, statistics designed to measure the distances between clusters and clusters' compactness. Table 5 below contains the average silhouette widths of each cluster, as well as the overall Dunn index statistic.</p> <p>5 Table Internal validation measures.</p> <p> <ephtml> <table><thead valign="bottom"><tr valign="bottom"><th /><th align="left">Cluster</th></tr><tr valign="bottom"><th /><th align="left">1</th><th align="left">2</th><th align="left">3</th><th align="left">4</th><th align="left">5</th></tr></thead><tbody valign="top"><tr><td>Silhouette coefficient</td><td>0.13</td><td>0.09</td><td>0.23</td><td>0.23</td><td>0.27</td></tr><tr><td>Dunn index</td><td>0.07</td></tr></tbody></table> </ephtml> </p> <p>Although the silhouette coefficient was not ideal (a value close to 1 indicates objects are well clustered; a value close to −1 indicates objects are not well clustered; Kassambara, [<reflink idref="bib25" id="ref146">25</reflink>]), the coefficient of each cluster was above zero, indicating that, on average, the data were appropriately clustered. The Dunn index was similarly less than ideal, being so close to zero. These two measures taken together indicate that there may be some issues with cluster assignment in the model.</p> <p>Extracting those school sites whose individual silhouette coefficient was less than zero shows that of the 382 school sites, 59 were potentially incorrectly assigned, though because these negative coefficients were still close to zero, it cannot be stated with certainty that the sites were incorrectly assigned—rather, the sites lie between clusters. Table 6 below contains a summary of the distributions of which clusters the sites were originally assigned to and to which cluster they might actually belong.</p> <p>6 Table Sites that may have been incorrectly assigned to a cluster.</p> <p> <ephtml> <table><thead valign="bottom"><tr valign="bottom"><th /><th /><th align="left"><italic>N</italic> sites that may have been inside this neighbor cluster</th><th>Total (% of assigned cluster sites)</th></tr><tr valign="bottom"><th /><th /><th align="left">1</th><th align="left">2</th><th align="left">3</th><th align="left">4</th><th align="left">5</th></tr></thead><tbody valign="top"><tr><td>Assigned cluster</td><td>1</td><td>0</td><td>5</td><td>7</td><td>6</td><td>4</td><td>22 (18.97)</td></tr><tr><td>2</td><td>9</td><td>0</td><td>0</td><td>2</td><td>5</td><td>16 (25.00)</td></tr><tr><td>3</td><td>1</td><td>0</td><td>0</td><td>7</td><td>0</td><td>8 (12.90)</td></tr><tr><td>4</td><td>0</td><td>2</td><td>9</td><td>0</td><td>1</td><td>12 (11.76)</td></tr><tr><td>5</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>1 (2.63)</td></tr></tbody></table> </ephtml> </p> <p>As illustrated in Table 6 above, Clusters 1 and 2 had the highest rates of potential mis‐assignments, with rates of 18.97% (Cluster 1, <emph>N</emph> = 116) and 25.00% (Cluster 2, <emph>N</emph> = 64), respectively. Clusters 3 and 4 had the second highest tier of potential incorrect assignment rates at 12.90% (Cluster 3, <emph>N</emph> = 62) and 11.76% (Cluster 4, <emph>N</emph> = 102). The lowest rate of potential incorrect assignment was in Cluster 5 with a rate of just 2.63% (Cluster 4, <emph>N</emph> = 38). Taken together, the cluster model had an average rate of 15.45% incorrect assignment, giving a minimum successful assignment rate of 84.55%, which was deemed felicitous enough to continue with the analysis.</p> <p>None of the five cluster "profiles" generated by the cluster analysis stand out as being perfectly equitable with regard to FL enrollment. Clusters 1, 3, and 5 represent the closest approximation of equity in FL enrollment, though all three exhibited low FL enrollment overall. Cluster 2 had the highest degrees of racial and economic inequity, and while Cluster 3 did not have issues with inequity, its schools had by far the lowest FL enrollment. In sum, each cluster demonstrated its own set of issues relating to racial and economic equity.</p> <hd id="AN0180802689-15">RQ2: If meaningful program profile clusters exist, which factors impact cluster membership?</hd> <p>After verifying that the data met the appropriate assumptions (collinearity tolerance statistics all approached 1, with none below 0.453; VIF values all approaching 1, with none above 2.5; low condition indexes with none above 7; no dimensions [<emph>N</emph> = 14] had two or more predictor variables with variance proportions surpassing 0.5), the data were prepared for multinominal logistic regression analysis.