by Rachel Fearon and Ashley Nelson
Last updated on December 5, 2025
for Data Visualization for Allwith Prof. Jack DoughertyTrinity College, Hartford CT, USA
In Connecticut, Multilingual learners (MLs) are defined as public school students whose primary language is not English and whose English proficiency poses a barrier to equitable learning in the regular school program. Massachusetts, similarly, defines MLs as any student who does not speak English or whose primary language is not English and who is unable to satisfactorily complete classwork in English. English learners are also commonly referred to as English learners (ELs) and English language learners (ELLs). Our partners at the School & State Finance Project are particularly interested in improving the data available for ML funding to address the insufficient reporting and allocation of finances in Connecticut, which is necessary to prepare MLs for educational opportunities and success.
Educational funding for MLs ensures that students and educators have the resources to remove linguistic barriers to prioritize accessible education and maximize long-term success. The purpose of this data story is to answer two questions aimed at addressing improved funding for MLs:
1) How are MLs distributed across school districts in CT and MA public school districts? What are some of the most commonly spoken languages amongst MLs?
2) Do district spending levels rise with the percentage of Multilingual Learners in districts across CT and MA?
In response to the first question, we found that in Massachusetts, 90% of school districts have at least one ML student; however, 19% of all districts have at least 100 MLs. Similarly, 7% of Connecticut school districts have over 20% of MLs in their student population.
Equitable funding signifies that the money allotted to ML programs is proportional to the percentage of MLs within each district. This is in contrast to having equal funding for all districts without accounting for the change in need across districts.
Regarding the second question, our key finding is that CT overall has a higher PPE for MLs compared to MA; however, in a district-level comparison, CT districts with a higher percentage of MLs have a lower PPE. Comparatively, Massachusetts has a small, though negligible, positive correlation between ML percentage and PE.
Based on these results, our findings suggest that Connecticut and Massachusetts do not equitably distribute funding across districts with higher percentages of multilingual learners. Such inequities contribute to the widening achievement gaps that necessitate the demands for improved ML funding.
According to the Connecticut State Department of Education (CSDE), the most common non-English languages spoken by MLs are Spanish, Portuguese, Arabic, Haitian Creole, and Mandarin. As per the MIRA 2023 report, in Massachusetts, the top non-English languages are Spanish, Portuguese, Chinese (including Mandarin and Cantonese), Haitian Creole, and Vietnamese. MLs are identified in a four-step process as outlined by the CSDE’s Every Student Succeeds Act (ESSA) and ACCESS for ELs. Upon registering as a student in a public school, students are assessed using the Home Language Survey (HLS) to determine if they are a potential ML. Following the second step, in which HLS results are reviewed, students who have a primary/home language other than English are administered the English Language Proficiency (ELP) screener. In the final step of identification, the results of the ELP screener indicate whether or not the student should be identified as an ML. Once identified as an ML, the students’ parents/guardians are informed of their language service options and are able to choose which services, if any, they opt into.
Funding for MLs ensures equitable learning opportunities for the ML students who make up an increasing proportion of public school students, in both Connecticut and Massachusetts. Notably, districts in Hartford and New Haven serve ML students who speak over 50 unique languages at home. In the states of Connecticut and Massachusetts, funding for elementary-secondary education is sourced from federal grants, state funds, and local funds (US Census Bureau). Connecticut’s Bilingual Education Program and Massachusetts’s Transitional Bilingual Education Program are designed to help students become proficient in English, such that they are able to master the same academic content as their peers. Both programs use English as the instructional language to facilitate this learning. By understanding the funding that is available for the Bilingual Education Programs, the Transitional Bilingual Education Program, and other school-based support, we can better understand how multilingual funding varies across different school districts in Connecticut and Massachusetts.
In making a comparison between Massachusetts and Connecticut, it’s important to acknowledge the population difference. According to the Department of Elementary and Secondary Education (DESE), there were 915,932 students enrolled across public districts in the 2024-2025 fiscal year (Massachusetts Department of Elementary and Secondary Education). This compares to Connecticut’s 508,402 students across public districts (EdSight). However, both states have similar percentages of multilingual and non-multilingual learners across the state in the 2024-25 fiscal year. In each state, over a tenth of the student population is multilingual learners. This high percentage shows the significance of ML programs and addressing equitable funding.
Fig 1. Percent of Multilingual Learners in CT and MA Districts 2024-2025 Fiscal Year shows the percentage of Multilingual Learners in CT and MA school districts. Connecticut has 11.3% MLs, while Massachusetts has 13.9%. This data gives background about the ML student population in CT and MA, and serves as a foundation for understanding the variation in funding within each state.
Table 1a. Percentage of MLs by CT District. This table shows the number and percentages of MLs in Connecticut districts. The Danbury School District has the highest percentage of MLs (38%), with about 4,500 students. Stamford School District has a comparable number of MLs, but because its overall student population is larger, MLs only account for 19% of the population. This represents a discrepancy in volume and proportion, suggesting a difference in relative burden concerning districts that overall serve a smaller number of students, but have a higher percentage of students with additional needs.
