informative essay about online distance learning

Essays About Online Learning: Top 6 Examples And Prompts

If you are writing essays about online learning, you can start by reading some essay examples and prompts in this article. 

People often regard online learning as kids stuck at home, glued to their devices. However, there is so much more to it than this simplistic concept. Many parents may see it as an “easy way out” for students to slack off on their studies while still passing their classes, but online learning has not reached its full potential yet. 

It has dramatically impacted how education is handled globally, for better or worse. It has forced teachers to take on extra work , while students say it has helped reduce their stress levels. It is undoubtedly a contentious topic. 

If you need help writing an essay about online learning, here are some essay examples you can use for inspiration.

1. Disabled Students Urge Universities To Make Online Learning More Accessible by Lucia Posteraro

2. why are more and more students taking online classes by perry mullins, 3. the benefits of online learning: 7 advantages of online degrees by kelsey miller, 4. why is online learning important by clare scott, 5. is online learning as effective as face-to-face learning by kelli wilkins, 6. i’m a high school student. i don’t want online learning to end. by rory selinger, prompts on essays about online learning, 1. how has online learning affected you, 2. compare and contrast online and in-person classes., 3. what can you learn from an online setup, 4. what is the future of online learning, 5. which is better- online or face-to-face learning, 6. can online learning be sustained long-term.

“Autism may hinder the ability to follow complex conversations, especially with background noise – but Charli’s lectures did not have subtitles. Moreover, extensions for group projects were too short for her extenuating circumstances.’

Posteraro tells the stories of students who want online learning to be more accessible. For example, Charli, a student with autism, was greatly affected by the transition from in-person to online classes during the COVID-19 pandemic. Unfortunately, online learning has not catered to her special needs, so she urges schools to take action to make online education more inclusive. You might also be interested in these essays about knowledge .

“The result of taking online classes is that students who take them become more proficient and comfortable with using computers. Students can learn to connect with one another online and with information in meaningful and useful ways. With that said more and more students are taking online classes because it’s the best way to save money work at your own pace and not have to be stressed about going to class.”

In his essay, Mullins discusses why more students prefer online learning. First, it lessens expenses, as students learn from the comfort of their rooms. Second, it helps students avert the fear of talking to strangers face-to-face, helping them communicate better. 

“It’s clear, then, that learning online helps prepare professionals for this shift toward online work. Below, explore what online courses entail, explore seven key benefits, and get the advice you need to determine if online courses are right for you.”

Miller briefly explains what online learning is, then proceeds to discuss its advantages. These include a self-paced schedule, improved communication, and new technical skills. However, he reminds readers that everyone is different; regardless of the benefits, they should only choose online learning if they believe it will work for them.

“Boil it right down and the answer is simple: change is constant. You must move with it. The true beauty of online learning is that it lends itself perfectly to your lifestyle. By its very nature, it can fit around you. Also, no longer are we taught how to do a job, it’s usually a case of figuring it out for yourself—and that’s where online learning can amplify your skills.”

Scott presents the importance of online learning. Similar to Miller, she mentions self-paced, giving students new skills. However, the most important lesson is that change is constant. Online learning exemplifies this precept, and these skills help us move along.

“While both ways of learning have advantages and disadvantages, what is more effective is based off of the student themselves. Students can weigh the costs and benefits between online learning and face-to-face learning. They can decide for themselves what would be best for them. Online learning can be as effective as face-to-face learning if the student is committed to putting their time and effort to study alone.”

Wilkins questions the notion that online learning is inferior to a face-to-face classes. She begins by listing the benefits of online classes, including comfort and easier schedules, as with Miller and Scott. However, she also mentions its disadvantages, such as the possibility of students being distracted and a lack of bonding between classmates. But, of course, it’s all up to the student in the end: they should decide which type of education they prefer.

“One thing I hope people now realize is that education is not a one-size-fits-all model. While the self-disciplined nature of remote learning is not for everyone, it has allowed students like me to flourish unimpeded by the challenges presented by typical classroom settings.”

A 14-year-old student, Selinger wishes to continue her education online as schools return to physical classes amid the pandemic. She discusses the relief she feels from the lack of peer pressure, judgment, and a rigorous schedule. Controlling your study schedule relieves students of pressure, and Selinger believes this is optimal for success. She believes online learning opens a path to be better rather than to “return to normal.”

In this essay, you can write about your experience of online learning. Whether you have had online coursework from school or college or taken an online course for your own interests, we’ve all had some experience learning online. Discuss how you benefited from online learning and the challenges you faced. For a compelling essay, conduct interviews to back up your experience by showing others who felt the same way.

Create an exciting comparative essay between online and in-person learning. You can compare and contrast the experiences and show the positives and negatives of each. Start by making a list or Venn diagram, and organize your essay. Include the structure, advantages, and disadvantages of each method of learning. 

Online learning can teach you some skills to succeed in the real world. In this essay, write about the unique skills you can gain from online learning. Perhaps you learn valuable IT skills, virtual note-taking, and basic administrative skills. Then, look into how these skills can benefit you in future studies or when trying to step into a new career path. 

We have barely scratched the surface of technology. In this essay, look to the future and imagine how online education will look. Then, research up-and-coming online learning technologies and see what will come next. Will the development of more online learning technology benefit students? Look into this exciting topic for an engaging discussion.

For this topic, writing an excellent argumentative essay is easy. First, from research and your own experience, list the benefits and downsides of each type of learning and determine which is more effective. Then, you can use Google and the essay examples above to support your argument.  

Online learning is most commonly used for students who are ill or during situations such as a global pandemic. It is meant to be temporary; however, can schools stick to a completely-online method of instruction? Include some advantages and disadvantages of online learning in your essay.

Tip: If writing an essay sounds like a lot of work, simplify it. Write a simple 5 paragraph essay instead.

If you’re still stuck, check out our general resource of essay writing topics .

Martin is an avid writer specializing in editing and proofreading. He also enjoys literary analysis and writing about food and travel.

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Online Education Essay: Distance Education & E-Learning

Online education has emerged as a dynamic and versatile alternative, providing learners with unprecedented access to a wealth of resources and opportunities. Let’s explore here, Online Education Essay

Online education, also known as e-learning or distance learning, is an innovative approach to acquiring knowledge and skills using digital technology and the Internet as the main medium of instruction.

This allows learners to remotely access educational content, interact with teachers, and collaborate with peers, overcoming geographic barriers and traditional classroom limitations.

Online education has experienced significant growth and development in recent years, changing the way people of all ages and backgrounds approach learning.

Online education essay explores the transformative power, benefits, challenges, and future prospects of online education in the modern era.

The importance of online education in today’s world cannot be overstated. The key points that highlight its importance are, such as…

Accessibility : Online education makes learning accessible to audiences around the world, overcoming geographic barriers. This allows people in remote and underserved areas to access quality education.

Flexibility : In an increasingly fast-paced world, online education offers flexibility in when and where you learn. This takes into account different schedules and lifestyles, including those of professionals and parents.

Lifelong learning : Online education promotes lifelong learning. Learners can gain new skills and knowledge at every stage of their lives and accelerate their personal and professional development.

Cost-effective : It often proves to be more cost-effective than traditional education. Learners can save on transportation, accommodation, and textbooks. This affordability increases access to education.

Customization : Online platforms allow you to personalize your learning experience and adapt content to your individual needs and speed. This improves comprehension and memory.

Technological advances : Integrating cutting-edge technologies such as virtual reality (VR) and artificial intelligence (AI) enriches the online learning experience and prepares learners for the digital age.

Pandemic response : The COVID-19 pandemic has highlighted the critical role of online education in ensuring continuity of learning during a crisis. This has become an important part of the education resilience toolkit.

Global collaboration : Online education fosters international collaboration and diverse perspectives. Learners can interact with peers and instructors from around the world, enriching their educational experience.

