In 2022, **** percent of higher education students in the United States were taking exclusively distance learning courses. A further **** percent of students were taking at least some distance learning courses. For both of these groups, this is a decrease from the previous year, demonstrating the declining impact of the COVID-19 pandemic.

English domiciled higher-level learners in England by distance learning flag, academic years 2018/19 to 2021/22. OfS-Recognised HE only.

In the fall of 2022, about *** million students were enrolled in at least one distance education course from a public postsecondary institution in the United States. This is compared to around ******* students who were enrolled in distance education courses from private, for profit institutions. The high enrollment level in distance education courses is due to the impact of the COVID-19 pandemic.

The dataset in Excel spreadsheet accompanying this article consists of 207 rows and 24 columns. Each row represents an individual responses to questionnaire's items.

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Distance Learning Market Size 2024-2028

The distance learning market size is forecast to increase by USD 149.23 billion at a CAGR of 9.65% between 2023 and 2028.

The growing demand for distance learning, fueled by the continuous development of technology, is a key driver of the distance learning market. As technology improves, online education becomes more accessible, engaging, and effective, allowing students to learn remotely with ease. The integration of advanced tools such as video conferencing, AI-driven assessments, and interactive content is further enhancing the appeal of distance learning.
In North America, the market is experiencing significant growth due to the integration of advanced technologies and shifting educational preferences. With a growing emphasis on flexible, personalized learning experiences, including self-paced e-learning, institutions are increasingly offering distance learning programs that cater to diverse student needs. This trend is expected to continue, contributing to the market's expansion in the region.

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

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The market is experiencing significant growth due to the increasing adoption of remote learning solutions among K-12 students and higher education students. Online assessments, video conferencing sessions, and virtual schools are becoming popular flexible education options for students who require flexibility in their learning schedules. Website-based mediums and application-based mediums, such as e-learning platforms, are increasingly being used to deliver educational programs. Internet access is essential for distance learning, making online learning platforms an indispensable tool for universities and colleges.

Market Segmentation

The market research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD Billion' for the period 2024-2028, as well as historical data from 2018 - 2022 for the following segments.

Type

  Traditional
  Online


Method

  Synchronous distance learning
  Asynchronous distance learning


Geography

  North America

    Canada
    US


  Europe

    Germany
    UK


  APAC

    China


  Middle East and Africa



  South America

By Type Insights

The traditional segment is estimated to witness significant growth during the forecast period. The market encompasses various methods and technologies, including gamification, personalized learning pathways, educational environments, and remote learning techniques. Traditional distance learning, characterized by asynchronous online courses, pre-recorded lecture books, and minimal instructor interaction, remains a significant revenue contributor. This approach caters to a broad audience, particularly those with limited access to digital devices or high-internet connectivity. Academic institutions and the government sector continue to offer traditional distance learning programs, such as those provided by the Open University in the UK via mail. However, corporate blended learning, online education solutions, and personalized learning solutions are gaining popularity due to their interactive and technologically advanced nature.

These methods include learning management systems, virtual classrooms, mobile e-learning platforms, and cloud-based e-Learning platforms. Moreover, the use of intranet connection, computers, tutorials, podcasts, recorded lectures, e-books, and machine learning technology enhances the learning experience. The market also serves academic users and corporate users through service providers and content providers. The increasing literacy rate, internet penetration, and the need for continuous skill upgrading further fuel the market's growth.

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The traditional segment accounted for USD 152.29 billion in 2018 and showed a gradual increase during the forecast period.

Regional Insights

North America is estimated to contribute 34% 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 North America is experiencing significant growth due to the integration of advanced technologies and shifting educational preferences. With the rise of gamification, personalized learning pathways, and educational environments, online education solutions have become increasingly popular. Academic institutions and the government sector are expanding their digital services, offering distance learning programs through Learning Management Systems and cloud-based e-Learning platforms. Remote learning methods, such as pre-recorded lectures, tutorials, podcas

