Turkey was the European country which had the largest share of its citizens self-reporting that they could not speak any foreign language, with almost 82 percent saying they were unable to do so. The United Kingdom and Bosnia and Herzegovina also had over 60 percent of their citizens self-reporting not being able to speak a foreign language. On the other hand, Slovenia, Sweden, and Estonia all had less than five percent of their populations stating they could not speak another language other than their country's main language. Slovenia, Luxembourg, and Norway were the three countries with the most citizens stating they could speak three foreign languages. On average, 37 percent of EU citizens report speaking one foreign language, 22 percent speak two, and 8.6 percent speak three, while 32 percent report speaking none.

Ireland stands as an outlier, as all citizens of the north-western European country self-reported as speaking a foreign language. This is, however, actually, a result of how the question was asked, as respondents interpreted English as being a foreign language (in the sense of not being native to Ireland), in spite of it being one of the two official languages in the country (alongside Irish) and being spoken by the vast majority of the population in Ireland as their first language.

As of 2021, English was by far the most studied foreign language at the upper secondary level in European Union countries, with 88.3 percent of all students studying the language. The number of English-learning students was greater than all other languages combined in 2021, with German, French, and Spanish being the next most studied languages in European secondary schools. Russian was the most-learned non-EU language in that year, with 2.7 percent of students learning the eastern Slavic language.

English is the most learned language by lower secondary level students in European Union countries as of 2022, with 98.1 percent of students learning the language. French, German, and Spanish are the next most learned languages, with almost a third of students learning French at lower secondary level. Russian was the non-EU language which had the most learners at this education level, with 2.2 percent of students.

This statistic presents the leading European countries by their level of English proficiency as of March 2019. According to data provided by Klazz, Sweden had the highest percentage of people who were proficient in English at 71 percent of the population.

As of 2021, English was the foreign language which the greatest share of primary school students in EU countries were learning, with 84.2 percent of primary education students learning the language in that year. French was the second most learned foreign language by primary school students in 2021, with 5.3 percent of students, while German was learned by 3.5 percent of schoolchildren.

This layer shows language group of language spoken at home by age. This is shown by county boundaries. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. This layer is symbolized to show the percentage of the population age 5+ who speak another Indo-European language at home.

Turkey is the European country with the most respondents stating that they cannot speak a foreign language, with over 93 percent of Turks with only primary or lower secondary education saying they have no foreign language skills. The United Kingdom stands out as the country which has the highest share of people with a tertiary education stating they know no foreign language, with approximately 54 percent of Brits with a university education knowing no foreign language. In contrast, Norway, Slovenia, and Sweden have very few respondents of any educational background stating they know no foreign language, with 11.5 percent of Norwegians who have only primary or lower secondary level education knowing no foreign language. In every country surveyed, the share of people with no foreign language skills declines with increases in educational attainment.

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Online Language Learning Market Size 2025-2029

The online language learning market size is forecast to increase by USD 81.55 billion at a CAGR of 27.5% between 2024 and 2029.

The market is experiencing significant growth, driven by the cost savings and flexibility offered by digital solutions. With the increasing globalization and the need for cross-cultural communication, there is a surging demand for e-learning. Online language learning platforms provide an affordable and convenient alternative to traditional classroom settings, enabling learners to access high-quality instruction from anywhere in the world. 
However, this market is not without challenges. The proliferation of open-source language learning resources poses a threat to commercial offerings. These free resources offer basic language learning capabilities, making it essential for commercial platforms to differentiate themselves through advanced features and personalized learning experiences. Additionally, the integration of artificial intelligence (AI) in language learning is transforming the industry.
AI-powered platforms can provide personalized instruction, real-time feedback, and adaptive learning paths, enhancing the overall learning experience. Companies seeking to capitalize on market opportunities must stay abreast of these trends and effectively navigate the challenges to succeed in the dynamic the market.

What will be the Size of the Online Language Learning Market During the Forecast Period?

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Movies, virtual classrooms, and interactive exercises are just a few examples of the diverse range of resources available to learners. Flexible pricing structures cater to various budgets and learning styles, making it an attainable goal for many individuals and organizations. Overall, the market is poised for continued growth as it adapts to evolving user needs and technological advancements. Travelers and professionals benefit greatly from tablets and computers to learn a new language on-the-go. The future of online education lies In the integration of AI, big data, and digital technology to provide customized and effective learning solutions.