Employability : Many online courses and degrees are designed to be industry-relevant. Learners will gain skills that are directly applicable to their career goals.

Sustainability : Online education contributes to environmental sustainability by reducing the carbon footprint associated with commuting to a physical campus.

Overview of the components that typically make up the structure of online education.

1. Platform or institution website

Online education experiences often begin with a platform or institution’s website. This website serves as a central hub where learners can access information about available courses, enrollment, and resources.

2. Registration and Registration

Learners typically begin by enrolling in a course or program online. Registration may include creating an account, providing personal information, and selecting a course.

3. Course catalog

Online education platforms typically maintain a catalog of available courses and programs. Learners can search this catalog to find courses that match their interests and goals.

4. Course structure

Each course or program has its own structure and may include modules, units, and lessons. The course structure describes the order in which content is presented and the learning objectives for each section.

5. Learning resources

Online courses typically offer a variety of learning materials, including video lectures, text-based content, multimedia, and downloadable resources. These materials can be accessed through the Platform’s interface.

6. Interactive elements

Many online courses include interactive elements to engage learners, such as discussion forums, quizzes, assignments, and group projects. Learners can use these tools to communicate with instructors and other students.

7. Evaluation and scoring

Online courses include assessments to assess learner understanding of the material. Evaluation methods vary but include quizzes, exams, essays, peer reviews, and participation grades.

8. Support and help

Online learners often have access to technical and academic support. Depending on the platform, support can be provided via email, chat, or help desk.

9. Track your progress

Many platforms offer tools that allow learners to track their progress throughout a course. Learners can monitor completed assignments, upcoming deadlines, and overall course progress.

10. Certifications and references

Upon successfully completing a course or program, learners can receive a certificate, degree, or digital badge. You can add these credentials to your resume or share them on your professional profile.

11. Community and Commitment

Online education often focuses on building a sense of community among learners. To encourage participation, you can offer discussion forums, virtual meetings, and networking opportunities.

12. Privacy and security

The platform focuses on privacy and security, ensuring that learners’ personal information is protected. It will typically outline your privacy policy and data processing practices.

13. Frequently Asked Questions and Help Center

Many platforms offer a section where learners can find answers to frequently asked questions. You can provide a comprehensive help center or knowledge base.

14. Feedback and improvements

Platforms often seek feedback from learners to improve their online education experience. This feedback can be used to improve the content, functionality, and usability of your course.

The Evolution of Online Education

The evolution of online education has been a dynamic journey marked by significant advances in technology and changes in educational paradigms. 

Early experiments (1960s-1970s)

The concept of online education dates back to the 1960s when educational institutions such as the University of Illinois began experimenting with computer-based education. Early efforts focused on delivering educational content via mainframe computers and teleprinters.

Emergence of the Internet (1980s-1990s)

The development of the World Wide Web in the late 1980s and early 1990s laid the foundation for modern online education. Educational institutions began to explore the potential of the Internet to provide course materials and facilitate communication.

First online courses (1990s)

The first online courses, often referred to as “virtual classrooms” or “e-learning,” appeared in the mid-1990s. These courses included text-based content and basic discussion forums. Learning Management

Systems (LMS) (late 1990s to 2000s)

In the late 1990s and early 2000s, learning management systems (LMS) such as Blackboard and Moodle were developed. LMS platforms have given teachers the tools to create, manage, and deliver online courses.

Multimedia integration (2000s)

As Internet bandwidth improved, online courses began to incorporate multimedia elements such as videos, animations, and interactive simulations. This has enriched the learning experience and made online education more engaging.

Massive Open Online Courses (MOOCs) (2010s)

In the 2010s, MOOCs emerged, allowing students to take courses from famous universities for free. His MOOC platforms such as Coursera, edX, and Udacity have reached millions of learners around the world.

Personalization and adaptive learning (since 2010)

Online education platforms are beginning to implement personalized learning paths and adaptive technology. Algorithms analyze learner progress and tailor content to individual needs.

Blended learning (since 2010)

Blended learning models that combine online and in-person instruction are becoming increasingly popular in K-12 and higher education. This approach provides flexibility while maintaining personal interaction.

Virtual reality (VR) and augmented reality (AR) (since 2010)

Advances in VR and AR technology are being integrated into online education to provide immersive learning experiences. Learners can explore virtual environments and simulations.

Coronavirus disease (COVID-19) pandemic (2020)

The global pandemic has forced schools and universities to close to prevent the spread of the virus, forcing a rapid shift to online education. This has accelerated the adoption of online learning and highlighted the need for a robust digital infrastructure.

Hybrid and distance learning (2020s)

Many institutions will continue to offer online and hybrid learning options even after in-person classes resume. Remote work and online education are becoming more integrated into daily life.

Continuous innovation (ongoing)

As technology advances, online education continues to evolve. Artificial intelligence, data analytics, and learning analytics are playing an increasingly important role in the design of online learning experiences.

Benefits of Online Education

Challenges in Online Education (Online education essay)

Technological Advancements in Online Learning (Online education essay)

Advances in technology have revolutionized the online learning landscape, improving the educational experience and expanding its possibilities. The main technological advances in online learning are as…

Learning Management System (LMS)

LMS platforms such as Moodle, Blackboard, and Canvas provide a central hub for course management, content delivery, and communication between instructors and students.

Mobile learning (M-Learning)

Mobile apps and responsive design make learning more accessible as learners can access course materials and participate in learning activities on their smartphones and tablets.

Video conferences and webinars

Tools like Zoom and Microsoft Teams make it easy to conduct live virtual classes and webinars, facilitating real-time interaction between instructors and learners.

Gamification

Gamification techniques such as badges, leaderboards, and interactive quizzes make learning more engaging and motivate learners to progress through course content.

Virtual reality (VR) and augmented reality (AR)

VR and AR technology provide an immersive learning experience, allowing learners to explore virtual environments and interact with their 3D objects, making it ideal for training in fields such as medicine, engineering, and aviation.

Artificial intelligence (AI)

AI-powered tools analyze learner data and provide personalized recommendations, including adaptive learning paths and targeted resources to address individual needs.

Big data and learning analytics

Big data analytics provides insights into learner behavior and performance, helping educators make data-driven decisions and improve course design and instruction.

Cloud computing

Cloud-based platforms store and deliver course content, making it accessible from anywhere and ensuring scalability for institutions and course providers.

Open Educational Resources (OER)

OER repositories provide free, open-licensed educational materials such as textbooks, videos, and assessments, reducing costs for learners.

Blockchain credentials

Blockchain technology is used to issue and verify digital credentials, making it easier to verify the authenticity of degrees, certificates, and badges earned online.

Chatbots and virtual assistants

AI-powered chatbots and virtual assistants provide instant support to learners by answering questions and guiding them through course content.

Peer learning platform

Online platforms facilitate peer-to-peer learning through features such as discussion forums, group projects, and collaboration tools.

Language processing and translation tools

Language processing technology and translation tools help you deliver courses in multiple languages ​​and support diverse learning groups.

Accessibility tools

Assistive technologies such as screen readers and closed captioning make online education more accessible to people with disabilities.

Cyber ​​security measures

Robust cybersecurity measures protect online learning platforms and learner data from cyber threats, ensuring the privacy and security of online education.

The Future of Online Education (Online education essay)

The future of online education holds tremendous growth and innovation. Advances in technology such as augmented reality and artificial intelligence provide immersive and personalized learning experiences.

Learning analytics provides deep insights and allows educators to tailor instruction to individual needs. The global reach of online education is expanding, providing access to high-quality courses to learners in underserved areas.

Moreover, online education will increasingly complement traditional classrooms and create hybrid learning environments. Continuing education and lifelong learning are becoming the norm as the lines between work and study blur.

