Pandemic has influenced all spheres of the humanity. COVID-19 impacted the education vertical in larger manner. Traditional classroom environment plays a very vital role in molding the life of an individual. Bond nurtured in the early ages of the life acts as the great moral support in the latter stages of the journey. As the pandemic has forced us into online education, this data collection aims to analyze the impact of online education. To check out the satisfactory level of the learners, review was conducted.

Gender – Male, Female Home Location – Rural, Urban Level of Education – Post Graduate, School, Under Graduate Age – Years Number of Subjects – 1- 20 Device type used to attend classes – Desktop, Laptop, Mobile Economic status – Middle Class, Poor, Rich Family size – 1 -10 Internet facility in your locality – Number scale (Very Bad to Very Good) Are you involved in any sports? – Yes, No Do elderly people monitor you? – Yes, No Study time – Hours Sleep time – Hours Time spent on social media – Hours Interested in Gaming? – Yes, No Have separate room for studying? – Yes, No Engaged in group studies? – Yes, No Average marks scored before pandemic in traditional classroom – range Your interaction in online mode - Number scale (Very Bad to Very Good) Clearing doubts with faculties in online mode - Number scale (Very Bad to Very Good) Interested in? – Practical, Theory, Both Performance in online - Number scale (Very Bad to Very Good) Your level of satisfaction in Online Education – Average, Bad, Good

radhakrishnan, sujatha (2021), “Online Education System - Review”, Mendeley Data, V1, doi: 10.17632/bzk9zbyvv7.1

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.

Between December 2022 and January 2024, ******** was the online learning platform reporting the highest traffic, with a peak of *** million visits to its websites in December 2023. ******** ranked second, with the platform reaching a peak of ** million visits in the examined period. The website ******* (which stands for technology, entertainment, design) saw a peak of over ** million visits in March 2023.

According to Cognitive Market Research, the global Online Education market size will be USD 71524.8 million in 2025. It will expand at a compound annual growth rate (CAGR) of 23.20% from 2025 to 2033.

North America held the major market share of 37% of the global revenue with a market size of USD 26464.18 million in 2025 and will grow at a compound annual growth rate (CAGR) of 21.5% from 2025 to 2033.
Europe accounted for a market share of 29% of the global revenue with a market size of USD 20742.19 million.
APAC held the market share of 24% of the global revenue with a market size of USD 17165.95 million in 2025 and will grow at a compound annual growth rate (CAGR) of 26.3% from 2025 to 2033.
South America has a market share of 4% of the global revenue with a market size of USD 2717.94 million in 2025 and will grow at a compound annual growth rate (CAGR) of 24.0% from 2025 to 2033.
Middle East had a market share of 4% of the global revenue and was estimated at a market size of USD 2860.99 million in 2025 and will grow at a compound annual growth rate (CAGR) of 23.5% from 2025 to 2033.
Africa had a market share of 2.20% of the global revenue and was estimated at a market size of USD 1573.55 million in 2025 and will grow at a compound annual growth rate (CAGR) of 23.5% from 2025 to 2033.
Asynchronous Learning category is the fastest growing Learning Type segment of the Online Education industry

Market Dynamics of Online Education Market

Key Drivers for Online Education Market

Increasing internet penetration and smartphone usage boost market growth: The widespread penetration of the internet and the increasing adoption of smartphones are significantly boosting the growth of the online education market. As per the International Telecommunication Union (ITU), around 67% of the global population, or 5.4 billion people, are now online, reflecting a 4.7% increase since 2022. This growth is higher than the 3.5% recorded from 2021 to 2022. Consequently, the number of offline individuals dropped to 2.6 billion, representing 33% of the world’s population. With more people gaining access to affordable and high-speed internet, especially in emerging economies, the demand for digital learning solutions is rapidly rising. Smartphones have become a convenient medium for accessing educational content, enabling users to learn anytime and anywhere. This trend is further fueled by the availability of mobile applications and responsive websites that offer interactive learning experiences. Additionally, the proliferation of mobile broadband networks and the growing use of social media for educational purposes are propelling the market growth by making online education more accessible and user-friendly.

