ABSTRACT English language teaching is an important means of constructing social identities, and among those identities we have social class identity (BLOCK, 2013). However, the small number of studies in this regard has failed to draw attention to its importance. In this article, we discuss why the issue of social class identity should become an important aspect to be considered and investigated in the language teaching-learning, especially hereafter in relation to the English language. We also seek, through the Critical Discourse Analysis (FAIRCLOUGH, 2003; VAN DIJK, 2015), present a part of a research that investigates how the English language coursebook, an authority instrument in the classroom (TILIO, 2010; CORACINI , 1999), constructs social class identities and the meanings of these identities. The results reinforce the premise that English language teaching is largely a locus for social class identities, contributing to the maintenance of exclusions and inequalities.

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 English Housing Survey, 2018-2019: Household Data Teaching Dataset is based on the English Housing Survey, 2018-2019: Household Data (held under SN8669) and constitutes real data which are used by the Department for Communities and Local Government and are behind many headlines. The teaching dataset is a subset which has been subjected to certain simplifications for the purpose of learning and teaching.

The main differences are:

• the number of variables has been reduced
• weighting has been simplified
• a reduced codebook is provided

This lecture note covers memos, emails, and brief reports as learning tools to help students finish English courses. The table below is a part of the semester learning plan for English courses in odd semesters for the Information systems study programme. Lectures on memos, emails, and brief reports are more comprehensively constructed to fulfil the goals. Thus, Indonesian lecture notes help students understand issues like memos, emails, and brief reports

Abstract This study focuses on the use of oral tasks to develop students’ willingness to communicate (WTC) under a Project Based Learning (PBL) approach. The participants were thirty-one 10th graders from a private school in Chile. The data of this research was collected through audio-recorded lessons and a students’ perception rating scale. The findings show that whenever the students were exposed to specific oral tasks, they used English more frequently to ask for information, as opposed to their L1, Spanish. The students also inclined to give more information in English when answering yes/no questions, as opposed to wh- questions. Moreover, the students used their L1 very few times; however, Spanish was still used when the teacher was not monitoring the tasks. The study also revealed that the students perceived they used English in different frequencies for different language functions.

This article delves deeply into effective methodologies for rapidly teaching English to older children, combining traditional pedagogical strategies with modern technology to create a more comprehensive approach. With communicative and collaborative methods, as well as advanced technological tools like mobile applications and Virtual Reality (VR), language learning becomes both accessible and engaging. These tools not only aid students in gaining language skills faster but also improve their motivation and retention.

The English Housing Survey 2008-2009 Household Data Teaching Dataset is based on the English Housing Survey, 2008-2009: Household Data (available from the UK Data Archive under SN 6613) and constitutes real data which are used by the Department for Communities and Local Government and are behind many headlines. The teaching dataset is a subset which has been subjected to certain simplifications and additions for the purpose of learning and teaching.

The main differences are:

• the number of variables has been reduced
• weighting has been simplified
• a reduced codebook is provided

Data set here is for the quantitative part only.

About this dataset

Context

The dataset tabulates the English population by year. The dataset can be utilized to understand the population trend of English.

Content

The dataset constitues the following datasets

• English, IN Population Dataset: Yearly Figures, Population Change, and Percent Change Analysis

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

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The English Housing Survey, 2018-2019: Household Data Teaching Dataset is based on the English Housing Survey, 2018-2019: Household Data (held under SN8669) and constitutes real data which are used by the Department for Communities and Local Government and are behind many headlines. The teaching dataset is a subset which has been subjected to certain simplifications for the purpose of learning and teaching. The main differences are:

the number of variables has been reduced weighting has been simplified a reduced codebook is provided

Further information is available in the study documentation which includes a dataset user guide. Information about other teaching resources and datasets can be found on the UK Data Service teaching resources webpages.

The main topics covered are:

housing characteristics household characteristics

This article analyzes the importance of using gender-sensitive texts in teaching English language communication skills. The study examines the role of gender-sensitive texts in reducing gender stereotypes and promoting equality. The results of the study conducted in an experimental classroom showed that gender-sensitive texts improved students' speaking, listening, reading, and writing skills, as well as positively changing their views on gender equality. The article discusses the importance of implementing gender-sensitive approaches in the educational process and future research directions.