The 2021-2022 School Learning Modalities dataset provides weekly estimates of school learning modality (including in-person, remote, or hybrid learning) for U.S. K-12 public and independent charter school districts for the 2021-2022 school year and the Fall 2022 semester, from August 2021 – December 2022. These data were modeled using multiple sources of input data (see below) to infer the most likely learning modality of a school district for a given week. These data should be considered district-level estimates and may not always reflect true learning modality, particularly for districts in which data are unavailable. If a district reports multiple modality types within the same week, the modality offered for the majority of those days is reflected in the weekly estimate. All school district metadata are sourced from the National Center for Educational Statistics (NCES) for 2020-2021. School learning modality types are defined as follows: In-Person: All schools within the district offer face-to-face instruction 5 days per week to all students at all available grade levels. Remote: Schools within the district do not offer face-to-face instruction; all learning is conducted online/remotely to all students at all available grade levels. Hybrid: Schools within the district offer a combination of in-person and remote learning; face-to-face instruction is offered less than 5 days per week, or only to a subset of students. Data Information School learning modality data provided here are model estimates using combined input data and are not guaranteed to be 100% accurate. This learning modality dataset was generated by combining data from four different sources: Burbio [1], MCH Strategic Data [2], the AEI/Return to Learn Tracker [3], and state dashboards [4-20]. These data were combined using a Hidden Markov model which infers the sequence of learning modalities (In-Person, Hybrid, or Remote) for each district that is most likely to produce the modalities reported by these sources. This model was trained using data from the 2020-2021 school year. Metadata describing the location, number of schools and number of students in each district comes from NCES [21]. You can read more about the model in the CDC MMWR: COVID-19–Related School Closures and Learning Modality Changes — United States, August 1–September 17, 2021. The metrics listed for each school learning modality reflect totals by district and the number of enrolled students per district for which data are available. School districts represented here exclude private schools and include the following NCES subtypes: Public school district that is NOT a component of a supervisory union Public school district that is a component of a supervisory union Independent charter district “BI” in the state column refers to school districts funded by the Bureau of Indian Education. Technical Notes Data from August 1, 2021 to June 24, 2022 correspond to the 2021-2022 school year. During this time frame, data from the AEI/Return to Learn Tracker and most state dashboards were not available. Inferred modalities with a probability below 0.6 were deemed inconclusive and were omitted. During the Fall 2022 semester, modalities for districts with a school closure reported by Burbio were updated to either “Remote”, if the closure spanned the entire week, or “Hybrid”, if the closure spanned 1-4 days of the week. Data from August

Financial overview and grant giving statistics of American Academy of Distance Learning & Training Inc

Online and Distance Education at Postsecondary Institutions, 2006-07 (PEQIS 16), is a study that is part of the Postsecondary Education Quick Information System (PEQIS) program; program data is available since 1997 at . PEQIS 16 (https://nces.ed.gov/pubsearch/pubsinfo.asp?pubid=2009044) is a survey that collects data on the prevalence and delivery of distance education courses in the 2006-07 12-month academic year, including the number of courses and enrollment for online courses, hybrid/blended online courses, and all other distance education courses. The survey also collects information about numbers of degree or certificate programs designed to be completed entirely through distance education and the technologies used for the instructional delivery of credit-granting distance education courses. The study was conducted using paper and web surveys. The weighted response rate was 87 percent. Postsecondary institutions were sample for this study. Key statistics produced from PEQIS 16 relate to information on the prevalence, types, delivery, policies, and acquisition or development of distance education courses and programs.

Historical Dataset of Marana Distance Learning is provided by PublicSchoolReview and contain statistics on metrics:Total Students Trends Over Years (2006-2023),Distribution of Students By Grade Trends,American Indian Student Percentage Comparison Over Years (2008-2022),Asian Student Percentage Comparison Over Years (2007-2022),Hispanic Student Percentage Comparison Over Years (2006-2023),Black Student Percentage Comparison Over Years (2012-2023),White Student Percentage Comparison Over Years (2006-2023),Two or More Races Student Percentage Comparison Over Years (2011-2023),Diversity Score Comparison Over Years (2006-2023),Reading and Language Arts Proficiency Comparison Over Years (2010-2022),Math Proficiency Comparison Over Years (2011-2023),Overall School Rank Trends Over Years (2011-2023),Graduation Rate Comparison Over Years (2013-2023)

Abstract The article presents the development and results of a systematic review of the literature on Mathematics Education and Distance Learning. This review is part of a doctoral research in development on e-learning and b-learning practices in Brazilian Mathematics Teacher Education Programs. The main objective of the review was to identify in Mathematics Education how previous researches (January 2011 and December 2017) defined the e-learning and b-learning teaching models. In addition, it is possible to understand at what levels of education these investigations are situated: basic education, initial or continuing teacher education. Although focusing on a doctoral undergraduate research, it is believed that the previous research, reproduced at other school levels, can also add elements and reflections to understand these models of courses in Distance Education. We carried out a systematic review based on orientations from different organizations and researchers dedicated to this area of research. In this sense, we followed different phases in the process to make the review: definition of objectives/questions, research equations and databases; determination of inclusion, exclusion, and methodological validity criteria; presentation and discussion of results; and data. As supporting software, both Google spreadsheets and NVivo11 were herein used. In addition to a higher incidence of work that occur in the teacher training context, the review results show great dispersion about the concept of e-learning and a lower occurrence of studies on b-learning models. Also, a significant number of works refer to the need to create conditions, in Distance Teacher Education Programs, for the constitution of (virtual) learning communities.