https://www.itu.int/itu-d/reports/statistics/2023/10/10/ff23-internet-use/

Rising adoption of digital technology in educational institutions propels demand: Educational institutions worldwide are increasingly adopting digital technologies to enhance teaching methods, engage students, and improve learning outcomes. According to the 2024 International Institute for Management Development (IMD) World Digital Competitiveness Ranking assesses the readiness of 67 economies to adopt digital technologies for economic transformation. Using data and executive surveys, it helps governments and companies identify resource priorities and best practices for digital growth. The integration of Learning Management Systems (LMS), digital whiteboards, and e-textbooks is transforming traditional classrooms into dynamic, interactive learning environments. This shift towards digitalization is driven by the need to cater to tech-savvy students and to bridge the gap between physical and virtual learning. Furthermore, the rise of hybrid learning models, where online resources complement in-person classes, is contributing to the increased demand for online education platforms. Government initiatives promoting digital education and the growing popularity of e-learning in higher education are also playing a pivotal role in driving this trend.

https://www.imd.org/centers/wcc/world-competitiveness-center/rankings/world-digital-competitiveness-ranking/

Restraint Factor for the Online Education Market

Concerns over data privacy and security in online platforms: Data privacy and security concerns are significant restraints in the online education market. With the increasing use of digital platforms, ...

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.

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.

Online university courses were estimated to have the highest revenue among other levels of digital education in Russia, at 15 billion Russian rubles in 2021. Furthermore, online secondary education services had a market value of 10 billion Russian rubles in the same year.

Education apps in Russia

The revenue of education applications in Russia was expected to reach 82 million U.S. dollars in 2025, up from 61 million U.S. dollars in 2021. The leading education app by in-app revenue in the country was Ewa, a language learning platform developed by Lithium Lab Pte Lld. in Singapore. The U.S.-based Duolingo was the second-most popular in the same category.

Russian e-Learner behavior

Over 60 percent of Russian participants of online courses took them for professional development, while personal interest was the second most common motivation. Most e-Learners took only free courses, while approximately 23 percent were enrolled in both free and paid programs. In most domestic digital education schools, 71 to 100 percent of students completed the programs.

[205+ Pages Report] The global online education market size is expected to grow from USD 217 billion in 2022 to USD 475 billion by 2030, at a CAGR of 9.1% from 2023-2030

Key Online Course App StatisticsTop Online Course AppsEducation App Market LandscapeOnline Course App RevenueOnline Course Revenue by AppOnline Course App UsersOnline Course Users by AppOnline Course...

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

The booming digital self-paced online education market, projected to reach nearly $10 billion by 2033, offers unparalleled learning flexibility. Explore market trends, key players (Coursera, Udemy, edX), and growth drivers in this comprehensive analysis. Discover how online learning is transforming education and reshaping the future of work.

Database includes data on online learning resources use of 1699 higher education students, including 56 special educational needs students. Measures of ICT- related beliefs, self-efficacy and perceived barriers are also in the dataset. Dataset was gathered in 2017 in Slovenia. It includes students from diverse disciplinary fields.

Context

This is an online survey on Bangladeshi Students, It has various columns such as level of education, age, access to internet, Impact of online classes on study hours, Issues faced in attending classes and device preferred to attend online class.

Content

It is a dataset in the tabular format and it contains 18 columns and 8784 rows.

Acknowledgements

Citation

@data{DVN/PLN7GM_2021, author = {Ferdows, Jannatul}, publisher = {Harvard Dataverse}, title = {{Online Survey Data of Bangladeshi Students}}, UNF = {UNF:6:mEhft2rgYEkMCtUf6rtYog==}, year = {2021}, version = {V1}, doi = {10.7910/DVN/PLN7GM}, url = {https://doi.org/10.7910/DVN/PLN7GM} }

Snapshot img

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?

Request Free Sample

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.

Get a glance at the market share of various segments Request Free Sample

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.

For more insights on the market share of various regions Request Free Sample

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

Historical Dataset of Karval Online Education is provided by PublicSchoolReview and contain statistics on metrics:Total Students Trends Over Years (2005-2023),Total Classroom Teachers Trends Over Years (2005-2023),Distribution of Students By Grade Trends,Student-Teacher Ratio Comparison Over Years (2005-2023),American Indian Student Percentage Comparison Over Years (2005-2023),Asian Student Percentage Comparison Over Years (2008-2012),Hispanic Student Percentage Comparison Over Years (2005-2023),Black Student Percentage Comparison Over Years (2005-2023),White Student Percentage Comparison Over Years (2005-2023),Diversity Score Comparison Over Years (2005-2023),Free Lunch Eligibility Comparison Over Years (2009-2023),Reduced-Price Lunch Eligibility Comparison Over Years (2009-2023),Reading and Language Arts Proficiency Comparison Over Years (2010-2014),Math Proficiency Comparison Over Years (2010-2014),Overall School Rank Trends Over Years (2010-2014),Graduation Rate Comparison Over Years (2011-2014)

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

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

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