According to a survey conducted in 2022, ** percent of students in higher education agreed that the quality of online instruction in higher education is the same as the quality of in-person instruction in the United States, while ** percent said that the quality was worse.

The online higher education market is experiencing robust growth, fueled by increasing accessibility, affordability demands, and technological advancements. The market's Compound Annual Growth Rate (CAGR) of 19.82% from 2019 to 2024 suggests a significant expansion, likely driven by factors such as the rising adoption of online learning platforms, flexible learning options catering to working professionals and geographically dispersed students, and the increasing recognition of online degrees by employers. The market segmentation, encompassing diverse types of online programs (e.g., bachelor's, master's, certificate programs) and applications across various fields (e.g., business, technology, healthcare), contributes to its broad appeal and expansion. Major players like American Public Education, Adtalem Global Education, and others are deploying competitive strategies focused on enhancing the learning experience, improving student support services, and expanding their program offerings to maintain a competitive edge. The geographic distribution indicates strong growth across North America and Asia-Pacific, driven by higher internet penetration and a growing young population seeking educational opportunities. However, challenges remain, including concerns about the perceived quality of online education compared to traditional institutions, the digital divide limiting access for certain demographics, and the need for continuous investment in technology and curriculum development to meet evolving learner needs. Looking ahead to 2033, the online higher education market is projected to maintain significant momentum, further expanding its reach and influence. Continued technological innovation, including advancements in virtual reality and artificial intelligence, will enhance the learning experience and attract a broader range of students. The growing importance of lifelong learning and upskilling will also drive demand for online courses and degree programs. Competitive pressures will likely lead to further innovation in pricing models, program offerings, and marketing strategies, fostering a dynamic and evolving market landscape. To fully capitalize on this growth, educational institutions must prioritize creating engaging and effective online learning environments, addressing concerns around quality and accessibility, and adapting to the ever-changing needs of students in a globally competitive market.

During a survey conducted in Spring 2023 in the United States, the most popular factor for choosing online education was the affordability of the program, with ** percent of respondents reporting this as one of their top three reasons. The second most popular factor was the reputation of the school or program.

Introduction

E-Learning Statistics: In today’s fast-moving digital world, e-learning has become a key tool for businesses and people who want to keep improving and growing. E-learning is convenient, easy to access, and flexible, making it a game-changer for traditional education. It’s now an essential resource for staying competitive and adaptable in various industries.

Before the global COVID-19 pandemic, online learning was already starting to show up in schools, from elementary through university, as well as in corporate training. Both students and teachers liked the flexibility it offered to everyone taking part in the lessons.

Don't worry; we've put together a list of important E-Learning Statistics for 2024, bringing together the most useful insights in one handy place.

2022

Like other sub-sectors in the education app market, skills and online training courses experienced significant growth at the beginning of the coronavirus pandemic, as many people lost jobs or were...

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

In 2023, seven percent of students strongly agreed that it was worthwhile for borrowers to take out loans for education after high school that is a predominantly online program in the United States. In comparison, 12 percent strongly disagreed with this belief.

In 2020, 58 percent of internet users in the United Kingdom (UK) aged between 16 and 24 used the internet to take part in online learning activities. Among 25-to-34-year-olds this share decreased to 36 percent. Among the other age groups, 55-to-64-years-olds were least likely do take part in online learning activities. Overall, the share of people using the internet for this purpose increased since 2015. The European questionnaire on Information and Communication Technologies Data reveals a disparity between the internet usage among different age groups. This disparity, although present in most countries, differs widely in its severity.

no. 8

Subject: EducationSpecific: Online Learning and FunType: Questionnaire survey data (csv / excel)Date: February - March 2020Content: Students' views about online learning and fun Data Source: Project OLAFValue: These data provide students' beliefs about how learning occurs and correlations with fun. Participants were 206 students from the OU

As of the third quarter of 2024, **************************** saw the highest reach of online learning videos worldwide, with approximately ** percent of internet users reporting they watched how-to videos, tutorials, and educational videos online weekly. Internet users located in ****** followed, with around **** percent of respondents stating that they watched learning videos.

Online learning (e-learning) course enrolment totals by course and year for public and Catholic schools. School boards report this data using the Ontario School Information System (OnSIS). Includes: * course code * course name * online learning course enrolment totals by year Enrolment totals include withdrawn or dropped courses. A student enrolled in more than one course is counted for each course. Data excludes private schools and Education and Community Partnership Program (ECPP) facilities. Not all courses offered by school boards are available to students via online learning. Cells are suppressed in categories with less than 10 students. Enrolment totals are rounded to the nearest five. Final as of October 4, 2024

Solution Probability Model and Fairness Evaluation

This in-session prediction model seeks to predict the users’ performance on the Orthografietrainer.net platform. The target variable is binary and predicts if the user will do the following sentence correctly or not. For fairness evaluations the best models (MLP and DTE), and the worst model (SVM) are considered. A random state is not set, thus, results might differ marginally.

A detailed description of the solution probability model and the fairness evaluation can be found here: tba

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

The COVID-19 pandemic brought with it not only sanitary challenges, but also social and economic difficulties on a global scale. The provision of means for students to keep on with their education despite the multiple social distancing restrictions is one of them. In this context, online learning has become a very useful alternative. Among ** Latin American and Caribbean countries analyzed in 2020, the most commonly used distance learning tool was online learning, implemented by ** countries in the region. In comparison, less than ** countries evaluated delivered devices or provided students with live online classes. Alternatives to online education Although online education has been the most chosen learning delivery system in Latin America and the Caribbean during the pandemic, a considerable part of the population in the region has little to no access to the internet or to digital learning tools. As a result, other creative ways of providing learning resources have been adopted. A good example of this has been the broadcasting of educational programs via television and radio. In Mexico, for instance, the program “Aprende en Casa” was launched at the beginning of the 2020/2021 scholar year to air educational content for each school level throughout the day. Digitalization in schools pre COVID-19 pandemic One of the characteristics of digitalization in Latin American schools, even before the COVID-19 pandemic, has been the evident inequalities among institutions and students. These disparities are present in multiple areas and vary not only between countries, but also within them. Uruguay, for instance, having one of the largest shares of pupils with an effective online learning support platform in the region, was also among the Latin American countries with the lowest share of students whose teachers were prepared to integrate digital devices to education.

Historical Dataset of Madison Online Learning Academy is provided by PublicSchoolReview and contain statistics on metrics:Total Students Trends Over Years (2022-2023),Distribution of Students By Grade Trends,Hispanic 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),Graduation Rate Comparison Over Years (2022-2023)

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.

The results show that online learning is considered less effective by students in remote areas; this happens because the communication network and infrastructure do not support them to follow online learning. After conducting this research, the assumptions about the displeasure or reduced effectiveness of online learning in this area proved correct and significant.

The share of internet users engaging in online learning activities in Latvia increased by 5.5 percentage points since the previous year. Therefore, the share of engagement in online learning activities in Latvia reached a peak in 2022 with 27.78 percent.The EU survey on the use of Information and Communication Technologies (ICT) in households and by individuals is an annual survey conducted since 2002 aiming at collecting and disseminating harmonised and comparable information on the use of ICT in households and by individuals. Data presented in this domain are collected on a yearly basis by the National Statistical Institutes and are based on Eurostat's annual model questionnaire. This questionnaire is updated each year to reflect the evolving situation of information and communication technologiesFind more statistics on other topics about Latvia with key insights such as share of daily internet users, share of internet users informing themselves about goods and services online, and share of people that upload self-created content.