</p> <p>A preliminary inspection of the descriptive statistics of each potential variable impacting cluster membership indicated several notable patterns. First, in terms of income disparity between white and minority families, Clusters 3 and 4 had more negative numbers than the other clusters, indicating that these sites were in parishes where there was a higher degree of disparity in favor of white households. In terms of desegregation orders, very few voluntary desegregation orders were observed, and they were mostly found in schools within Clusters 3 and 4. In terms of involuntary desegregation orders, every cluster contained schools that were within parishes that had been put under a court‐mandated desegregation order, with Clusters 2 and 5 having the most schools that have experienced court‐mandated desegregation orders. Special school districts were concentrated in Clusters 3 and 4, with Cluster 1 containing only one high school site within a special school district. In terms of Title I status, the majority of sites across all clusters were classified as schoolwide Title I schools, with the exception of Cluster 5 which had the highest number of schools that were not eligible for Title I funding at all. The majority of schools across all clusters were <emph>not</emph> located within QOZs, with Cluster 4 containing the most schools that were actually within a QOZ (i.e., located in a high‐poverty area). Finally, with regard to rurality, Clusters 1 and 2 contained the most rural schools, with Clusters 4 and 5 containing the most schools in urbanized areas (Table 7).</p> <p>7 Table Descriptive statistics of potential program‐external variables impacting cluster membership.</p> <p> <ephtml> <table><thead valign="bottom"><tr valign="bottom"><th /><th /><th align="left">Total <italic>N</italic> (%)</th><th align="left">Cluster 1 <italic>N</italic> (%)</th><th align="left">Cluster 2 <italic>N</italic> (%)</th><th align="left">Cluster 3 <italic>N</italic> (%)</th><th align="left">Cluster 4 <italic>N</italic> (%)</th><th align="left">Cluster 5 <italic>N</italic> (%)</th></tr><tr valign="bottom"><th /><th><italic>N</italic></th><th align="left">382</th><th align="left">116</th><th align="left">64</th><th align="left">62</th><th align="left">102</th><th align="left">38</th></tr></thead><tbody valign="top"><tr><td>Income disparity</td><td>% difference</td><td>−27.36 (19.87)</td><td>−21.78 (26.84)</td><td>−26.57 (16.44)</td><td>−33.51 (14.05)</td><td>−32.09 (14.82)</td><td>−23.16 (14.20)</td></tr><tr><td>Involuntary desegregation orders</td><td>Never</td><td>85 (22.3)</td><td>29 (25.0)</td><td>9 (14.1)</td><td>20 (32.3)</td><td>21 (20.6)</td><td>6 (15.8)</td></tr><tr><td>Yes, closed</td><td>142 (37.2)</td><td>36 (31.0)</td><td>25 (39.1)</td><td>17 (27.4)</td><td>47 (46.1)</td><td>17 (44.7)</td></tr><tr><td>Yes, open</td><td>155 (40.6)</td><td>51 (44.0)</td><td>30 (46.9)</td><td>25 (40.3)</td><td>34 (33.3)</td><td>15 (39.5)</td></tr><tr><td>Voluntary desegregation orders</td><td>Never</td><td>352 (92.1)</td><td>113 (97.4)</td><td>61 (95.3)</td><td>55 (88.7)</td><td>86 (84.3)</td><td>37 (97.4)</td></tr><tr><td>Yes, closed</td><td>30 (7.9)</td><td>3 (2.6)</td><td>3 (4.7)</td><td>7 (11.3)</td><td>16 (15.7)</td><td>1 (2.6)</td></tr><tr><td>Yes, open</td><td>0 (0)</td><td>0 (0)</td><td>0 (0)</td><td>0 (0)</td><td>0 (0)</td><td>0 (0)</td></tr><tr><td>Special school districts</td><td>No</td><td>308 (80.6)</td><td>115 (99.1)</td><td>54 (84.4)</td><td>38 (61.3)</td><td>72 (70.6)</td><td>29 (76.3)</td></tr><tr><td>Yes</td><td>66 (17.3)</td><td>1 (0.9)</td><td>9 (14.1)</td><td>22 (35.5)</td><td>27 (26.5)</td><td>7 (18.4)</td></tr><tr><td>City‐based</td><td>8 (2.1)</td><td>0 (0)</td><td>1 (1.6)</td><td>2 (3.2)</td><td>3 (2.9)</td><td>2 (5.3)</td></tr><tr><td>Title I status</td><td>Not reported</td><td>18 (4.7)</td><td>1 (0.9)</td><td>1 (1.6)</td><td>9 (14.5)</td><td>4 (3.9)</td><td>3 (7.9)</td></tr><tr><td>TA eligible—None</td><td>9 (2.4)</td><td>3 (2.6)</td><td>3 (4.7)</td><td>0 (0)</td><td>0 (0)</td><td>3 (7.9)</td></tr><tr><td>TA school</td><td>1 (0.3)</td><td>0 (0)</td><td>0 (0)</td><td>0 (0)</td><td>0 (0)</td><td>1 (2.6)</td></tr><tr><td>SW eligible—TA program</td><td>3 (0.8)</td><td>0 (0)</td><td>1 (1.6)</td><td>0 (0)</td><td>0 (0)</td><td>2 (5.3)</td></tr><tr><td>SW eligible school—None</td><td>75 (19.6)</td><td>33 (28.4)</td><td>23 (35.9)</td><td>7 (11.3)</td><td>4 (3.9)</td><td>8 (21.1)</td></tr><tr><td>SW school</td><td>241 (63.1)</td><td>69 (59.5)</td><td>31 (48.4)</td><td>40 (64.5)</td><td>94 (92.2)</td><td>7 (18.4)</td></tr><tr><td>Not a Title I school</td><td>35 (9.2)</td><td>10 (8.6)</td><td>5 (7.8)</td><td>6 (9.7)</td><td>0 (0)</td><td>14 (36.8)</td></tr><tr><td>Qualified opportunity zones status</td><td>No</td><td>323 (84.6)</td><td>106 (91.4)</td><td>55 (85.9)</td><td>52 (83.9)</td><td>74 (72.5)</td><td>36 (94.7)</td></tr><tr><td>Yes</td><td>59 (15.4)</td><td>10 (8.6)</td><td>9 (14.1)</td><td>10 (16.1)</td><td>28 (27.5)</td><td>2 (5.3)</td></tr><tr><td>Rurality</td><td>Rural</td><td>189 (49.5)</td><td>87 (75.0)</td><td>36 (56.3)</td><td>28 (45.2)</td><td>23 (22.5)</td><td>15 (39.5)</td></tr><tr><td>Urban cluster</td><td>62 (16.2)</td><td>22 (19.0)</td><td>14 (21.9)</td><td>8 (12.9)</td><td>17 (16.7)</td><td>1 (2.6)</td></tr><tr><td>Urbanized area</td><td>131 (34.3)</td><td>7 (6.0)</td><td>14 (21.9)</td><td>26 (41.9)</td><td>62 (60.8)</td><td>22 (57.9)</td></tr></tbody></table> </ephtml> </p> <p>3 Abbreviations: SW = schoolwide; TA = targeted assistance.