How is this Online Language Learning Industry segmented and which is the largest segment?

The report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

End-user

  Courses
  Solutions
  Apps


Language

  English
  Mandarin
  Spanish
  Others


Delivery Format

  Live Online Classes
  Self-Paced Online Courses
  Hybrid Learning


Target Learner

  School Students
  University Students
  Working Professionals
  Adults for Personal Development


End User Type

  Individual Learners
  Educational Institutions


Geography

  APAC

    China
    India
    Japan


  Europe

    Germany
    UK
    France
    Spain


  North America

    Canada
    US


  South America

    Brazil


  Middle East and Africa

By End-user Insights

The courses segment is estimated to witness significant growth during the forecast period. The markets cater to the growing demand for multilingual skills In the multinational corporate sector. E-learning platforms offer high-quality language courses, providing flexibility and accessibility to learners worldwide. The providers use artificial intelligence algorithms to personalize teachings, enhancing learner engagement and retention. Institutions and individual learners benefit from affordable pricing structures and technological advancements, such as wearable technologies, IoT, and cloud computing. Courses include multimedia resources, interactive exercises, and virtual classrooms, enabling cross-cultural communication. E-learning platforms offer self-learning apps, tutoring, and adaptive learning programs, catering to various age groups and language levels. Despite technological barriers, the tools provide learners with big data analysis, machine learning, and personalized learning modes.

English, Spanish, French, German, Chinese, Italian, Arabic, Korean, and other languages are available, ensuring Internet access is a prerequisite. The e-learning market continues to grow, democratizing education and promoting lifelong learning and skill development.

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The courses segment was valued at USD 7.83 billion in 2019 and showed a gradual increase during the forecast period.

Regional Analysis

APAC is estimated to contribute 44% to the growth of the global market during the forecast period. Technavio's analysts have elaborately explained the regional trends and drivers that shape the market during the forecast period.

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The market In the Asia-Pacific r

Speakers of foreign languages by ability to speak English by NUTS3. (Census 2022 Theme 2 Table 6 )Census 2022 table 2.6 is speakers of foreign languages by ability to speak English. Details include foreign language speakers by their ability to speak English. Census 2022 theme 2 is Migration, Ethnicity, Religion and Foreign Languages. The Nomenclature of Territorial Units for Statistics (NUTS) were created by Eurostat in order to define territorial units for the production of regional statistics across the European Union. In 2003 the NUTS classification was established within a legal framework (Regulation (EC) No 1059/2003).Changes made under the 2014 Local Government Act prompted a revision of the Irish NUTS 2 and NUTS 3 Regions. The main changes at NUTS 3 level were the transfer of South Tipperary from the South-East into the Mid-West NUTS 3 region and the movement of Louth from the Border to the Mid-East NUTS 3 Region. NUTS 3 Regions are grouped into three NUTS 2 Regions (Northern and Western, Southern, Eastern and Midland) which correspond to the Regional Assemblies established in the 2014 Local Government Act. The revisions made to the NUTS boundaries have been given legal status under Commission Regulation (EU) 2016/2066.Coordinate reference system: Irish Transverse Mercator (EPSG 2157). These boundaries are based on 20m generalised boundaries sourced from Tailte Éireann Open Data Portal. NUTS3 Regions 2015This dataset is provided by Tailte Éireann

As of February 2025, English was the most popular language for web content, with over 49.4 percent of websites using it. Spanish ranked second, with six percent of web content, while the content in the German language followed, with 5.6 percent. English as the leading online language United States and India, the countries with the most internet users after China, are also the world's biggest English-speaking markets. The internet user base in both countries combined, as of January 2023, was over a billion individuals. This has led to most of the online information being created in English. Consequently, even those who are not native speakers may use it for convenience. Global internet usage by regions As of October 2024, the number of internet users worldwide was 5.52 billion. In the same period, Northern Europe and North America were leading in terms of internet penetration rates worldwide, with around 97 percent of its populations accessing the internet.

SAP2011T2T5DC07 - Speakers of foreign languages by language spoken. Published by Central Statistics Office. Available under the license Creative Commons Attribution 4.0 (CC-BY-4.0).Speakers of foreign languages by language spoken...