The future of online education promises increased accessibility, flexibility, and relevance in a rapidly evolving knowledge-based world.

Online Education Best Practices (Online education essay)

Best practices in online education are essential to ensuring an effective and engaging digital learning experience. Clear communication between teachers and students, as well as between colleagues, is very important.

Well-structured courses with structured content, intuitive navigation, and regular updates accelerate student success. Encouraging active participation through discussions, collaborative projects, and peer feedback fosters a sense of community.

Flexibility in assessment and learning paths accommodates the diverse needs of learners. Timely feedback and support, as well as technical troubleshooting assistance, will enhance your learning process.

Additionally, educators must continually adapt to evolving online tools and teaching methods to ensure that online education is accessible, engaging, and effective.

We hope this online education essay covers all aspects of distance learning and e-learning and helps you understand this type of education.

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Argumentative Essay: Online Learning and Educational Access

Conventional learning is evolving with the help of computers and online technology. New ways of learning are now available, and improved access is one of the most important benefits available. People all around the world are experiencing improved mobility as a result of the freedom and potential that online learning provides, and as academic institutions and learning organisations adopt online learning technologies and remote-access learning, formal academic education is becoming increasingly legitimate. This essay argues the contemporary benefits of online learning, and that these benefits significantly outweigh the issues, challenges and disadvantages of online learning.

Online learning is giving people new choices and newfound flexibility with their personal learning and development. Whereas before, formal academic qualifications could only be gained by participating in a full time course on site, the internet has allowed institutions to expand their reach and offer recognized courses on a contact-partial, or totally virtual, basis. Institutions can do so with relatively few extra resources, and for paid courses this constitutes excellent value, and the student benefits with greater educational access and greater flexibility to learn and get qualified even when there lots of other personal commitments to deal with.

Flexibility is certainly one of the most important benefits, but just as important is educational access. On top of the internet’s widespread presence in developed countries, the internet is becoming increasingly available in newly developed and developing countries. Even without considering the general informational exposure that the internet delivers, online academic courses and learning initiatives are becoming more aware of the needs of people from disadvantaged backgrounds, and this means that people from such backgrounds are in a much better position to learn and progress than they used to be.

The biggest argument that raises doubt over online learning is the quality of online courses in comparison to conventional courses. Are such online courses good enough for employers to take notice? The second biggest argument is the current reality that faces many people from disadvantaged backgrounds, despite the improvements made in this area in recent years – they do not have the level of basic access needed to benefit from online learning. In fact, there are numerous sources of evidence that claim disadvantaged students are not receiving anywhere near the sort of benefits that online learning institutions and promoters are trying to instigate. Currently there are many organisations, campaigns and initiatives that are working to expand access to higher education. With such high participation, it can be argued that it is only a matter of time before the benefits are truly realised, but what about the global online infrastructure?

There is another argument that is very difficult to dispel, and that is the response of different types of students to the online learning paradigm. Evidence shows that there are certain groups of students that benefit from college distance learning much more than other groups. In essence, students must be highly motivated and highly disciplined if they are to learn effectively in their own private environment.

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• Published: 25 January 2021

Online education in the post-COVID era

• Barbara B. Lockee 1  

Nature Electronics volume  4 ,  pages 5–6 ( 2021 ) Cite this article

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The coronavirus pandemic has forced students and educators across all levels of education to rapidly adapt to online learning. The impact of this — and the developments required to make it work — could permanently change how education is delivered.

The COVID-19 pandemic has forced the world to engage in the ubiquitous use of virtual learning. And while online and distance learning has been used before to maintain continuity in education, such as in the aftermath of earthquakes 1 , the scale of the current crisis is unprecedented. Speculation has now also begun about what the lasting effects of this will be and what education may look like in the post-COVID era. For some, an immediate retreat to the traditions of the physical classroom is required. But for others, the forced shift to online education is a moment of change and a time to reimagine how education could be delivered 2 .

Looking back

Online education has traditionally been viewed as an alternative pathway, one that is particularly well suited to adult learners seeking higher education opportunities. However, the emergence of the COVID-19 pandemic has required educators and students across all levels of education to adapt quickly to virtual courses. (The term ‘emergency remote teaching’ was coined in the early stages of the pandemic to describe the temporary nature of this transition 3 .) In some cases, instruction shifted online, then returned to the physical classroom, and then shifted back online due to further surges in the rate of infection. In other cases, instruction was offered using a combination of remote delivery and face-to-face: that is, students can attend online or in person (referred to as the HyFlex model 4 ). In either case, instructors just had to figure out how to make it work, considering the affordances and constraints of the specific learning environment to create learning experiences that were feasible and effective.

The use of varied delivery modes does, in fact, have a long history in education. Mechanical (and then later electronic) teaching machines have provided individualized learning programmes since the 1950s and the work of B. F. Skinner 5 , who proposed using technology to walk individual learners through carefully designed sequences of instruction with immediate feedback indicating the accuracy of their response. Skinner’s notions formed the first formalized representations of programmed learning, or ‘designed’ learning experiences. Then, in the 1960s, Fred Keller developed a personalized system of instruction 6 , in which students first read assigned course materials on their own, followed by one-on-one assessment sessions with a tutor, gaining permission to move ahead only after demonstrating mastery of the instructional material. Occasional class meetings were held to discuss concepts, answer questions and provide opportunities for social interaction. A personalized system of instruction was designed on the premise that initial engagement with content could be done independently, then discussed and applied in the social context of a classroom.

These predecessors to contemporary online education leveraged key principles of instructional design — the systematic process of applying psychological principles of human learning to the creation of effective instructional solutions — to consider which methods (and their corresponding learning environments) would effectively engage students to attain the targeted learning outcomes. In other words, they considered what choices about the planning and implementation of the learning experience can lead to student success. Such early educational innovations laid the groundwork for contemporary virtual learning, which itself incorporates a variety of instructional approaches and combinations of delivery modes.

Online learning and the pandemic

Fast forward to 2020, and various further educational innovations have occurred to make the universal adoption of remote learning a possibility. One key challenge is access. Here, extensive problems remain, including the lack of Internet connectivity in some locations, especially rural ones, and the competing needs among family members for the use of home technology. However, creative solutions have emerged to provide students and families with the facilities and resources needed to engage in and successfully complete coursework 7 . For example, school buses have been used to provide mobile hotspots, and class packets have been sent by mail and instructional presentations aired on local public broadcasting stations. The year 2020 has also seen increased availability and adoption of electronic resources and activities that can now be integrated into online learning experiences. Synchronous online conferencing systems, such as Zoom and Google Meet, have allowed experts from anywhere in the world to join online classrooms 8 and have allowed presentations to be recorded for individual learners to watch at a time most convenient for them. Furthermore, the importance of hands-on, experiential learning has led to innovations such as virtual field trips and virtual labs 9 . A capacity to serve learners of all ages has thus now been effectively established, and the next generation of online education can move from an enterprise that largely serves adult learners and higher education to one that increasingly serves younger learners, in primary and secondary education and from ages 5 to 18.

The COVID-19 pandemic is also likely to have a lasting effect on lesson design. The constraints of the pandemic provided an opportunity for educators to consider new strategies to teach targeted concepts. Though rethinking of instructional approaches was forced and hurried, the experience has served as a rare chance to reconsider strategies that best facilitate learning within the affordances and constraints of the online context. In particular, greater variance in teaching and learning activities will continue to question the importance of ‘seat time’ as the standard on which educational credits are based 10 — lengthy Zoom sessions are seldom instructionally necessary and are not aligned with the psychological principles of how humans learn. Interaction is important for learning but forced interactions among students for the sake of interaction is neither motivating nor beneficial.