</p> <hd id="AN0180802689-16">Model fit</hd> <p>Analysis of the model fitting tests indicates that the model was a statistically significant improvement over a baseline model with no predictors. Despite the Pearson goodness‐of‐fit test indicating a statistically significant result (Pearson: <emph>χ</emph>² = 2269.65, <emph>df</emph> = 812, <emph>p</emph> < 0.001), likelihood ratio (<emph>χ</emph>² = 281.31; <emph>df</emph> = 60; <emph>p</emph> < 0.001), deviance goodness‐of‐fit (Deviance: <emph>χ</emph>² = 584.97, <emph>df</emph> = 804, <emph>p</emph> = 1.000), and Pseudo <emph>R</emph><sups>2</sups> (McFadden = 0.255) tests all indicated that the model was of a good fit.</p> <p>As shown in Table 8 below, all predictor variables contributed significantly to the model fit except voluntary desegregation orders. This lack of significance may be due to the fact that there have been so few voluntary desegregation orders in the data set (<emph>N</emph> = 4; U.S. Department of Justice, [<reflink idref="bib71" id="ref147">71</reflink>]).</p> <p>8 Table Likelihood ratio tests for each factor.</p> <p> <ephtml> <table><thead valign="bottom"><tr valign="bottom"><th /><th><italic>χ</italic>²</th><th><italic>df</italic></th><th><italic>p</italic></th></tr></thead><tbody valign="top"><tr><td>Intercept</td><td>0.000</td><td>0</td><td>‐</td></tr><tr><td>Income disparity</td><td>26.73</td><td>4</td><td><0.001</td></tr><tr><td>Involuntary desegregation order</td><td>30.87</td><td>8</td><td><0.001</td></tr><tr><td>Voluntary desegregation order</td><td>4.65</td><td>4</td><td>0.326</td></tr><tr><td>Special district</td><td>17.70</td><td>8</td><td>0.024</td></tr><tr><td>Qualified opportunity zone status</td><td>10.12</td><td>4</td><td>0.038</td></tr><tr><td>Title I status</td><td>72.74</td><td>24</td><td><0.001</td></tr><tr><td>Rurality</td><td>90.30</td><td>8</td><td><0.001</td></tr></tbody></table> </ephtml> </p> <hd id="AN0180802689-17">Contributions of individual factors</hd> <p>After the initial model fit tests, a reference category was selected, against which each of the remaining target categories would be compared. Because Cluster 5 most closely reflected the racial makeup of the state overall (indicating less segregation by race; USCB, [<reflink idref="bib63" id="ref148">63</reflink>]), with the highest overall rate of FL enrollment and small degrees of enrollment disparity for minority (−0.94%) and economically disadvantaged (−1.52%) students, this cluster was selected as the reference category. As a result, the statistics reported in Table 9 all reflect comparisons between Cluster 5 and each remaining cluster. Due to the high number of predictor variables, only those predictor variables that yielded statistically significant results are reported here.</p> <p>9 Table Results of the nominal logistic regression (significant results only).</p> <p> <ephtml> <table><thead valign="bottom"><tr valign="bottom"><th /><th>Cluster 1 versus Cluster 5</th><th>Cluster 2 versus Cluster 5</th><th>Cluster 3 versus Cluster 5<ext-link href="a" /></th><th align="left">Cluster 4 versus Cluster 5<ext-link href="a" /></th></tr><tr valign="bottom"><th /><th><italic>B</italic> (SE)</th><th>Exp(B) odds ratio</th><th>95% CIs exp(B) lower, upper</th><th><italic>p</italic></th><th><italic>B</italic> (<italic>SE</italic>)</th><th>Exp(B) Odds Ratio</th><th>95% CIs Exp(B) Lower, Upper</th><th><italic>p</italic></th><th><italic>B</italic> (<italic>SE</italic>)</th><th>Exp(B) Odds Ratio</th><th>95% CIs Exp(B) Lower, Upper</th><th><italic>p</italic></th><th><italic>B</italic> (<italic>SE</italic>)</th><th>Exp(B) Odds Ratio</th><th>95% CIs Exp(B) Lower, Upper</th><th><italic>p</italic></th></tr></thead><tbody valign="top"><tr><td>Intercept</td><td>−4.83 (2.79)</td><td>.</td><td>.</td><td>0.083</td><td>−2.95 (2.49)</td><td>.</td><td>.</td><td>0.236</td><td>−1.37 (2.30)</td><td>.</td><td>.</td><td>0.552</td><td>−2.91 (2.48)</td><td>.</td><td>.