The GlobalPhone corpus developed in collaboration with the Karlsruhe Institute of Technology (KIT) was designed to provide read speech data for the development and evaluation of large continuous speech recognition systems in the most widespread languages of the world, and to provide a uniform, multilingual speech and text database for language independent and language adaptive speech recognition as well as for language identification tasks.

The entire GlobalPhone corpus enables the acquisition of acoustic-phonetic knowledge of the following 22 spoken languages: Arabic (ELRA-S0192), Bulgarian (ELRA-S0319), Chinese-Mandarin (ELRA-S0193), Chinese-Shanghai (ELRA-S0194), Croatian (ELRA-S0195), Czech (ELRA-S0196), French (ELRA-S0197), German (ELRA-S0198), Hausa (ELRA-S0347), Japanese (ELRA-S0199), Korean (ELRA-S0200), Polish (ELRA-S0320), Portuguese (Brazilian) (ELRA-S0201), Russian (ELRA-S0202), Spanish (Latin America) (ELRA-S0203), Swahili (ELRA-S0375), Swedish (ELRA-S0204), Tamil (ELRA-S0205), Thai (ELRA-S0321), Turkish (ELRA-S0206), Ukrainian (ELRA-S0377), and Vietnamese (ELRA-S0322).

In each language about 100 sentences were read from each of the 100 speakers. The read texts were selected from national newspapers available via Internet to provide a large vocabulary. The read articles cover national and international political news as well as economic news. The speech is available in 16bit, 16kHz mono quality, recorded with a close-speaking microphone (Sennheiser 440-6). The transcriptions are internally validated and supplemented by special markers for spontaneous effects like stuttering, false starts, and non-verbal effects like laughing and hesitations. Speaker information like age, gender, occupation, etc. as well as information about the recording setup complement the database. The entire GlobalPhone corpus contains over 450 hours of speech spoken by more than 2100 native adult speakers.

Data is shortened by means of the shorten program written by Tony Robinson. Alternatively, the data could be delivered unshorten.

The Vietnamese part of GlobalPhone was collected in summer 2009. In total 160 speakers were recorded, 140 of them in the cities of Hanoi and Ho Chi Minh City in Vietnam, and an additional set of 20 speakers were recorded in Karlsruhe, Germany. All speakers are Vietnamese native speakers, covering the main dialectal variants from South and North Vietnam. Of these 160 speakers, 70 were female and 90 were male. The majority of speakers are well educated, being graduated students and engineers. The age distribution of the speakers ranges from 18 to 65 years. Each speaker read between 50 and 200 utterances from newspaper articles, corresponding to roughly 9.5 minutes of speech or 138 utterances per person, in total we recorded 22.112 utterances. The speech was recorded using a close-talking microphone Sennheiser HM420 in a push-to-talk scenario using an inhouse developed modern laptop-based data collection toolkit. All data were recorded at 16kHz and 16bit resolution in PCM format. The data collection took place in small-sized rooms with very low background noise. Information on recording place and environmental noise conditions are provided in a separate speaker session file for each speaker. The speech data was recorded in two phases. In a first phase data was collected from 140 speakers in the cities of Hanoi and Ho Chi Minh. In the second phase we selected utterances from the text corpus in order to cover rare Vietnamese phonemes. This second recording phase was carried out with 20 Vietnamese graduate students who live in Karlsruhe. In sum, 22.112 utterances were spoken, corresponding to 25.25 hours of speech. The text data used for recording mainly came from the news posted in online editions of 15 Vietnamese newspaper websites, where the first 12 were used for the training set, while the last three were used for the development and evaluation set. The text data collected from the first 12 websites cover almost 4 Million word tokens with a vocabulary of 30.000 words resulting in an Out-of-Vocabulary rate of 0% on the development set and 0.067% on the evaluation set. For the text selection we followed the standard GlobalPhone protocols and focused on national and international politics and economics news (see [SCHULTZ 2002]). The transcriptions are provided in Vietnamese-style Roman script, i.e. using several diacritics encoded in UTF-8. The Vietnamese data are organized in a training set of 140 speakers with 22.15 hours of speech, a development set of 10 speakers, 6 from North and 4 from South Vietnam with 1:40 hours of speech and an evaluation set of 10 speakers with same gender and dialect distribution as the development set with 1:30 hours of speech. More details on corpus statistics, collection scenario, and system building based on the Vietnamese part of GlobalPhone can be found under [Vu and Schultz, 2009, 2010].