Data of the paper Descriptive Analysis of the Relationship between University Teachers’ Understanding of English Education Policy and College Students’ Learning English

The English Housing Survey 2012-2013 Household Data Teaching Dataset is based on the English Housing Survey, 2012-2013: Household Data (held under SN 7512) and constitutes real data which are used by the Department for Communities and Local Government and are behind many headlines. The teaching dataset is a subset which has been subjected to certain simplifications and additions for the purpose of learning and teaching.

The main differences are:the number of variables has been reducedweighting has been simplifieda reduced codebook is providedFurther information is available in the study documentation which includes a dataset user guide. Information about other teaching resources and datasets can be found on the Teaching resources webpage.

The main topics covered are:housing characteristicshousehold characteristicssatisfaction with the home and local area

The English Housing Survey 2008-2009 Household Data Teaching Dataset is based on the English Housing Survey, 2008-2009: Household Data (available from the UK Data Archive under SN 6613) and constitutes real data which are used by the Department for Communities and Local Government and are behind many headlines. The teaching dataset is a subset which has been subjected to certain simplifications and additions for the purpose of learning and teaching.

The main differences are:the number of variables has been reducedweighting has been simplifieda reduced codebook is providedFurther information is available in the study documentation (below) which includes a dataset user guide. Information about other teaching resources can be found on the Teaching resources webpage.

The main topics covered are:housing characteristicshousehold characteristicssatisfaction with the home and local area

Understanding Society, the UK Household Longitudinal Study, is a longitudinal survey of the members of approximately 40,000 households (at Wave 1) in the United Kingdom. The overall purpose of Understanding Society is to provide high quality longitudinal data about subjects such as health, work, education, income, family, and social life to help understand the long term effects of social and economic change, as well as policy interventions designed to impact upon the general well-being of the UK population. The Understanding Society main survey sample consists of a large General Population Sample plus three other components: the Ethnic Minority Boost Sample, the former British Household Panel Survey sample and the Immigrant and Ethnic Minority Boost Sample.

Derived from 150+ years of lexical research, these comprehensive English datasets, focused on American English, offer linguistically annotated data including headwords, definitions, senses, examples, POS tags, and semantic metadata. Ideal for dictionary tools, NLP, and AI applications.

One of our flagship datasets, the American English data, is expertly curated and linguistically annotated by professionals, with annual updates to ensure accuracy and relevance. The below datasets in American English are available for license:

1. American English Monolingual Dictionary Data
2. American English Synonyms and Antonyms Data
3. American English Pronunciations with Audio

Key Features (approximate numbers):

1. American English Monolingual Dictionary Data

Our American English Monolingual Dictionary Data is the foremost authority on American English, including detailed tagging and labelling covering parts of speech (POS), grammar, region, register, and subject, providing rich linguistic information. Additionally, all grammar and usage information is present to ensure relevance and accuracy.

• Words: 140,000
• Senses: 222,000
• Examples sentences: 140,000
• Format: XML and JSON formats
• Delivery: Email (link-based file sharing) and REST API
• Updated frequency: annually

1. American English Synonyms and Antonyms Data

The American English Synonyms and Antonyms Dataset is a leading resource offering comprehensive and up-to-date coverage of word relationships in contemporary American English. It includes rich linguistic detail such as precise definitions and part-of-speech (POS) tags, making it an essential asset for developing AI systems and language technologies that require deep semantic understanding.

• Synonyms: 600,000
• Antonyms: 22,000
• Format: XML and JSON formats
• Delivery: Email (link-based file sharing) and REST API
• Updated frequency: annually

1. American English Pronunciations with Audio (word-level)

This dataset provides IPA transcriptions and mapped audio files for words in contemporary American English, with a focus on US speaker usage. It includes syllabified transcriptions, variant spellings, part-of-speech tags, and pronunciation group identifiers. Audio files are supplied separately and linked where available – ideal for TTS, ASR, and pronunciation modeling.

• Transcriptions (IPA): 250,000
• Audio files: 180,000
• Format: XLSX (for transcriptions), MP3 and WAV (audio files)
• Updated frequency: annually

Use Cases:

We consistently work with our clients on new use cases as language technology continues to evolve. These include Natural Language Processing (NLP) applications, TTS, dictionary display tools, games, translations, word embedding, and word sense disambiguation (WSD).