</td><td>0.242</td></tr><tr><td>Title I: SW eligible, none Title I: SW</td><td>1.71 (0.65) 2.99 (0.63)</td><td>5.52 19.90</td><td>1.55, 19.62 5.75, 68.88</td><td>0.008 <0.001</td><td>2.40 (0.72) 2.69 (0.72)</td><td>11.06 14.78</td><td>2.67, 45.72 3.61, 60.63</td><td><0.001 <0.001</td><td>0.2.65 (0.68)</td><td>14.15</td><td>3.75, 53.35</td><td><0.001</td><td>3.05 (1.39) 5.42 (1.33)</td><td>21.08 226.67</td><td>1.38, 322.19 16.88, 3044.68</td><td>0.028 <0.001</td></tr><tr><td>Rural Urban cluster</td><td>2.10 (0.56) 2.46 (0.86)</td><td>8.20 11.66</td><td>2.75, 24.46 2.17, 62.71</td><td><0.001 0.004</td><td>1.28 (0.55) 2.01 (0.86)</td><td>3.59 7.49</td><td>1.21, 10.64 1.37, 40.53</td><td>0.021 0.019</td><td>.</td><td>.</td><td>.</td><td>.</td><td>.</td><td>.</td><td>.</td><td>.</td></tr></tbody></table> </ephtml> </p> <p>4 a There was also a statistically significant <emph>p</emph>‐value for schools without a reported Title I status in this comparison. However, because a lack of reported status does not provide any useful information about a site's status (i.e., what their status would be, had they reported; what prevented them from reporting), these values have been omitted.</p> <p>An examination of the odds ratios shows that each of the target categories (Clusters 1–4) are all more likely to be Title I schoolwide sites than those in Cluster 5, with schools in Cluster 4 being over 200 times more likely to be schoolwide programs than those in Cluster 5. For those schools who are eligible for schoolwide Title I funding but do not have any program at all, Clusters 1, 2, and 4 are all more likely to fall into this status category, with schools in Cluster 4 being over 20 times more likely than Cluster 5 to be in this status category. Turning to rurality, schools in Clusters 1 and 2 are both more likely to be within areas classified as rural or urban clusters, with rurality playing a bigger role in determining the odds of membership in Cluster 1.</p> <p>In sum, the logistic regression analysis demonstrated that, of the seven program‐external factors selected for inclusion in the model, only two had a significant impact on whether a school exhibited high degrees of inequity in FL enrollment: Title I funding status and rurality.</p> <hd id="AN0180802689-18">LIMITATIONS</hd> <p>Like any research endeavor, this study is not without its limitations. First, by relying entirely on quantitative data from publicly available government documents, there is inevitably a lack of qualitative nuance from those studied: the FL students themselves, their teachers, school administrators, and other FL stakeholders. By relying solely on quantitative reports, this study was not able to give "voice" to the particular needs, experiences, and expectations of Louisiana high school students (considering) taking a FL. Second, as acknowledged above, the researcher had to exert some influence in the number of clusters generated in the analysis, breaking the "tie" in the recommended number of clusters generated by the function <emph>nbClust()</emph>. As demonstrated above, quite a few schools were incorrectly assigned to a cluster (see Table 6), indicating some flaws in the cluster analysis. Finally, as with any data simplification—in this case, the cluster analysis—there is some granularity in the discussion that must be killed. However, it is the researcher's hope that those who use the results presented here do so with caution and acknowledgment that variation exists from school to school and from student to student.</p> <hd id="AN0180802689-19">DISCUSSION</hd> <p>The analyses above have shone a light on the ways in which FL programs cluster into meaningful profiles across the state of Louisiana, as well as the factors that may impact those FL programs. The largest cluster, Cluster 1, is made up of mid‐sized schools in overwhelmingly rural communities that are predominately white and low‐income. The programs are housed within high schools spread across 42 school districts, with the largest numbers of Cluster 1 schools found in Allen (<emph>n</emph> = 6), Livingston (<emph>n</emph> = 7), and Vernon (<emph>n</emph> = 9) parishes. Though the average minority enrollment across Cluster 1 is well below the statewide average, four of the five parishes in which median minority household income is actually higher than that of white households have schools within Cluster 1 (US Census Bureau, [<reflink idref="bib62" id="ref149">62</reflink>]). This economic advantage that minority families have in the workforce may be the reason for the slight overrepresentation of minority students in FL coursework, though the mean overall rate of FL enrollment is still low (22.59%). This economic advantage may also have affected the number of desegregation orders impacting schools in Cluster 1 (it has the second‐lowest rate of desegregation orders after Cluster 3; Qiu & Hannah‐Jones, [<reflink idref="bib47" id="ref150">47</reflink>]; U.S. Department of Justice, [<reflink idref="bib71" id="ref151">71</reflink>]). These findings corroborate previous research that suggests that racially integrated schools afford better educational outcomes to all their students (Reardon et al., [<reflink idref="bib49" id="ref152">49</reflink>]; Renzulli & Evans, [<reflink idref="bib50" id="ref153">50</reflink>]; Saporito, [<reflink idref="bib53" id="ref154">53</reflink>]).