While the blurring of the lines between traditional and distance education has been noted for several decades 11 , the pandemic has quickly advanced the erasure of these boundaries. Less single mode, more multi-mode (and thus more educator choices) is becoming the norm due to enhanced infrastructure and developed skill sets that allow people to move across different delivery systems 12 . The well-established best practices of hybrid or blended teaching and learning 13 have served as a guide for new combinations of instructional delivery that have developed in response to the shift to virtual learning. The use of multiple delivery modes is likely to remain, and will be a feature employed with learners of all ages 14 , 15 . Future iterations of online education will no longer be bound to the traditions of single teaching modes, as educators can support pedagogical approaches from a menu of instructional delivery options, a mix that has been supported by previous generations of online educators 16 .

Also significant are the changes to how learning outcomes are determined in online settings. Many educators have altered the ways in which student achievement is measured, eliminating assignments and changing assessment strategies altogether 17 . Such alterations include determining learning through strategies that leverage the online delivery mode, such as interactive discussions, student-led teaching and the use of games to increase motivation and attention. Specific changes that are likely to continue include flexible or extended deadlines for assignment completion 18 , more student choice regarding measures of learning, and more authentic experiences that involve the meaningful application of newly learned skills and knowledge 19 , for example, team-based projects that involve multiple creative and social media tools in support of collaborative problem solving.

In response to the COVID-19 pandemic, technological and administrative systems for implementing online learning, and the infrastructure that supports its access and delivery, had to adapt quickly. While access remains a significant issue for many, extensive resources have been allocated and processes developed to connect learners with course activities and materials, to facilitate communication between instructors and students, and to manage the administration of online learning. Paths for greater access and opportunities to online education have now been forged, and there is a clear route for the next generation of adopters of online education.

Before the pandemic, the primary purpose of distance and online education was providing access to instruction for those otherwise unable to participate in a traditional, place-based academic programme. As its purpose has shifted to supporting continuity of instruction, its audience, as well as the wider learning ecosystem, has changed. It will be interesting to see which aspects of emergency remote teaching remain in the next generation of education, when the threat of COVID-19 is no longer a factor. But online education will undoubtedly find new audiences. And the flexibility and learning possibilities that have emerged from necessity are likely to shift the expectations of students and educators, diminishing further the line between classroom-based instruction and virtual learning.

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Lockee, B.B. Online education in the post-COVID era. Nat Electron 4 , 5–6 (2021). https://doi.org/10.1038/s41928-020-00534-0

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Published : 25 January 2021

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DOI : https://doi.org/10.1038/s41928-020-00534-0

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Taking distance learning ‘offline’: Lessons learned from navigating the digital divide during COVID-19

Subscribe to the center for universal education bulletin, angelica towne amporo and angelica towne amporo chief strategy and innovation officer & co-founder - educate hawah nabbuye hawah nabbuye 2018 echidna global scholar - the brookings institution, uganda country director - educate uganda @hawahhawah.

August 7, 2020

As we adjust to life during a global pandemic, it’s hard to imagine what life was like over a century ago during outbreaks. While in the past most faced quarantines without a telephone or a radio, today there is an expansive universe online. Even as the coronavirus forces physical isolation, the spectacular technological advances of the digital age make local and global connection possible. However, within education, the new centrality of communication technology in the context of the vast digital divide means the pandemic is exacerbating inequality, excluding many youth from their right to learn.

Prior to the outbreak of COVID-19, our East African youth skills organization, Educate! , reached youth primarily through national education systems—delivering our model directly in schools or working with the government. For over a decade, we’ve been operating this way, partnering with secondary schools in Uganda to prepare youth with the skills to succeed in today’s economy, as well as working on systems-level integration of skills-based learning in Uganda, Rwanda, and Kenya. But schools across East Africa have been closed since March, and access to tools like smartphones, internet, and electricity is scarce in the region. This means that many distance learning strategies being deployed in other parts of the world are not feasible, and we’ve observed a significant gap in solutions for youth. The challenges inherent to delivering distance learning in resource-constrained areas remain largely unsolved—requiring creative, context-driven solutions.

Our approach

When schools across East Africa closed in mid-March, Educate! acted quickly to launch a response—aiming to embrace the now and act swiftly —pivoting to deliver components of our skills-based model to youth remotely through radio, SMS (text messaging), and interactive voice response (“robocalls”). During this time, our team began executing extensive remote learning research, as well as developing data collection platforms, which would be key to ensuring our program best fit the needs of our learners.

Although moving to distance learning was new territory for us, luckily we didn’t have to reinvent the wheel. First, we invested in learning from the many organizations working to tackle the digital divide prior to COVID-19: Girl Effect in girls’ empowerment, Eneza and M-Shule in academic learning, and the countless organizations providing learning continuity in humanitarian emergencies . Leveraging these learnings and equity-focused best practices , our local teams of curriculum and learning experience designers hit the ground running.

In just over three months of implementation, we’ve experienced exciting progress and key breakthroughs, coupled with failures, flops, and stubborn challenges—all of which have been critical for developing distance learning strategies of our own. By sharing our emerging best practices, we hope to contribute to the creation of quality and equitable distance learning solutions, allowing young people in every corner of the world to stay engaged with their education.

Lessons learned for effective distance learning solutions

1. leverage user data to tailor programmatic design to learner realities.

Our greatest obstacle has been determining how to consistently reach youth with limited access to the internet and connectivity through phone or radio. To address this challenge and inform an effective response, we needed to deeply understand our students’ realities. And to understand our students’ realities, we needed data! While we leveraged existing country data on school closures, as well as young people’s broad access to technology, we needed to collect data specific to our students’ lives. We needed to understand what life was like at home, how frequently our students could access a phone or radio, what barriers they faced learning outside the classroom, and if gender affected their ability to participate.

While collecting data under countrywide coronavirus restrictions has been challenging, it has been critical for informing our response. To collect data, our team leveraged low-tech means, including disseminating surveys to youth through SMS, WhatsApp, and phone calls. We leveraged phone-based surveys to guide our programmatic decisionmaking and used WhatsApp groups for rapid design feedback. We have also targeted data on gender, developing a data point within our student contact database, allowing us to disaggregate by gender. As our team targets equal participation among boys and girls in our programming, disaggregation by gender has been critical for informing our remote gender equity strategy (discussed below in learning #5).

While these data collection platforms don’t reach all of our students, these systems have generated rich datasets on key indicators, such as participation. A key barrier we discovered through student surveys is that many youth have taken on new home responsibilities, cutting into time for their studies. Mornings are especially busy, as many students are completing household chores or supporting their families with agricultural work. In response to these learnings, we scheduled radio lessons on the weekends and sent learning prompts via SMS later in the day, when youth had finished their chores. By listening closely to our students and looking at a holistic picture of their lives, we have been able to increase participation in our remote programming quite simply, without addressing the complex issues of technology access.

2. Go beyond broadcasting content: Layer strategies and build in interaction

It’s widely recognized that real and meaningful learning occurs in the classroom only when curriculum goes beyond rote memorization and lecture-based instruction. We believe that the same approach should be applied to distance learning, so we have prioritized hybrid distance learning strategies that have two-way engagement built in.

We are taking a multipronged approach in Uganda—leveraging radio for content delivery, with robocalls, SMS, and remote mentorship for follow-up assessment, engagement, and guidance. While we don’t believe that distance learning strategies can replace in-person instruction, we think that “layering” strategies with built-in engagement can strengthen their impact. Evidence backs this up: In Kenya, a study examining the multimedia platform Shujazz showed that youth exhibited positive behavior changes after receiving targeted content through comics, social media, and SMS. Lastly, building in student responses to these mechanisms has the added advantage of supporting critical data collection.

3. Look for new ways to engage families

As schools began to close in March, our team urgently worked to collect student phone numbers to enroll students in our remote programming. However, of the 13,000 phone numbers we were able to collect, fewer than 50 percent were active. In addition, research conducted by our team at the outset of the pandemic found that many of our students only have access to a shared device for about 30 minutes per day.

