In the fall of 2020, around 10.3 percent of U.S. public school students were English Language Learner (ELL) students. This figure is an increase from 8.1 percent of U.S. public school students in 2000.

In fall 2020, there were about 4.96 million English Language Learner (ELL) students enrolled in public elementary and secondary schools across the United States. A ranking of the most spoken languages across the world can be accessed here.

In 2023, the United Kingdom had the most students learning English as a foreign language. There were approximately ******* students who were learning English as a second language that year, followed by Australia with almost ******* foreign students. The third place ranking was completed by the U.S., with around ******* students learning English as a foreign language. The global English learning market Learning English has become increasingly important for young people, especially in terms of international communication, increasing employability at multinational firms or securing work abroad, and specializing in fields as a global expert. The United Kingdom had both the highest number of students learning English as a foreign language and the shared highest distribution of English language learning revenue. In the past few years, the English language learning market has experienced a decrease in revenue, perhaps partially due to more free online platforms becoming accessible, as well as more companies and schools implementing English learning systems. 2022, however, has shown signs of improvement in terms of revenue. In 2020, revenues generated by the English learning market were dramatically lower due to travel restrictions implemented to limit the spread of COVID-19, as this trend continued into 2021. The global language services industry Over the course of the past two decades, the need for bilingual and multilingual skills has increased substantially. In an increasingly globalized world that is becoming more and more connected over time, people have been motivated to learn additional languages to suit both personal and professional demands. The market size of the global language services industry has grown to reflect this, with expected revenues of around ** billion U.S. dollars projected for 2022.

Report on Demographic Data in New York City Public Schools, 2020-21Enrollment counts are based on the November 13 Audited Register for 2020. Categories with total enrollment values of zero were omitted. Pre-K data includes students in 3-K. Data on students with disabilities, English language learners, and student poverty status are as of March 19, 2021. Due to missing demographic information in rare cases and suppression rules, demographic categories do not always add up to total enrollment and/or citywide totals. NYC DOE "Eligible for free or reduced-price lunch” counts are based on the number of students with families who have qualified for free or reduced-price lunch or are eligible for Human Resources Administration (HRA) benefits. English Language Arts and Math state assessment results for students in grade 9 are not available for inclusion in this report, as the spring 2020 exams did not take place. Spring 2021 ELA and Math test results are not included in this report for K-8 students in 2020-21. Due to the COVID-19 pandemic’s complete transformation of New York City’s school system during the 2020-21 school year, and in accordance with New York State guidance, the 2021 ELA and Math assessments were optional for students to take. As a result, 21.6% of students in grades 3-8 took the English assessment in 2021 and 20.5% of students in grades 3-8 took the Math assessment. These participation rates are not representative of New York City students and schools and are not comparable to prior years, so results are not included in this report. Dual Language enrollment includes English Language Learners and non-English Language Learners. Dual Language data are based on data from STARS; as a result, school participation and student enrollment in Dual Language programs may differ from the data in this report. STARS course scheduling and grade management software applications provide a dynamic internal data system for school use; while standard course codes exist, data are not always consistent from school to school. This report does not include enrollment at District 75 & 79 programs. Students enrolled at Young Adult Borough Centers are represented in the 9-12 District data but not the 9-12 School data. “Prior Year” data included in Comparison tabs refers to data from 2019-20. “Year-to-Year Change” data included in Comparison tabs indicates whether the demographics of a school or special program have grown more or less similar to its district or attendance zone (or school, for special programs) since 2019-20. Year-to-year changes must have been at least 1 percentage point to qualify as “More Similar” or “Less Similar”; changes less than 1 percentage point are categorized as “No Change”. The admissions method tab contains information on the admissions methods used for elementary, middle, and high school programs during the Fall 2020 admissions process. Fall 2020 selection criteria are included for all programs with academic screens, including middle and high school programs. Selection criteria data is based on school-reported information. Fall 2020 Diversity in Admissions priorities is included for applicable middle and high school programs. Note that the data on each school’s demographics and performance includes all students of the given subgroup who were enrolled in the school on November 13, 2020. Some of these students may not have been admitted under the admissions method(s) shown, as some students may have enrolled in the school outside the centralized admissions process (via waitlist, over-the-counter, or transfer), and schools may have changed admissions methods over the past few years. Admissions methods are only reported for grades K-12. "3K and Pre-Kindergarten data are reported at the site level. See below for definitions of site types included in this report. Additionally, please note that this report excludes all students at District 75 sites, reflecting slightly lower enrollment than our total of 60,265 students