</p> <p>Cluster 2's schools are large, predominately white, and poor, though not all students in the cluster's 38 school districts are in poverty. In fact, the parishes with the highest number of schools in this cluster—Bossier, Iberia, Jefferson, Lafayette, Lafourche, Ouachita, Rapides, and West Carroll—all have middling income and race‐based income disparities (US Census Bureau, [<reflink idref="bib62" id="ref155">62</reflink>]). Additionally, four of the top five parishes with the highest median white incomes (Ascension, East Baton Rouge, St. Charles, St. James, and West Feliciana) are represented in Cluster 2. However, Cluster 2 also has the highest proportion of schools impacted by desegregation orders and has the most open, involuntary desegregation orders of all the clusters (Qiu & Hannah‐Jones, [<reflink idref="bib47" id="ref156">47</reflink>]; U.S. Department of Justice, [<reflink idref="bib71" id="ref157">71</reflink>]). This (hi)story of discrimination is borne out by its FL enrollment rates, with Cluster 2 being the group of programs that exhibits the greatest disparities in FL enrollment. When minoritized (<emph>M</emph> = −8.85%; SD = 6.29) and economically disadvantaged (<emph>M</emph> = −10.60%; SD = 7.03) students are underrepresented to the extent they are in FL programs, these students' representation in higher education will be correspondingly low, due to Louisiana's high school diploma type requirements (Macdonald et al., [<reflink idref="bib37" id="ref158">37</reflink>]; O'Rourke et al., [<reflink idref="bib43" id="ref159">43</reflink>]). These factors merit further study to determine whether there are programmatic changes that can be made to counteract discriminatory counseling and tracking practices (Anya, [<reflink idref="bib1" id="ref160">1</reflink>]; Moore, [<reflink idref="bib41" id="ref161">41</reflink>]; Glynn & Wassell, [<reflink idref="bib18" id="ref162">18</reflink>]).</p> <p>Cluster 3 is made up of small schools that are overwhelmingly populated by minoritized and economically disadvantaged students. Notably, Cluster 3 contains a large number of nontraditional schools, including <emph>alternative schools</emph> (<emph>n</emph> = 5) for students with behavioral problems that prevent them from enrolling in mainstream schools, <emph>detention centers and juvenile facilities</emph> (<emph>n</emph> = 7), and support and service centers like <emph>rehabilitation hospitals</emph> (<emph>n</emph> = 7). Together, these nontraditional schooling environments account for nearly a third (30.65%) of the schools in this cluster, which may account for the low FL enrollment rates. In fact, 35 of the 36 schools with an FL enrollment rate of 0% are within Cluster 3 (the remaining school is in Cluster 4). This results in over half (56.45%) of all schools in Cluster 3 having no FL program at all. The schools that do have FL programs have minoritized and economically disadvantaged students overrepresented, which could be an indication of successful recruitment and retention of these students in particular. Connecting those Cluster 3 schools without an FL program with schools within the cluster that do have FL might be a fruitful way to increase access to FL coursework in the highest minority and highest poverty cluster. Unfortunately, little is known about how language education manifests in these specialized, nontraditional school settings (e.g., ELLs in Flores, [<reflink idref="bib17" id="ref163">17</reflink>]). However, it is possible that there might be a generalized expectation that students attending schools in these clusters will not be attending a 4‐year college or university, leading administrators to prioritize subjects required for the <emph>Jump Start: Career Diploma</emph>, though additional research is needed to explore this as a possible explanation.</p> <p>Cluster 4 contains mid‐sized schools that serve mostly minority (88.57%) and economically disadvantaged students (84.43%). Of the 37 parishes that contain Cluster 4 schools, by far, the parishes with the largest number of schools in Cluster 4 are East Baton Rouge and Orleans—the two of the most highly populated parishes in the state, resulting in Cluster 4 being the most urban of the five clusters. This urbanity might be one reason why the rate of FL enrollment is higher than the other high‐poverty clusters (esp. Cluster 3), as it is usually easier for schools in urbanized areas to recruit and maintain their teachers (Ingersoll, [<reflink idref="bib22" id="ref164">22</reflink>]). Additionally, the overwhelming majority of schools within Cluster 4 (92.2%) receive schoolwide Title I funding, meaning that very few schools (3.9%) are passed over when their districts are allocating funding. This comparatively higher funding may also contribute to these schools' higher rate of FL enrollment (28.18%), as they are potentially better funded than those schools in Clusters 1–3, where anywhere from a tenth to a third of Title I‐eligible schools do not receive Title I funds (Bajak et al., [<reflink idref="bib5" id="ref165">5</reflink>]; U.S. Department of Education, [<reflink idref="bib68" id="ref166">68</reflink>]).