Drawing on lessons learned from past emergencies, we conducted targeted outreach to parents and family members. We quickly learned that youth could participate more consistently in our remote programming if they used a family member’s phone rather than their own, as parents and relatives were more likely to own a phone as well as keep their phone numbers active. We also believe this strategy enhances the quality of the learning for youth because parents can help ensure their children engage actively with learning prompts. Further, a number of studies show that when communities and parents are engaged in students’ learning, academic achievement increases.

After targeting outreach to families, we saw a 29 percent increase in participation in our remote programming, and since launching, we have grown our reach from roughly 10 percent of our previous student level to 50-60 percent, with the expectation that our reach will continue to grow as we scale nationally. As with all things technology-enabled, this growth is exponential and has a snowball effect, so we’re hopeful about the future.

4. Incorporate story-based learning to keep youth engaged

Our team leveraged this feedback to rewrite radio scripts, rework linear learning activities, and introduce new characters within the lessons. While we are continuing to iterate on our distance learning curriculum, we are already beginning to see a positive impact, as 90 percent of our listeners have reported they relate to these story-based activities.

5. Think critically about pedagogy and content delivery to better support girls

Educate!’s curriculum was developed with gender responsiveness at the forefront, and we’ve designed our model to address critical gaps girls face—such as asset and skills gaps—to impact life outcomes. As we’ve worked to transition our curriculum to entirely new delivery mechanisms, we have taken a deliberate approach to integrating gender equity within our remote programming’s design and delivery.

Leveraging the data collection strategies outlined above, we discovered that boys in our programming were more likely to own their own phones than girls—making it challenging for our female learners to participate actively during radio lessons, as well as with assessments and learning prompts delivered via SMS. While we are still working to tackle the core issue of access among female learners, our team has set out to support girls and promote equal participation through a variety of programmatic components.

Our team of designers ensures that the content of every lesson and learning prompt delivered by radio or SMS is gender-responsive. For example, lead characters within our curriculum are female secondary school students, and we select confident female entrepreneurs within our case studies. Through our in-school model, we’ve seen that this strategy is effective in combating the socialization of girls to be quiet and reserved, as well as the negative stereotypes that typecast girls as less competent. In addition to gender-responsive pedagogy, we have begun exploring the implementation of all-girl listener groups as a way to create safe spaces at home for female learners. Following the release of a radio lesson, a female Educate! youth mentor convenes five to 10 girls on a conference call, where they connect to reflect on what they learned in the lesson, as well as discuss challenges they face learning at home.

In the foreseeable future, it seems likely that restrictions on gatherings will remain, limiting the education sector’s ability to reach youth directly in schools. By sharing these early lessons in effective distance learning, we believe we can work together as a sector to navigate this new normal. Together, we can rethink traditional education on a global level—pushing it further into the 21st century and toward a more equitable future.

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Distance Learning: Advantages and Disadvantages

Introduction, the essence of distance learning, advantages and disadvantages of distance learning, works cited.

Computer and information technologies have significantly affected all spheres of human life. These technologies have also changed the field of education, since the improvement and development of this direction is one of the main mechanisms that make up the public life of the United States. Thus, a new form of distance learning has appeared in modern human life, which, along with the traditional form, has taken an important place in our society. This kind of training allows not only to study but also to improve the qualification level of its users.

The research paper offered to the reader is devoted to the concept of distance learning, as well as its advantages and disadvantages. The question of the advantages and disadvantages of distance learning has been in the focus of research attention especially against the background of a general quarantine, which justifies the actuality of this topic. To facilitate the preparation of this final project, the author formulates the problem in several forms of proposals, namely:

• Analysis of the phenomenon of distance learning.
• Analysis of the pros and cons of distance learning.

This study focuses on analyzing the pros and cons of distance learning, as well as predicting its further application. The results of this study are of practical use, because they will be of interest to students and teachers who are choosing whether to switch to remote learning.

Sawsan Abuhammad, the Assistant Professor in Jordan University of Science and Technology, in his article “Barriers to distance learning during the COVID-19 outbreak: A qualitative review from parents’ perspective” (2020) states the following. The author claims that many parents have faced serious problems in the process of distance learning of their children. The author believes that the barriers that arose among the parents were of a personal, financial and technical nature. The author also states that these barriers need to be eliminated with the help of some changes, including through communication with other parents and students.

The author used the social network Facebook to recognize local groups, as well as keywords including distance learning, parents and Jordan. The author used a general qualitative method and analyzed all the messages and posts of parents related to this topic. This article was written by the author in order to describe and clarify the ideas of parents about the obstacles to distance learning during the coronavirus crisis (Abuhammad). The main audience of this article is parents, as well as persons representing the government and making decisions regarding distance learning. Thus, in the process of distance learning, many parents have various barriers that need to be overcome. We intend to use this source to demonstrate the problems and difficulties of distance learning.

Živko Bojović, Petar D. Bojović, Dušan Vujošević and Jelena Šuh, in their article “Education in times of crisis: Rapid transition to distance learning” (2020), state the following. They claim that the pandemic crisis has a negative effect on the standard of living and education. The authors believe that violation can pose a serious threat, and therefore a working model is needed that will allow switching from the traditional form of training to distance learning quickly and painlessly. The authors also argue that distance learning is acceptable on a long-term basis, if it is implemented correctly.

The authors of this article used a modeling method that allowed them to determine organizational and technical solutions for maintaining the quality of teaching. In addition, the authors used the method of comparative analysis of the survey data of students and teachers. The article was written by the authors in order to facilitate the transition from traditional learning to distance learning against the background of the pandemic and quarantine (Bojović et al.). The model developed by them has many advantages and thoughtful solutions. The main audience of this article is teachers and other representatives of educational institutions who face the difficult task of implementing distance learning. We intend to use this article to better understand the essence of distance learning, as well as its advantages.

Tim Surma and Paul A. Kirschner in their article, “Technology enhanced distance learning should not forget how learning happens” (2020), state the following. They believe that the traditional type of learning is under threat due to the accelerated process of adapting the traditional learning process to a new, remote one. They argue that modern technologies are both a danger and a chance for education to reach a completely new level.

The authors of this article used the methods of surveys and interviews to find out the attitude of students and teachers to the new form of education, and to track the progress in learning. This article was written by the authors in order to provide the importance of clear guidelines and optimal use of distance learning technologies (Surma and Kirschner). Moreover, the authors identified important principles that will help students get used to a new form of education, for example, feedback and an individual approach. The main audiences of this article are students, parents and teachers who will be interested in this information for the successful implementation of distance learning. We intend to use this article to understand the possible future prospects of the distance learning method.

John Traxler, the Professor of Digital Learning in the Institute of Education at the University of Wolverhampton, in his article, “Distance Learning—Predictions and Possibilities” (2018), states the following. The author claims that the definition of distance learning is not clear, but vague and changeable. The author considers the process of distance learning in a global context and studies the issue of adaptation and implementation of distance learning. The author believes that people should be ready for global changes, be open and aware, since changes are inevitable.

The author of this article uses observation and comparison methods that allow determining the essence of distance learning, the danger of pressure on educational institutions, as well as the importance of innovations in education. This article was written by the author in order to create a complete understanding of the phenomenon of distance education in a global context (Traxler). In addition, this article demonstrates the difficulties of distance learning application in conditions of ignorance or isolation. The main audience of this article is teachers, students and parents who want to get acquainted in more detail with the concept of distance learning in a global context. We intend to use this article to learn more about what distance learning is, as well as its goals and objectives.