This dataset includes the attendance rate for public school students PK-12 by student group and by district during the 2020-2021 school year. Student groups include: Students experiencing homelessness Students with disabilities Students who qualify for free/reduced lunch English learners All high needs students Non-high needs students Students by race/ethnicity (Hispanic/Latino of any race, Black or African American, White, All other races) Attendance rates are provided for each student group by district and for the state. Students who are considered high needs include students who are English language learners, who receive special education, or who qualify for free and reduced lunch. When no attendance data is displayed in a cell, data have been suppressed to safeguard student confidentiality, or to ensure that statistics based on a very small sample size are not interpreted as equally representative as those based on a sufficiently larger sample size. For more information on CSDE data suppression policies, please visit http://edsight.ct.gov/relatedreports/BDCRE%20Data%20Suppression%20Rules.pdf.

This dataset includes the attendance rate for public school students PK-12 by district during the 2020-2021 school year.

Attendance rates are provided for each district for the overall student population and for the high needs student population. Students who are considered high needs include students who are English language learners, who receive special education, or who qualify for free and reduced lunch.

When no attendance data is displayed in a cell, data have been suppressed to safeguard student confidentiality, or to ensure that statistics based on a very small sample size are not interpreted as equally representative as those based on a sufficiently larger sample size. For more information on CSDE data suppression policies, please visit http://edsight.ct.gov/relatedreports/BDCRE%20Data%20Suppression%20Rules.pdf.

A student is chronically absent if he/she misses ten percent or greater of the total number of days enrolled in the school year for any reason. Pre-Kindergarten students are excluded from this calculation. Connecticut State Department of Education collects annual data for grades K through 12, based on June reports of attendance. Charter Districts have been entered as individual districts. or the 2019-20 school year, chronic absenteeism calculations are based only on in-person school days until mid-March 2020. Geography

Overall attendance data include students in Districts 1-32 and 75 (Special Education). Students in District 79 (Alternative Schools & Programs), charter schools, home schooling, and home and hospital instruction are excluded. Pre-K data do not include NYC Early Education Centers or District Pre-K Centers; therefore, Pre-K data are limited to those who attend K-12 schools that offer Pre-K. Transfer schools are included in citywide, borough, and district counts but removed from school-level files. Attendance is attributed to the school the student attended at the time. If a student attends multiple schools in a school year, the student will contribute data towards multiple schools. Starting in 2020-21, the NYC DOE transitioned to NYSED's definition of chronic absenteeism. Students are considered chronically absent if they have an attendance of 90 percent or less (i.e. students who are absent 10 percent or more of the total days). In order to be included in chronic absenteeism calculations, students must be enrolled for at least 10 days (regardless of whether present or absent) and must have been present for at least 1 day. The NYSED chronic absenteeism definition is applied to all prior years in the report. School-level chronic absenteeism data reflect chronic absenteeism at a particular school. In order to eliminate double-counting students in chronic absenteeism counts, calculations at the district, borough, and citywide levels include all attendance data that contribute to the given geographic category. For example, if a student was chronically absent at one school but not at another, the student would only be counted once in the citywide calculation. For this reason, chronic absenteeism counts will not align across files. All demographic data are based on a student's most recent record in a given year. Students With Disabilities (SWD) data do not include Pre-K students since Pre-K students are screened for IEPs only at the parents' request. English language learner (ELL) data do not include Pre-K students since the New York State Education Department only begins administering assessments to be identified as an ELL in Kindergarten. Only grades PK-12 are shown, but calculations for "All Grades" also include students missing a grade level, so PK-12 may not add up to "All Grades". Data include students missing a gender, but are not shown due to small cell counts. Data for Asian students include Native Hawaiian or Other Pacific Islanders . Multi-racial and Native American students, as well as students missing ethnicity/race data are included in the "Other" ethnicity category. In order to comply with the Family Educational Rights and Privacy Act (FERPA) regulations on public reporting of education outcomes, rows with five or fewer students are suppressed, and have been replaced with an "s". Using total days of attendance as a proxy , rows with 900 or fewer total days are suppressed. In addition, other rows have been replaced with an "s" when they could reveal, through addition or subtraction, the underlying numbers that have been redacted. Chronic absenteeism values are suppressed, regardless of total days, if the number of students who contribute at least 20 days is five or fewer. Due to the COVID-19 pandemic and resulting shift to remote learning in March 2020, 2019-20 attendance data was only available for September 2019 through March 13, 2020. Interactions data from the spring of 2020 are reported on a separate tab. Interactions were reported by schools during remote learning, from April 6 2020 through June 26 2020 (a total of 57 instructional days, excluding special professional development days of June 4 and June 9). Schools were required to indicate any student from their roster that did not have an interaction on a given day. Schools were able to define interactions in a way that made sense for their students and families. Definitions of an interaction included: • Student submission of an assignment or completion of an