Historical Dataset of Campbell Distance Learning is provided by PublicSchoolReview and contain statistics on metrics:Total Students Trends Over Years (2021-2023),Total Classroom Teachers Trends Over Years (2021-2023),Distribution of Students By Grade Trends,Student-Teacher Ratio Comparison Over Years (2021-2023),American Indian Student Percentage Comparison Over Years (2022-2023),Asian Student Percentage Comparison Over Years (2021-2023),Hispanic Student Percentage Comparison Over Years (2021-2023),Black Student Percentage Comparison Over Years (2021-2023),White Student Percentage Comparison Over Years (2021-2023),Two or More Races Student Percentage Comparison Over Years (2021-2023),Diversity Score Comparison Over Years (2021-2023),Free Lunch Eligibility Comparison Over Years (2021-2023),Reduced-Price Lunch Eligibility Comparison Over Years (2021-2023)

In March 2020, 60 percent of Russian universities provided distant learning without major interruptions. Another quarter of higher education institutions offered studying from home, but experienced occasional problems. The country's education ministry recommended all universities to switch to distance learning as a preventive measure against the spread coronavirus (COVID-19). Mail.ru Group offered technical assistance with switching to online learning to universities.

For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Facts and Figures page.

This section presents statistical information referring to the academic results of enrolled students from the Statistics of Non-university Education carried out by the Sub-Directorate General of Statistics and Studies of the Ministry of Education and Professional Training in cooperation with the statistical services of the Ministries/Departments of Education of the Autonomous Communities. Information is provided on academic results according to different characteristics of the student body and educational centers for all non-university General Regime, Special Regime and Adult Education.

Introduction

E-Learning Statistics: E-learning has swiftly transitioned from a supplementary resource to a fundamental aspect of modern education, harnessing digital technologies and online platforms to offer flexible, accessible learning opportunities. As internet usage, smartphone adoption, and the demand for skill development continue to rise, e-learning has emerged as a global trend, impacting both educational institutions and corporate training programs.

The COVID-19 pandemic further accelerated this shift, driving widespread adoption of online learning solutions. These statistics provide valuable insights that shed light on the current state, growth prospects, and key trends within the e-learning sector, underscoring its transformative role and the opportunities it creates for stakeholders across the globe.

Historical Dataset of Northern Arizona Distance Learning is provided by PublicSchoolReview and contain statistics on metrics:Total Students Trends Over Years (2017-2023),Distribution of Students By Grade Trends,American Indian Student Percentage Comparison Over Years (2017-2023),Asian Student Percentage Comparison Over Years (2017-2022),Hispanic Student Percentage Comparison Over Years (2019-2023),White Student Percentage Comparison Over Years (2017-2023),Two or More Races Student Percentage Comparison Over Years (2019-2022),Diversity Score Comparison Over Years (2017-2023),Reading and Language Arts Proficiency Comparison Over Years (2017-2022),Math Proficiency Comparison Over Years (2017-2023),Overall School Rank Trends Over Years (2017-2023),Graduation Rate Comparison Over Years (2018-2023)

Financial overview and grant giving statistics of Pennsylvania Distance Learning Association

Historical Dataset of Cloverdale Distance Learning Academy is provided by PublicSchoolReview and contain statistics on metrics:Total Students Trends Over Years (2022-2023),Distribution of Students By Grade Trends,Asian Student Percentage Comparison Over Years (2022-2023),Hispanic Student Percentage Comparison Over Years (2022-2023),Black Student Percentage Comparison Over Years (2022-2023),White Student Percentage Comparison Over Years (2022-2023),Two or More Races Student Percentage Comparison Over Years (2022-2023),Diversity Score Comparison Over Years (2022-2023),Math Proficiency Comparison Over Years (2022-2023),Overall School Rank Trends Over Years (2022-2023)

Financial overview and grant giving statistics of North Carolina Community College Association of Distance Learning

The Distance Education Courses for Public Elementary and Secondary School Students, 2009-10 (FRSS 98), is a study that is part of the Fast Response Survey System (FRSS) program; program data is available since 1998-99 at . FRSS 98 (https://nces.ed.gov/pubsearch/pubsinfo.asp?pubid=2012009) is a sample survey that provides national estimates on distance education courses in public school districts, including enrollment in distance education courses, how districts monitor these courses, the motivations for providing distance education, and the technologies used for delivering distance education. The study was conducted using surveys via the web or by mail. District superintendents were sampled. The study's weighted response rate was 95%. Key statistics produced from FRSS 98 include the types of distance education courses taken by students, whether the district plans to expand the number of distance education courses, and the technologies used for delivering distance education.