</p> <p>Finally, Cluster 5 contains very large, predominately white schools with relatively low levels of poverty compared to the other clusters. Like Cluster 4, Cluster 5's schools are mostly urban, though this cluster contains only the largest cities (e.g., New Orleans, Baton Rouge) and very few urban clusters. The schools in this cluster are by far the largest, with an average student population well over one thousand per site (<emph>M</emph> = 1,635 students per school). Cluster 5's schools are spread across only 17 school districts, with a higher proportion of city‐based school districts than any other cluster. These city‐based school districts are the result of "break‐away" communities formed by white flight (Harris, [<reflink idref="bib20" id="ref167">20</reflink>]; Renzulli & Evans, [<reflink idref="bib50" id="ref168">50</reflink>]). Also included in this cluster are highly selective and high‐performing magnet schools, which may explain why so many of its students are enrolled in FL coursework (nearly half—41.42% on average), so that those students can qualify for the <emph>TOPS University Diploma</emph> and attend a university after graduation.</p> <p>Taken together, these five cluster profiles do not coalesce into neat descriptions of "good" and "bad" places for FL study. Instead, they represent the complex reality of FL enrollment patterns in Louisiana high schools—one where multiple layers of advantages and disadvantages intersect in ways that complicate the picture for policymakers charged with improving access. The picture is likely to be further complicated by the recent expansion of the FL requirements to include computer science coursework (Louisiana Believes, [<reflink idref="bib35" id="ref169">35</reflink>])–though as of the writing of this piece, the change's impact is yet to come into focus due to a lack of data. Table 10 below summarizes the intersections revealed by the current analysis and proposes some potential solutions that policymakers, researchers, and other stakeholders may want to consider layering, piloting, and adapting to improve FL enrollment rates across all five clusters. In presenting these initial proposals, the author does not aim to anticipate all meaningful and worthwhile solutions but rather hopes this paper can be used as a starting point for a larger conversation among policymakers, researchers, and others to ensure schools in each of the five clusters receive support that is relevant to their programmatic needs and enrollment patterns.</p> <p>10 Table Proposed intersectional solutions.</p> <p> <ephtml> <table><thead valign="bottom"><tr valign="bottom"><th /><th align="center">Cluster 1</th><th>Cluster 2</th><th align="center">Cluster 3</th><th align="center">Cluster 4</th><th align="center">Cluster 5</th></tr></thead><tbody valign="top"><tr><td>Increasing racial equity<list list-type="Bullet"><list-item><p>Investigate counseling practices to determine whether bias may be influencing who is recommended for foreign language (FL) study (e.g., Anya, <xref ref-type="bibr" rid="bibr1">2020</xref>).</p></list-item><list-item><p>Interview students, teachers, administrators, and other stakeholders to determine whether students from minoritized racial backgrounds are able to see themselves reflected in the FL curriculum.</p></list-item></list></td><td /><td><graphic href="" /></td><td /><td align="center"><graphic href="" /></td><td align="center"><graphic href="" /></td></tr><tr><td>Increasing equity for ED students<list list-type="Bullet"><list-item><p>Interview students, teachers, administrators, and other stakeholders to determine whether any FL‐specific financial barriers are present (e.g., FL‐specific fees).</p></list-item><list-item><p>Investigate counseling practices to determine whether bias may be influencing who is recommended for FL study (e.g., Schoener & McKenzie, <xref ref-type="bibr" rid="bibr54">2016</xref>).</p></list-item></list></td><td align="center"><graphic href="" /></td><td><graphic href="" /></td><td /><td align="center"><graphic href="" /></td><td align="center"><graphic href="" /></td></tr><tr><td>Increasing recruitment and retention of rural FL teachers<list list-type="Bullet"><list-item><p>Build on work by researchers in related education fields (e.g., Tran & Smith, 2020 in general education) to determine what motivates teachers to work in rural areas.