The main benefit of distance learning is that it allows a person to study anywhere, but requires a computer and the Internet. The material is easily accessible and easy to handle and structure, and it also has all the necessary features that students of higher educational institutions need. In addition, the student is free to build their own individual training schedule, depending on their free time and desire to study (Lassoued et al.). The difference between classical distance learning and its more advanced form is small – the lack of personal communication between students and teachers (Bojović et al.). In this paper, the pros and cons of distance learning will be considered, but first it is required to understand the very essence of distance learning.

In the process of remote learning, students and teachers are at a significant spatial and temporal distance from each other. Teachers use a variety of computer technologies to make the process of remote learning as interesting and useful for students as possible (Schneider and Council). Distance type of education has an important goal-to expand opportunities and provide new services for those people who want to acquire new skills or change their profession. There are six main forms of distance learning, which are the most common.

• external education;
• university education;
• training that involves the cooperation of several educational institutions;
• creation of specialized institutions where distance classes are held;
• autonomous learning systems;
• special multimedia courses that differ in a certain informal component.

At the same time, different technologies are combined: pedagogical, informational, and often andragogic. There is a British synchronous model of distance learning and an American asynchronous one. Distance education is a new, specific form of education, somewhat different from the usual forms of full-time or distance learning (Dietrich et al.). As for the present, the real contingent of potential students can include those who are often on business trips, military personnel, women on maternity leave, and people with physical disabilities. In addition, this category consists of those who want to get additional education with a lack of time. Distance learning has several key characteristics that are important to consider when analyzing this type of learning.

• flexible and convenient schedule of classes;
• modularity;
• mass character;
• active mutual communication and a variety of communication tools;
• the totality of knowledge and orientation to the independence of students, to the motivation of learning.

Indeed, the effectiveness of distance learning directly depends on those teachers who work with students on the Internet. Such teachers should be psychologically ready to work with students in a new educational and cognitive network environment. Another problem is the infrastructure of student information support in networks. The question of what the structure and composition of the educational material should be remains open. Also, the question is raised about the conditions of access to distance learning courses.

Analyzing the components of distance learning related to the educational institution, they can determine the structure of the network system. It should include educational material submitted in the form of programs, tasks, control and graduation papers, and scientific and practical assistance (Costa et al.). The student should be provided with fundamental printed textbooks, teaching aids, and hypertext multimedia programs (Arthur-Nyarko et al.). Additional materials may include lectures prepared by teachers on disciplines that can be transmitted via the network. In addition, distance learning provides communication in various modes, teacher advice on implementing term papers, theses, or other final work.

The essential component of distance learning is the ability to consider situations that are close to reality. In addition, important elements are creating conditions for the self-realization of students, the disclosure of their potential, the systematic learning process, the individuality of the approach (Bojović et al.). This component is the basis of academic and cognitive activity and affects the quality of distance learning.

Electronic versions of textbooks, which became the basis for the creation of distance courses and traditional books, do not solve the problems of independent activity in obtaining knowledge. These software products only create a virtual learning environment in which distance learning is carried out. Here there are psychological problems, such as inexperience, lack of self-education skills, poor volitional self-regulation, the influence of group attitudes, etc. When developing distance learning programs, it is crucial to carefully plan classes, including each of them with the setting of learning goals and objectives.

If interpersonal communication between students and the teacher is ineffective, there is a possibility of a communication barrier. If this happens, the information is delivered in a distorted form, which leads to the fact that there is a threat of the cognitive barrier growing into a relationship barrier. The barrier of relations turns into a feeling of distrust and hostility towards information and its source.

There are also many disadvantages in distance learning that should be listed and that cannot be ignored. It is worth starting with technical and methodological problems, including ignoring the psychological laws of perception and assimilation of information using multimedia tools of different modalities. There are also methodological problems, including the complexity of developing electronic versions of traditional educational materials, primarily textbooks and practical manuals.

Many students and experts believe that distance learning has many indisputable and obvious advantages.

• A student studying remotely independently plans their schedule and decides how much time to devote to studying.
• The opportunity to study anywhere. Students studying remotely are not tied to a place or time, as they only need an Internet connection.
• Study on the job from the main activity. Distance learning allows to work or study at several courses at the same time to get additional education.
• High learning outcomes. Remote students study the necessary material independently, which allows them to better memorize and assimilate knowledge.
• Distance learning is much cheaper, since it does not require expenses for accommodation and travel, as well as for a foreign passport if the university is located abroad.
• Remote education provides a calm environment, as exams and communication with teachers are held online, which allows students to avoid anxiety.
• Teachers who conduct remote classes have the opportunity to do additional things, cover a larger number of students, as well as teach while, for example, on maternity leave.
• Remote learning allows teachers to use a more individual approach to their students, as well as to devote a sufficient amount of time to all students.

Experiments have confirmed that the quality and structure of training courses, as well as the quality of teaching in distance learning is often much better than in traditional forms of education. New electronic technologies can not only ensure the active involvement of students in the educational process, but also allow them to manage this process, unlike most traditional educational environments (Arthur-Nyarko et al.). The interactive capabilities of the programs and information delivery systems used in the distance learning system make it possible to establish and even stimulate feedback. Despite the predominant number of advantages of distance education, this system is not perfect. During the implementation of e-learning programs, the following problems of distance education were identified.

• Remote learning requires strong concentration and motivation. Almost all the educational material is mastered by a remote student independently. Remote classes require students to have perseverance and developed patience.
• In the process of distance learning, it is difficult to develop interpersonal communication skills, since contact with teachers and other students is minimal.
• In the process of distance learning, it is quite difficult to acquire practical skills, thus, specialties that require practical skills suffer.
• The problem of user identification. It is difficult to track whether a student wrote their exam honestly, since the only way to check this is video surveillance, which is not always possible.
• Insufficient computer literacy. In every country there are remote areas where there is no direct access to the Internet. Moreover, often the residents of such areas do not have any desire to learn, so it is necessary to spread computer literacy.

It is required to start by creating special Internet conferences and forums in schools that would guarantee the relative “live” communication of groups of students to deal with disadvantages (Chen et al.). It is also necessary to cooperate with traditional and distance learning, cooperation between teachers and students using a broad terminological and methodological base of psychology and pedagogy (Abuhammad). Despite all these problems, distance learning is very much appreciated by psychologists and teachers (Traxler). Nevertheless, the complete replacement of traditional education systems with similar ones-distance ones still causes some caution. One thing is indisputable – remotely studying students are more adapted to external conditions, are responsible and active, and therefore more successful in the modern business world.

Speaking about the distance form of education, it is necessary to talk about the creation of a single information and educational space. When it comes to distance learning, it is necessary to understand the presence of a teacher, a textbook and a student in the system, as well as the interaction of a teacher and students. It follows from this that the main thing in the organization of distance learning is the creation of electronic courses, the development of didactic foundations of distance learning, and the training of teachers-coordinators. It is not necessary to identify the distance form with the correspondence form of education, because it provides for constant contact with the teacher and imitation of all types of full-time training.

The dynamism of economic and socio-cultural processes in society causes changes in the field of education. Since the features of distance education are simply not acceptable for many students. Based on psychology and the methodology of independent learning, distance learning has some advantages and disadvantages. Summing up, we can unequivocally answer that distance education has a future. However, much depends on how quickly the problems of eliminating information illiteracy, technical equipment and improving the quality of e-education will be resolved. These factors arise during the implementation of remote scientific programs and projects. So, the factors and examples given above show the need to create and expand distance learning in the United States.

Abuhammad, Sawsan. “ Barriers to distance learning during the COVID-19 outbreak: A qualitative review from parents’ perspective. ” Heliyon (2020): e05482. Web.

Arthur-Nyarko, Emmanuel, Douglas Darko Agyei, and Justice Kofi Armah. “Digitizing distance learning materials: Measuring students’ readiness and intended challenges.” Education and Information Technologies (2020): 1-16. Web.

Bojović, Živko, et al. “Education in times of crisis: Rapid transition to distance learning.” Computer Applications in Engineering Education 28.6 (2020): 1467-1489.