Please cite the following paper when using this dataset:

N. Thakur, “A Large-Scale Dataset of Twitter Chatter about Online Learning during the Current COVID-19 Omicron Wave,” Journal of Data, vol. 7, no. 8, p. 109, Aug. 2022, doi: 10.3390/data7080109

Abstract

The COVID-19 Omicron variant, reported to be the most immune evasive variant of COVID-19, is resulting in a surge of COVID-19 cases globally. This has caused schools, colleges, and universities in different parts of the world to transition to online learning. As a result, social media platforms such as Twitter are seeing an increase in conversations, centered around information seeking and sharing, related to online learning. Mining such conversations, such as Tweets, to develop a dataset can serve as a data resource for interdisciplinary research related to the analysis of interest, views, opinions, perspectives, attitudes, and feedback towards online learning during the current surge of COVID-19 cases caused by the Omicron variant. Therefore this work presents a large-scale public Twitter dataset of conversations about online learning since the first detected case of the COVID-19 Omicron variant in November 2021. The dataset is compliant with the privacy policy, developer agreement, and guidelines for content redistribution of Twitter and the FAIR principles (Findability, Accessibility, Interoperability, and Reusability) principles for scientific data management.

Data Description

The dataset comprises a total of 52,984 Tweet IDs (that correspond to the same number of Tweets) about online learning that were posted on Twitter from 9th November 2021 to 13th July 2022. The earliest date was selected as 9th November 2021, as the Omicron variant was detected for the first time in a sample that was collected on this date. 13th July 2022 was the most recent date as per the time of data collection and publication of this dataset.

The dataset consists of 9 .txt files. An overview of these dataset files along with the number of Tweet IDs and the date range of the associated tweets is as follows. Table 1 shows the list of all the synonyms or terms that were used for the dataset development.

Filename: TweetIDs_November_2021.txt (No. of Tweet IDs: 1283, Date Range of the associated Tweet IDs: November 1, 2021 to November 30, 2021)

Filename: TweetIDs_December_2021.txt (No. of Tweet IDs: 10545, Date Range of the associated Tweet IDs: December 1, 2021 to December 31, 2021)

Filename: TweetIDs_January_2022.txt (No. of Tweet IDs: 23078, Date Range of the associated Tweet IDs: January 1, 2022 to January 31, 2022)

Filename: TweetIDs_February_2022.txt (No. of Tweet IDs: 4751, Date Range of the associated Tweet IDs: February 1, 2022 to February 28, 2022)

Filename: TweetIDs_March_2022.txt (No. of Tweet IDs: 3434, Date Range of the associated Tweet IDs: March 1, 2022 to March 31, 2022)

Filename: TweetIDs_April_2022.txt (No. of Tweet IDs: 3355, Date Range of the associated Tweet IDs: April 1, 2022 to April 30, 2022)

Filename: TweetIDs_May_2022.txt (No. of Tweet IDs: 3120, Date Range of the associated Tweet IDs: May 1, 2022 to May 31, 2022)

Filename: TweetIDs_June_2022.txt (No. of Tweet IDs: 2361, Date Range of the associated Tweet IDs: June 1, 2022 to June 30, 2022)

Filename: TweetIDs_July_2022.txt (No. of Tweet IDs: 1057, Date Range of the associated Tweet IDs: July 1, 2022 to July 13, 2022)

The dataset contains only Tweet IDs in compliance with the terms and conditions mentioned in the privacy policy, developer agreement, and guidelines for content redistribution of Twitter. The Tweet IDs need to be hydrated to be used. For hydrating this dataset the Hydrator application (link to download and a step-by-step tutorial on how to use Hydrator) may be used.

Table 1. List of commonly used synonyms, terms, and phrases for online learning and COVID-19 that were used for the dataset development

Terminology

List of synonyms and terms

COVID-19

Omicron, COVID, COVID19, coronavirus, coronaviruspandemic, COVID-19, corona, coronaoutbreak, omicron variant, SARS CoV-2, corona virus

online learning

online education, online learning, remote education, remote learning, e-learning, elearning, distance learning, distance education, virtual learning, virtual education, online teaching, remote teaching, virtual teaching, online class, online classes, remote class, remote classes, distance class, distance classes, virtual class, virtual classes, online course, online courses, remote course, remote courses, distance course, distance courses, virtual course, virtual courses, online school, virtual school, remote school, online college, online university, virtual college, virtual university, remote college, remote university, online lecture, virtual lecture, remote lecture, online lectures, virtual lectures, remote lectures