</p></list-item><list-item><p>Work with schools and districts to support small FL programs and insulate low enrollment programs from job cuts.</p></list-item></list></td><td align="center"><graphic href="" /></td><td><graphic href="" /></td><td /><td /><td /></tr><tr><td>Increasing funding<list list-type="Bullet"><list-item><p>Conduct interviews and focus groups with schools that do not receive Title I funding to determine whether funds can be (re)allocated (Bajak et al., <xref ref-type="bibr" rid="bibr5">2020</xref> ; Snyder et al., <xref ref-type="bibr" rid="bibr55">2019</xref> ; U.S. Department of Education, <xref ref-type="bibr" rid="bibr68">2019</xref>).</p></list-item><list-item><p>Work with schools and districts to identify additional funding sources (e.g., qualified opportunity zones; IRS, <xref ref-type="bibr" rid="bibr24">2018</xref> , <xref ref-type="bibr" rid="bibr23">2021</xref>).</p></list-item></list></td><td align="center"><graphic href="" /></td><td><graphic href="" /></td><td align="center"><graphic href="" /></td><td align="center"><graphic href="" /></td><td /></tr></tbody></table> </ephtml> </p> <p>Importantly, when interpreting the cluster profiles and program‐external factors, it must be recalled that enrollment in FL coursework has long‐lasting impacts beyond high school. In particular, the fact that two years of FL coursework are required for enrollment in a 4‐year college or university program upon graduation from high school means that the question of whether or not a student is able to access an FL class will impact their future earning potential, career prospects, and other aspects of their adult life. As a result, policymakers, school administrators, high school counselors, FL teachers, and all other stakeholders must be mindful and intentional in the ways in which they address issues of access to FL education.</p> <hd id="AN0180802689-20">CONCLUSION AND FUTURE RESEARCH</hd> <p>The analyses presented in this paper addressed the extent to which high schools in Louisiana could be clustered into "profiles" based on the policies of their FL programs and what factors may have impacted each school's assignment into a given cluster, that is, "profile." The cluster analysis indicated that FL programs do, in fact, cluster into five distinct profiles, each with varying degrees of equity and accessibility to FL education. Multinomial logistic regression analysis revealed that the two factors that played the biggest roles in determining cluster (i.e., "profile") membership—and, by extension, access to FL—were a school's site‐level Title I funding status and the degree of rurality of its district, with economically disadvantaged high school students attending Title I schools and rural schools being the most likely to experience inequitable access to FL coursework. These findings will need to be contrasted with new FL enrollment data, as it becomes available to monitor the impact of expanding FL requirements and determine the type and degree of impact these changes have on access to FL education in Louisiana. The author hopes these findings will be useful to policymakers in that they demonstrate that to increase equitable access to FL in Louisiana, tailored action plans must be carefully developed, responding to existing (in)accessibility of FL in high schools across the state.</p> <hd id="AN0180802689-21">ACKNOWLEDGMENTS</hd> <p>Thank you to Michèle Braud and Paula Calderon for inspiring me to think deeply about education and use my voice for our chère Louisiane.</p> <ref id="AN0180802689-22"> <title> REFERENCES </title> <blist> <bibl id="bib1" idref="ref10" type="bt">1</bibl> <bibtext> Anya, U. (2020). African Americans in world language study: The forged path and future directions. Annual Review of Applied Linguistics, 40, 97 – 112.</bibtext> </blist> <blist> <bibl id="bib2" idref="ref48" type="bt">2</bibl> <bibtext> Anya, U., & Randolph, L. J. (2019). Diversifying language educators and learners. 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Items – Name: Title
  Label: Title
  Group: Ti
  Data: Foreign Language Education in Louisiana: A Cluster Analysis
– Name: Language
  Label: Language
  Group: Lang
  Data: English
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Erin+Fell%22">Erin Fell</searchLink> (ORCID <externalLink term="http://orcid.org/0000-0001-5667-8935">0000-0001-5667-8935</externalLink>)
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="SO" term="%22Foreign+Language+Annals%22"><i>Foreign Language Annals</i></searchLink>. 2024 57(3):698-724.