[Schultz 2002] Tanja Schultz (2002): GlobalPhone: A Multilingual Speech and Text Database developed at Karlsruhe University, Proceedings of the International Conference of Spoken Language Processing, ICSLP 2002, Denver, CO, September 2002. [Vu and Schultz, 2010] Ngoc Thang Vu, Tanja Schultz (2010): Optimization On Vietnamese Large Vocabulary Speech Recognition, 2nd Workshop on Spoken Languages Technologies for Under-resourced Languages, SLTU 2010, Penang, Malaysia, May 2010. [Vu and Schultz, 2009] Ngoc Thang Vu, Tanja Schultz (2009): Vietnamese Large Vocabulary Continuous Speech Recognition, Automatic Speech Recognition and Understanding, ASRU 2009, Merano.

The joint UNESCO-OECD-Eurostat (UOE) data collection on formal education systems provides annual data on student participation and completion of educational programmes as well as data on personnel, cost and type of resources devoted to education. The reference period for non-monetary education data is the school year and for monetary data it is the calendar year. The International Statistics of Education and Training Systems ÔÇô UNESCO-UIS/OECD/Eurostat (UOE) Questionnaire aims to provide the data required by international bodies, in addition to offering results at the national level. It is a synthesis and analysis operation that appears in the National Statistical Plan 2021-2024 (Prog. 8677) and is carried out by the S.G. of Statistics and Studies of the Ministry of Education and Vocational Training in collaboration with the Ministry of Universities and the National Institute of Statistics. Its purpose is to integrate the statistical information of the activity of the educational-training system in its different levels of education in order to meet the demands of international statistics, of the same name, requested by Eurostat, OECD and UNESCO-UIS. A selection of tables with data derived from this statistic is provided below, together with a presentation summary note:

The joint UNESCO-OECD-Eurostat (UOE) data collection on formal education systems provides annual data on student participation and completion of educational programmes as well as data on personnel, cost and type of resources devoted to education. The reference period for non-monetary education data is the school year and for monetary data it is the calendar year. The International Statistics of Education and Training Systems ÔÇô UNESCO-UIS/OECD/Eurostat (UOE) Questionnaire aims to provide the data required by international bodies, in addition to offering results at the national level. It is a synthesis and analysis operation that appears in the National Statistical Plan 2021-2024 (Prog. 8677) and is carried out by the S.G. of Statistics and Studies of the Ministry of Education and Vocational Training in collaboration with the Ministry of Universities and the National Institute of Statistics. Its purpose is to integrate the statistical information of the activity of the educational-training system in its different levels of education in order to meet the demands of international statistics, of the same name, requested by Eurostat, OECD and UNESCO-UIS. A selection of tables with data derived from this statistic is provided below, together with a presentation summary note:

Speakers of foreign languages by ability to speak English

This statistic shows the trend of candidates who took a European language at Higher level and received a grade between A and C, from 2011 to 2018 as a percentage of the total number of learners for each year. In 2018, Italian was the most successful subject at 92 percent, followed by German with 89 percent.

Speakers of foreign languages by ability to speak English

This statistic shows trend of candidates who took a European language in the A level exam in Northern Ireland from the year 2011 to 2019 as a percentage of the total number of candidates for each year. In 2019, Spanish and French were the most popular languages. Spanish was proportionally much more popular as an A level subject in Northern Ireland than in England and in Wales.

In 2021, Duolingo was the most installed multilingual language learning app among users aged between 20 and 34 years in the examined European markets. In the United Kingdom, Duolingo accounted for 66 percent of installs, while in Turkey it gathered 52 percent of installs from users in the examined age group. Babbel was the second most-installed language app in Germany and had a 17 percent market share in Italy.

This statistic shows the trend of candidates taking a European language subject in the A level exams from the year 2011 to 2019 in Wales, as a percentage of the total number of candidates for each year. While entries for each year have declined, Welsh and French have seen the biggest drop in numbers. Overall, Welsh is still the most popular choice of European language in Wales.