This dataset includes the attendance rate for public school students PK-12 by town during the 2020-2021 school year. Attendance rates are provided for each town for the overall student population and for the high needs student population. Students who are considered high needs include students who are English language learners, who receive special education, or who qualify for free and reduced lunch. When no attendance data is displayed in a cell, data have been suppressed to safeguard student confidentiality, or to ensure that statistics based on a very small sample size are not interpreted as equally representative as those based on a sufficiently larger sample size. For more information on CSDE data suppression policies, please visit http://edsight.ct.gov/relatedreports/BDCRE%20Data%20Suppression%20Rules.pdf.

In 2023, the English language learning market generated approximately *** billion U.S. dollars in revenue worldwide and almost reached pre-pandemic figures. In 2020, due to the COVID-19 pandemic and travel restrictions, this industry suffered greatly, losing over ** percent in revenues with this trend continuing into 2021.

Enrolments in regular second language programs (or core language programs), French immersion programs, and education programs in the minority official language offered in public elementary and secondary schools, by type of program, grade and sex.

Analysis of ‘School Attendance by Town, 2020-2021’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/c9fb7867-f130-4bde-a8be-1080fece72ab on 26 January 2022.

--- Dataset description provided by original source is as follows ---

This dataset includes the attendance rate for public school students PK-12 by town during the 2020-2021 school year.

Attendance rates are provided for each town for the overall student population and for the high needs student population. Students who are considered high needs include students who are English language learners, who receive special education, or who qualify for free and reduced lunch.

When no attendance data is displayed in a cell, data have been suppressed to safeguard student confidentiality, or to ensure that statistics based on a very small sample size are not interpreted as equally representative as those based on a sufficiently larger sample size. For more information on CSDE data suppression policies, please visit http://edsight.ct.gov/relatedreports/BDCRE%20Data%20Suppression%20Rules.pdf.

--- Original source retains full ownership of the source dataset ---

The computer assisted language Learning reseach & publication dataset, which was indexed by Scopus from 1983 to 2020. The dataset contains data authors, authors ID Scopus, title, year, source title, volume, issue, article number in Scopus, DOI, link, affiliation, abstract, index keywords, references, Correspondence Address, editors, publisher, conference name, conference date, conference code, ISSN, language, document type, access type, and EID.