– Name: Avail
  Label: Availability
  Group: Avail
  Data: Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us
– Name: PeerReviewed
  Label: Peer Reviewed
  Group: SrcInfo
  Data: Y
– Name: Pages
  Label: Page Count
  Group: Src
  Data: 27
– Name: DatePubCY
  Label: Publication Date
  Group: Date
  Data: 2024
– Name: TypeDocument
  Label: Document Type
  Group: TypDoc
  Data: Journal Articles<br />Reports - Research
– Name: Audience
  Label: Education Level
  Group: Audnce
  Data: <searchLink fieldCode="EL" term="%22High+Schools%22">High Schools</searchLink><br /><searchLink fieldCode="EL" term="%22Secondary+Education%22">Secondary Education</searchLink><br /><searchLink fieldCode="EL" term="%22Elementary+Secondary+Education%22">Elementary Secondary Education</searchLink>
– Name: Subject
  Label: Descriptors
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Second+Language+Learning%22">Second Language Learning</searchLink><br /><searchLink fieldCode="DE" term="%22Second+Language+Instruction%22">Second Language Instruction</searchLink><br /><searchLink fieldCode="DE" term="%22High+School+Students%22">High School Students</searchLink><br /><searchLink fieldCode="DE" term="%22State+Policy%22">State Policy</searchLink><br /><searchLink fieldCode="DE" term="%22Educational+Policy%22">Educational Policy</searchLink><br /><searchLink fieldCode="DE" term="%22Required+Courses%22">Required Courses</searchLink><br /><searchLink fieldCode="DE" term="%22Educational+Change%22">Educational Change</searchLink><br /><searchLink fieldCode="DE" term="%22Minority+Group+Students%22">Minority Group Students</searchLink><br /><searchLink fieldCode="DE" term="%22Equal+Education%22">Equal Education</searchLink><br /><searchLink fieldCode="DE" term="%22Access+to+Education%22">Access to Education</searchLink><br /><searchLink fieldCode="DE" term="%22Federal+Aid%22">Federal Aid</searchLink><br /><searchLink fieldCode="DE" term="%22Rural+Areas%22">Rural Areas</searchLink><br /><searchLink fieldCode="DE" term="%22Educational+Finance%22">Educational Finance</searchLink><br /><searchLink fieldCode="DE" term="%22Disadvantaged+Schools%22">Disadvantaged Schools</searchLink><br /><searchLink fieldCode="DE" term="%22Language+Enrollment%22">Language Enrollment</searchLink><br /><searchLink fieldCode="DE" term="%22Educational+Legislation%22">Educational Legislation</searchLink><br /><searchLink fieldCode="DE" term="%22Federal+Legislation%22">Federal Legislation</searchLink><br /><searchLink fieldCode="DE" term="%22Elementary+Secondary+Education%22">Elementary Secondary Education</searchLink>
– Name: Subject
  Label: Geographic Terms
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Louisiana%22">Louisiana</searchLink>
– Name: SubjectThesaurus
  Label: Laws, Policies and Program Identifiers
  Group: Su
  Data: <searchLink fieldCode="SU" term="%22Elementary+and+Secondary+Education+Act+Title+I%22">Elementary and Secondary Education Act Title I</searchLink>
– Name: DOI
  Label: DOI
  Group: ID
  Data: 10.1111/flan.12737
– Name: ISSN
  Label: ISSN
  Group: ISSN
  Data: 0015-718X<br />1944-9720
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Louisiana is currently the only state in the United States that requires foreign language (FL) study for some, but not all of their high school students, and these requirements are undergoing seismic changes. Considering that "geographic," "economic," and "integration" factors contribute to whether a school can provide FL at all--and biased counseling dissuades minoritized students from taking FL--this study asks: "has FL education been equitably accessible to all Louisiana high school students who want to pursue it, regardless of race or economic background?" To address this question, this paper presents results from two analyses. First, program-internal equity variables (e.g., [dis]similarity between school-wide and FL student demographics) were clustered to produce "profiles" of FL programs. Next, a multinomial logistic regression using program-external factors (e.g., federal funding status, desegregation orders) was conducted to isolate the factors impacting equity. Federal funding and rurality were both found to be significant, with schools receiving federal Title I funds and rural schools being much more likely to exhibit inequitable access to FL courses. In (a) identifying schools with enrollment inequities and (b) the outside factors associated with greater inequity, this paper aims to provide policymakers with empirically based tools to address (in)equity in Louisiana high school FL education. These findings can be especially helpful in light of the state's recent (2023-2024 school year) expansion of FL requirements to accept computer science courses.
– Name: AbstractInfo
  Label: Abstractor
  Group: Ab
  Data: As Provided
– Name: DateEntry
  Label: Entry Date
  Group: Date
  Data: 2024
– Name: AN
  Label: Accession Number
  Group: ID
  Data: EJ1448002
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=eric&AN=EJ1448002
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  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1111/flan.12737
    Languages:
      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 27
        StartPage: 698
    Subjects:
      – SubjectFull: Second Language Learning
        Type: general
      – SubjectFull: Second Language Instruction
        Type: general
      – SubjectFull: High School Students
        Type: general
      – SubjectFull: State Policy
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      – SubjectFull: Educational Policy
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      – SubjectFull: Educational Change
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      – SubjectFull: Minority Group Students
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      – SubjectFull: Access to Education
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      – SubjectFull: Federal Aid
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      – SubjectFull: Rural Areas
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      – SubjectFull: Educational Finance
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      – SubjectFull: Disadvantaged Schools
        Type: general
      – SubjectFull: Language Enrollment
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      – SubjectFull: Louisiana
        Type: general
      – SubjectFull: Elementary and Secondary Education Act Title I
        Type: general
    Titles:
      – TitleFull: Foreign Language Education in Louisiana: A Cluster Analysis
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            NameFull: Erin Fell
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            – D: 01
              M: 09
              Type: published
              Y: 2024
          Identifiers:
            – Type: issn-print
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              Value: 1944-9720
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              Value: 57
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            – TitleFull: Foreign Language Annals
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