Table 1b. Percentage of MLs by MA District. This table shows the number and percentages of MLs in Massachusetts districts. There is a wide range of percentages. Phoenix Academy Charter Public High School has the highest ML enrollment rate at 62.6% with 162 students, whereas districts at the lower end have percentages as low as 0.1%, often only being 1 or 2 students. However, Taunton has the highest individual ML reporting 930 students, which is 11.3% of the district population.
It is important to consider the diversity of languages spoken by MLs in MA and CT; linguistic diversity and variety play a role in resource allocation, particularly when considering the needs of MLs who speak less common primary languages compared to those who share a primary language with several other MLs.
Fig. 2a: Top Languages Spoken by MLs in CT Districts, 2023-24 presents the most prevalent non-English languages spoken by Multilingual Learners across Connecticut districts in the 2023-24 school year. Spanish is the most spoken language. While Fig. 2a uses data from the 2024-25 school year, the most recent available data for Connecticut districts report the 2023-24 school year. Though the number of individuals may vary in the 2024-2025 school year, the ranking of prevalence is unlikely to change.
Fig. 2b: Top Languages Spoken by MLs in MA Districts, 2024-25. This bar chart presents the most prevalent non-English languages spoken by Multilingual Learners across Massachusetts districts in the 2024-25 school year. Spanish is the most spoken language.
The data on languages spoken by MLs demonstrates that the resources needed to support Multilingual Learners are not identical between the two states. The differing percentages of MLs, in concert with the difference in primary languages spoken by MLs, signify the varying levels of need to support linguistic programs. Thus, it is worth questioning whether per-pupil spending accounts for the additional support required not only as the percentage of MLs within a district increases, but also taking into account their lingual backgrounds.
Fig. 2c: Population of Shared Top Languages of MLs in CT and MA. This visualization highlights the shared top languages that are spoken in CT and MA: Spanish, Portuguese, and Haitian Creole, using data from Fig. 2a and Fig. 2b. Connecticut has a higher percentage of Spanish-speaking MLs, whereas Massachusetts has a greater distribution of MLs who speak languages other than Spanish.
There seems to be a disparity between CT and MA per pupil expenditure (PPE) in public districts, which puts CT as the higher spender. However, in relation to the percentage of Multilingual Learners, Connecticut appears to actually be spending less on those with higher ML populations.
In figures 3a and 3b, we used scatter plots to show the relationship between per pupil expenditure (PPE) and percent of MLs in each district. Each data point represents a school district and how PPE and ML compare for that district. Using a trendline to make a better comparison, the plots show that there is a weak correlation between these variables. We see in Fig. 3a that most of Connecticut’s highly ML-populated districts have a PPE around $20,000, while many of the higher spending districts have less than 10% of Multilingual Learners. In contrast, Fig. 3b shows that for Massachusetts there are more districts that have a PPE of over $20,000 with over 10% of Multilingual Learners.
Additionally, a correlation coefficient (r) was used to measure the linear relationship between these two variables to more precisely define the relations that are visible in the figures below. The closer r is to -1 or 1 means the stronger negative or positive relationship there is between the data, respectively. The closer to 0 the coefficient is, the weaker the correlation. As seen below, Connecticut’s trendline shows a weak negative correlation between PPE and percentage MLs, whereas Massachusetts shows a weak positive correlation.
Fig. 3a. This scatter plot shows a weak negative correlation (r = -0.268) between the Connecticut districts’ PPE and MLs. This signifies that districts with a higher percentage of MLs also have some of the lower PPE.
Fig. 3b: For Massachusetts, the slope of the trendline is upwards and has a correlation coefficient (r) of 0.039. This correlation coefficient represents a weak positive correlation between PPE and the percentage of MLs.
Most data comes from Massachusetts’ official Department of Elementary and Secondary Education (DESE) website and Connecticut’s official state website CT.gov. Because of the data currently available, we are left with a few uncertainties in our data.
Firstly, the data of popular non-English languages spoken in Connecticut is from the 2023-24 school year, while the Massachusetts data is more recent (2024-25).
Similarly, the Connecticut data in Fig. 3b, the per pupil expenditure and percent of multilingual learners, is from the 2023-2024 fiscal year, while Massachusetts’ data is from the recent 2024-2025 fiscal year. Additionally, the data of enrolled MLs in Connecticut contains suppressed populations, leaving some numbers unknown. This means that some districts have been excluded from the data shown, which interferes with the direct comparison between CT and MA funding.
To determine if there’s a correlation between PPE and the percentage of MLs, we calculated the Pearson correlation coefficient (r) in Google Sheets. To do so, we removed the unknown and suppressed PPE data points for each state, causing some districts to be excluded from this calculation, as they were excluded in the charts. The correlation coefficient measures the linear relationship between the two variables to more precisely define the relations that are visible in the charts.
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