Chen, Emily, Kristie Kaczmarek, and Hiroe Ohyama. “Student perceptions of distance learning strategies during COVID‐19.” Journal of dental education (2020). Web.

Costa, Roberto D., et al. “The theory of learning styles applied to distance learning.” Cognitive Systems Research 64 (2020): 134-145. Web.

Dietrich, Nicolas, et al. “Attempts, successes, and failures of distance learning in the time of COVID-19.” Journal of Chemical Education 97.9 (2020): 2448-2457. Web.

Lassoued, Zohra, Mohammed Alhendawi, and Raed Bashitialshaaer. “ An exploratory study of the obstacles for achieving quality in distance learning during the COVID-19 pandemic. ” Education Sciences 10.9 (2020): 232. Web.

Schneider, Samantha L., and Martha Laurin Council. “Distance learning in the era of COVID-19.” Archives of dermatological research 313.5 (2021): 389-390. Web.

Surma, Tim, and Paul A. Kirschner. “Technology enhanced distance learning should not forget how learning happens.” Computers in human behavior 110 (2020): 106390. Web.

Traxler, John. “ Distance learning—Predictions and possibilities. ” Education Sciences 8.1 (2018): 35. Web.

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Research Article

Distance learning in higher education during COVID-19: The role of basic psychological needs and intrinsic motivation for persistence and procrastination–a multi-country study

Roles Conceptualization, Methodology, Writing – original draft

* E-mail: [email protected]

Affiliation Department of Developmental and Educational Psychology, Faculty of Psychology, University of Vienna, Vienna, Austria

Roles Conceptualization, Data curation, Methodology, Project administration, Writing – review & editing

Roles Formal analysis, Methodology, Writing – original draft, Writing – review & editing

Roles Conceptualization, Methodology, Writing – review & editing

Roles Data curation, Writing – review & editing

Affiliation Department of Mathematics, Faculty of Mathematics, University of Vienna, Vienna, Austria

Roles Conceptualization, Funding acquisition, Methodology, Writing – review & editing

Roles Conceptualization, Funding acquisition, Methodology

Affiliation Department of Psychology, Faculty of Education, Aleksandër Moisiu University, Durrës, Albania

Affiliation Department of Educational Sciences, Faculty of Philology and Education, Bedër University, Tirana, Albania

Affiliation Xiangya School of Nursing, Central South University, Changsha, China

Affiliations Xiangya School of Nursing, Central South University, Changsha, China, Department of Nursing Science, University of Turku, Turku, Finland

Affiliation Study of Nursing, University of Applied Sciences Bjelovar, Bjelovar, Croatia

Affiliation Baltic Film, Media and Arts School, Tallinn University, Tallinn, Estonia

Affiliation Faculty of Educational Sciences, University of Helsinki, Helsinki, Finland

Affiliation Department of Psychology, University of Bonn, Bonn, Germany

Affiliation Chair of Educational Psychology, Technische Universität Berlin, Berlin, Germany

Affiliation Department of Educational Studies, University of Potsdam, Potsdam, Germany

Affiliation Faculty of Education, University of Akureyri, Akureyri, Iceland

Affiliation Department of Global Education, Tsuru University, Tsuru, Japan

Affiliation Career Center, Osaka University, Osaka University, Suita, Japan

Affiliation Graduate School of Education, Osaka Kyoiku University, Kashiwara, Japan

Affiliation Department of Psychology, Faculty of Philosophy, University of Prishtina ’Hasan Prishtina’, Pristina, Kosovo

Affiliation Department of Social Work, Faculty of Philosophy, University of Pristina ’Hasan Prishtina’, Pristina, Kosovo

Affiliation Department of Psychology, Faculty of Social Sciences and Humanities, Klaipėda University, Klaipėda, Lithuania

Affiliation Geography Department, Junior College, University of Malta, Msida, Malta

Affiliation Institute of Family Studies, Faculty of Philosophy, Ss. Cyril and Methodius University in Skopje, Skopje, North Macedonia

Affiliation Institute of Psychology, Faculty of Social Science, University of Gdańsk, Gdańsk, Poland

Affiliation Faculty of Historical and Pedagogical Sciences, University of Wrocław, Wrocław, Poland

Affiliation Faculty of Educational Studies, Adam Mickiewicz University, Poznań, Poland

Affiliation CERNESIM Environmental Research Center, Alexandru Ioan Cuza University, Iași, România

Affiliation Social Sciences and Humanities Research Department, Institute for Interdisciplinary Research, Alexandru Ioan Cuza University of Iași, Iași, România

Affiliation Department of Informatics, Örebro University School of Business, Örebro University, Örebro, Sweden

Affiliation Faculty of Social Studies, Penn State University, State College, Pennsylvania, United States of America

•  [ ... ],

Affiliations Department of Developmental and Educational Psychology, Faculty of Psychology, University of Vienna, Vienna, Austria, Department for Teacher Education, Centre for Teacher Education, University of Vienna, Vienna, Austria

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• Elisabeth R. Pelikan, 
• Selma Korlat, 
• Julia Reiter, 
• Julia Holzer, 
• Martin Mayerhofer, 
• Barbara Schober, 
• Christiane Spiel, 
• Oriola Hamzallari, 
• Ana Uka, 

• Published: October 6, 2021
• https://doi.org/10.1371/journal.pone.0257346
• Peer Review
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Due to the COVID-19 pandemic, higher educational institutions worldwide switched to emergency distance learning in early 2020. The less structured environment of distance learning forced students to regulate their learning and motivation more independently. According to self-determination theory (SDT), satisfaction of the three basic psychological needs for autonomy, competence and social relatedness affects intrinsic motivation, which in turn relates to more active or passive learning behavior. As the social context plays a major role for basic need satisfaction, distance learning may impair basic need satisfaction and thus intrinsic motivation and learning behavior. The aim of this study was to investigate the relationship between basic need satisfaction and procrastination and persistence in the context of emergency distance learning during the COVID-19 pandemic in a cross-sectional study. We also investigated the mediating role of intrinsic motivation in this relationship. Furthermore, to test the universal importance of SDT for intrinsic motivation and learning behavior under these circumstances in different countries, we collected data in Europe, Asia and North America. A total of N = 15,462 participants from Albania, Austria, China, Croatia, Estonia, Finland, Germany, Iceland, Japan, Kosovo, Lithuania, Poland, Malta, North Macedonia, Romania, Sweden, and the US answered questions regarding perceived competence, autonomy, social relatedness, intrinsic motivation, procrastination, persistence, and sociodemographic background. Our results support SDT’s claim of universality regarding the relation between basic psychological need fulfilment, intrinsic motivation, procrastination, and persistence. However, whereas perceived competence had the highest direct effect on procrastination and persistence, social relatedness was mainly influential via intrinsic motivation.

Citation: Pelikan ER, Korlat S, Reiter J, Holzer J, Mayerhofer M, Schober B, et al. (2021) Distance learning in higher education during COVID-19: The role of basic psychological needs and intrinsic motivation for persistence and procrastination–a multi-country study. PLoS ONE 16(10): e0257346. https://doi.org/10.1371/journal.pone.0257346

Editor: Shah Md Atiqul Haq, Shahjalal University of Science and Technology, BANGLADESH

Received: March 30, 2021; Accepted: August 29, 2021; Published: October 6, 2021

Copyright: © 2021 Pelikan et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: Data is now publicly available: Pelikan ER, Korlat S, Reiter J, Lüftenegger M. Distance Learning in Higher Education During COVID-19: Basic Psychological Needs and Intrinsic Motivation 2021. doi: 10.17605/OSF.IO/8CZX3 .