If you have a specific use case in mind that isn't listed here, we’d be happy to explore it with you. Don’t hesitate to get in touch with us at Growth.OL@oup.com to start the conversation.

Pricing:

Oxford Languages offers flexible pricing based on use case and delivery format. Our datasets are licensed via term-based IP agreements and tiered pricing for API-delivered data. Whether you’re integrating into a product, training an LLM, or building custom NLP solutions, we tailor licensing to your specific needs.

Contact our team or email us at Growth.OL@oup.com to explore pricing options and discover how our language data can support your goals.

About the sample:

The samples offer a brief overview of one or two language datasets (monolingual or/and bilingual dictionary data). To help you explore the structure and features of our dataset, we provide a sample in CSV format for preview purposes only.

If you need the complete original sample or more details about any dataset, please contact us (Growth.OL@oup.com) to request access or further information.

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Europe English Language Teaching (ELT) Market Size 2025-2029

The europe english language teaching (elt) market size is forecast to increase by USD 8.47 billion at a CAGR of 13% between 2024 and 2029.

Market Size & Forecast

Market Opportunities: USD 84.23 billion
Market Future Opportunities: USD USD 8.47 billion 
CAGR : 13%

Market Summary

The market is witnessing significant advancements, with a growing emphasis on innovative teaching methods and technology integration. According to recent studies, the European ELT market is projected to reach a value of €23.3 billion by 2025, marking a substantial increase from its current size. This growth can be attributed to the rising demand for English language proficiency in various sectors, including business, education, and tourism. Moreover, there is a noticeable trend towards the adoption of game-based learning in ELT, with this approach expected to account for over 30% of the market share by 2025. However, the implementation of these technologies comes with considerable setup costs for educational institutes, necessitating strategic planning and investment. Despite these challenges, the European ELT market continues to evolve, offering numerous opportunities for stakeholders and driving advancements in language education.

What will be the size of the Europe English Language Teaching (ELT) Market during the forecast period?

Explore in-depth regional segment analysis with market size data with forecasts 2025-2029 - in the full report.
Request Free Sample The European English Language Teaching (ELT) market exhibits a steady expansion, with current market participation exceeding 30% of the European education sector. This growth is driven by the increasing demand for effective communication skills in a globalized business environment. Looking ahead, future expectations indicate a continuous upward trend, with market expansion projected to surpass 15% annually. Notably, the integration of technology in language learning has significantly influenced the ELT market. Learning Management Systems (LMS), online language courses, and language learning software have become essential tools for educators and learners alike. The adoption of personalized learning pathways, learning analytics, and effective feedback mechanisms has led to increased student engagement and improved language skills development. In comparison, the use of traditional language learning methodologies, such as classroom management techniques and teacher professional development, still holds importance but is increasingly being supplemented by technology-driven approaches. This juxtaposition highlights the evolving nature of the ELT market, as it adapts to meet the changing needs of learners and businesses in Europe.

How is this Europe English Language Teaching (ELT) Market segmented?

The europe english language teaching (elt) market market research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029,for the following segments. End-userInstitutional learnersIndividual learnersLearning MethodOnline learningBlended learningClassroom learningApplicationFor kids and teensFor adultsFor businessesFor educational and testsMaterial TypeTextbooksDigital ContentGeographyEuropeFranceGermanyItalySpain

By End-user Insights

The institutional learners segment is estimated to witness significant growth during the forecast period.

The European English Language Teaching (ELT) market caters to the institutional learners segment, comprising students and candidates enrolled in full-time language training courses at universities, schools, and language-specific institutes, such as the British Council. These learners engage in various learning modes, including e-learning, face-to-face classroom sessions, and traditional practices. Currently, task-based language learning and communicative language teaching are popular strategies, enabling learners to acquire vocabulary and grammar through real-life situations. Mobile-assisted language learning and interactive whiteboards are increasingly integrated into ELT curricula, fostering a more interactive and engaging learning experience. Moreover, teacher training programs focus on differentiated instruction strategies, ensuring personalized learning experiences. Formative assessment techniques and assessment rubrics are employed to provide continuous feedback and evaluate learners' progress. Pronunciation teaching techniques and digital literacy skills development are also integral components of ELT. Looking ahead, the ELT market anticipates a significant expansion, with language learning apps and educational technology platforms gaining traction. Augmented reality applications and blended learning models are expected to revolutionize language instruction, offering immersive, interactive