The six data sets were created for an undergraduate course at the Babes-Bolyai University, Faculty of Mathematics and Computer Science, held for second year students in the autumn semester. The course is taught both in Romanian and English with the same content and evaluation rules in both languages. The six data sets are the following: - FirstCaseStudy_RO_traditional_2019-2020.txt - contains data about the grades from the 2019-2020 academic year (when traditional face-to-face teaching method was used) for the Romanian language - FirstCaseStudy_RO_online_2020-2021.txt - contains data about the grades from the 2020-2021 academic year (when online teaching was used) for the Romanian language - SecondCaseStudy_EN_traditional_2019-2020.txt - contains data about the grades from the 2019-2020 academic year (when traditional face-to-face teaching method was used) for the English language - SecondCaseStudy_EN_online_2020-2021.txt - contains data about the grades from the 2020-2021 academic year (when online teaching was used) for the English language - ThirdCaseStudy_Both_traditional_2019-2020.txt - the concatenation of the two data sets for the 2019-2020 academic year (so all instances from FirstCaseStudy_RO_traditional_2019-2020 and SecondCaseStudy_EN_traditional_2019-2020 together) - ThirdCaseStudy_Both_online_2020-2021.txt - the concatenation of the two data sets for the 2020-2021 academic year (so all instances from FirstCaseStudy_RO_online_2020-2021 and SecondCaseStudy_EN_online_2020-2021 together)Instances from the data sets for the 2019-2020 academic year contain 12 attributes (in this order): - the grades received by the student for 7 laboratory assignments that were presented during the semester. For assignments that were not turned in a grade of 0 was given. Possible values are between 0 and 10 - the grades received by the student for 2 practical exams. If a student did not participate in a practical exam, de grade was 0. Possible values are between 0 and 10. - the number of seminar activities that the student had. Possible values are between 0 and 7. - the final grade the student received for the course. It is a value between 4 and 10. - the category of the final grade: - E for grades 10 or 9 - G for grades 8 or 7 - S for grades 6 or 5 - F for grade 4Instances from the data sets for the 2020-2021 academic year contain 10 attributes (in this order): - the grades received by the student for 7 laboratory assignments that were presented during the semester. For assignments that were not turned in a grade of 0 was given. Possible values are between 0 and 10 - a seminar bonus computed based on the number of seminar activities the student had during the semester, which was added to the final grade. Possible values are between 0 and 0.5. - the final grade the student received for the course. It is a value between 4 and 10. - the category of the final grade: - E for grades 10 or 9 - G for grades 8 or 7 - S for grades 6 or 5 - F for grade 4

Explore the progression of average salaries for graduates in Teaching English As A Second Language (Emphasis On Digital Media & Learning) from 2020 to 2023 through this detailed chart. It compares these figures against the national average for all graduates, offering a comprehensive look at the earning potential of Teaching English As A Second Language (Emphasis On Digital Media & Learning) relative to other fields. This data is essential for students assessing the return on investment of their education in Teaching English As A Second Language (Emphasis On Digital Media & Learning), providing a clear picture of financial prospects post-graduation.

This statistic depicts the market size of the global digital language learning industry in 2015 and projected values for 2020 and 2025, broken down by language. In 2025, it is predicted that digital English language learning will have a global market size of *** billion euros.

This statistic depicts the market size of the global language learning industry in 2015 and a projected value for 2020, broken down by segment. In 2020, it is predicted that the offline English language learning segment will have a global market size of **** billion U.S. dollars.

Education has faced unprecedented disruption during the COVID pandemic. Understanding how students have adapted as we have entered a different phase of the pandemic and some communities have returned to more typical schooling will inform a suite of policy interventions and subsequent research. We use data from an oral reading fluency assessment---a rapid assessment taking only a few minutes that measures a fundamental reading skill---to examine COVID’s effects on children’s reading ability during the pandemic. We find that students in the first 200 days of the 2020--2021 school year tended to experience slower growth in ORF relative to pre-pandemic years. We also observed slower growth in districts with a high percentage of English language learners (ELLs) and/or students eligible for free and reduced-price lunch (FRL). These findings offer valuable insight into the effect of COVID on one of the most fundamental skills taught to children.

Full-time equivalent enrolments of English as an Additional Language or Dialect (EALD) students by school type, collected from 2012 as part of the annual enrolment data collection in Term 3. EALD general support students - these students have a non-English speaking background and learn Standard Australian English to a level required at their respective year level of schooling. This applies to students who are permanent residents and most categories of temporary residents. It includes Aboriginal or Torres Strait Islander students who speak an Aboriginal or Torres Strait Islander language, including Aboriginal English. Note: Due to Covid 19 restrictions, the English as an Additional Language data was not collected in 2020 and 2022. The 2019 data is used for 2020, the 2021 data is used for 2022 for reporting purpose.

The majority of pre-university students enrolled in the academic year 2022/2023 in Romania were studying French as their second foreign language - **** million students, followed by German and English.