Funding: This work was funded by the Vienna Science and Technology Fund (WWTF) [ https://www.wwtf.at/ ] and the MEGA Bildungsstiftung [ https://www.megabildung.at/ ] through project COV20-025, as well as the Academy of Finland [ https://www.aka.fi ] through project 308351, 336138, and 345117. BS is the grant recipient of COV20-025. KSA is the grant recipient of 308351, 336138, and 345117. Open access funding was provided by University of Vienna. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

In early 2020, countries across the world faced rising COVID-19 infection rates, and various physical and social distancing measures to contain the spread of the virus were adopted, including curfews and closures of businesses, schools, and universities. By the end of April 2020, roughly 1.3 billion learners were affected by the closure of educational institutions [ 1 ]. At universities, instruction was urgently switched to distance learning, bearing challenges for all actors involved, particularly for students [ 2 ]. Moreover, since distance teaching requires ample preparation time and situation-specific didactic adaptation to be successful, previously established concepts for and research findings on distance learning cannot be applied undifferentiated to the emergency distance learning situation at hand [ 3 ].

Generally, it has been shown that the less structured learning environment in distance learning requires students to regulate their learning and motivation more independently [ 4 ]. In distance learning in particular, high intrinsic motivation has proven to be decisive for learning success, whereas low intrinsic motivation may lead to maladaptive behavior like procrastination (delaying an intended course of action despite negative consequences) [ 5 , 6 ]. According to self-determination theory (SDT), satisfaction of the three basic psychological needs for autonomy, competence and social relatedness leads to higher intrinsic motivation [ 7 ], which in turn promotes adaptive patterns of learning behavior. On the other hand, dissatisfaction of these basic psychological needs can detrimentally affect intrinsic motivation. According to SDT, satisfaction of the basic psychological needs occurs in interaction with the social environment. The context in which learning takes place as well as the support of social interactions it encompasses play a major role for basic need satisfaction [ 7 , 8 ]. Distance learning, particularly when it occurs simultaneously with other physical and social distancing measures, may impair basic need satisfaction and, in consequence, intrinsic motivation and learning behavior.

The aim of this study was to investigate the relationship between basic need satisfaction and two important learning behaviors—procrastination (as a consequence of low or absent intrinsic motivation) and persistence (as the volitional implementation of motivation)—in the context of emergency distance learning during the COVID-19 pandemic. In line with SDT [ 7 ] and previous studies (e.g., [ 9 ]), we also investigated the mediating role of intrinsic motivation in this relationship. Furthermore, to test the universal importance of SDT for intrinsic motivation and learning behavior under these specific circumstances, we collected data in 17 countries in Europe, Asia, and North America.

The fundamental role of basic psychological needs for intrinsic motivation and learning behavior

SDT [ 7 ] provides a broad framework for understanding human motivation, proposing that the three basic psychological needs for autonomy, competence, and social relatedness must be satisfied for optimal functioning and intrinsic motivation. The need for autonomy refers to an internal perceived locus of control and a sense of agency. In an academic context, students who learn autonomously feel that they have an active choice in shaping their learning process. The need for competence refers to the feeling of being effective in one’s actions. In addition, students who perceive themselves as competent feel that they can successfully meet challenges and accomplish the tasks they are given. Finally, the need for social relatedness refers to feeling connected to and accepted by others. SDT proposes that the satisfaction of each of these three basic needs uniquely contributes to intrinsic motivation, a claim that has been proved in numerous studies and in various learning contexts. For example, Martinek and colleagues [ 10 ] found that autonomy satisfaction was positively whereas autonomy frustration was negatively related to intrinsic motivation in a sample of university students during COVID-19. The same held true for competence satisfaction and dissatisfaction. A recent study compared secondary school students who perceived themselves as highly competent in dealing with their school-related tasks during pandemic-induced distance learning to those who perceived themselves as low in competence [ 11 ]. Students with high perceived competence not only reported higher intrinsic motivation but also implemented more self-regulated learning strategies (such as goal setting, planning, time management and metacognitive strategies) and procrastinated less than students who perceived themselves as low in competence. Of the three basic psychological needs, the findings on the influence of social relatedness on intrinsic motivation have been most ambiguous. While in some studies, social relatedness enhanced intrinsic motivation (e.g., [ 12 ]), others could not establish a clear connection (e.g., [ 13 ]).

Intrinsic motivation, in turn, is regarded as particularly important for learning behavior and success (e.g., [ 6 , 14 ]). For example, students with higher intrinsic motivation tend to engage more in learning activities [ 9 , 15 ], show higher persistence [ 16 ] and procrastinate less [ 6 , 17 , 18 ]. Notably, intrinsic motivation is considered to be particularly important in distance learning, where students have to regulate their learning themselves. Distance-learning students not only have to consciously decide to engage in learning behavior but also persist despite manifold distractions and less external regulation [ 4 ].

Previous research also indicates that the satisfaction of each basic need uniquely contributes to the regulation of learning behavior [ 19 ]. Indeed, studies have shown a positive relationship between persistence and the three basic needs (autonomy [ 20 ]; competence [ 21 ]; social relatedness [ 22 ]). Furthermore, all three basic psychological needs have been found to be related to procrastination. In previous research with undergraduate students, autonomy-supportive teaching behavior was positively related to satisfaction of the needs for autonomy and competence, both of which led to less procrastination [ 23 ]. A qualitative study by Klingsieck and colleagues [ 18 ] supports the findings of previous studies on the relations of perceived competence and autonomy with procrastination, but additionally suggests a lack of social relatedness as a contributing factor to procrastination. Haghbin and colleagues [ 24 ] likewise found that people with low perceived competence avoided challenging tasks and procrastinated.

SDT has been applied in research across various contexts, including work (e.g., [ 25 ]), health (e.g., [ 26 ]), everyday life (e.g., [ 27 ]) and education (e.g., [ 15 , 28 ]). Moreover, the pivotal role of the three basic psychological needs for learning outcomes and functioning has been shown across multiple countries, including collectivistic as well as individualistic cultures (e.g., [ 29 , 30 ]), leading to the conclusion that satisfaction of the three basic needs is a fundamental and universal determinant of human motivation and consequently learning success [ 31 ].

Self-determination theory in a distance learning setting during COVID-19

As Chen and Jang [ 28 ] observed, SDT lends itself particularly well to investigating distance learning, as the three basic needs for autonomy, competence and social relatedness all relate to important aspects of distance learning. For example, distance learning usually offers students greater freedom in deciding where and when they want to learn [ 32 ]. This may provide students with a sense of agency over their learning, leading to increased perceived autonomy. At the same time, it requires students to regulate their motivation and learning more independently [ 4 ]. In the unique context of distance learning during COVID-19, it should be noted that students could not choose whether and to what extent to engage in distance learning, but had to comply with external stipulations, which in turn may have had a negative effect on perceived autonomy. Furthermore, distance learning may also influence perceived competence, as this is in part developed by receiving explicit or implicit feedback from teachers and peers [ 33 ]. Implicit feedback in particular may be harder to receive in a distance learning setting, where informal discussions and social cues are largely absent. The lack of face-to-face contact may also impede social relatedness between students and their peers as well as students and their teachers. Well-established communication practices are crucial for distance learning success (see [ 34 ] for an overview). However, providing a nurturing social context requires additional effort and guidance from teachers, which in turn necessitates sufficient skills and preparation on their part [ 34 , 35 ]. Moreover, the sudden switch to distance learning due to COVID-19 did not leave teachers and students time to gradually adjust to the new learning situation [ 36 ]. As intrinsic motivation is considered particularly relevant in the context of distance education [ 28 , 37 ], applying the SDT framework to the novel situation of pandemic-induced distance learning may lead to important insights that allow for informed recommendations for teachers and educational institutions about how to proceed in the context of continued distance teaching and learning.

In summary, the COVID-19 situation is a completely new environment, and basic need satisfaction during learning under pandemic-induced conditions has not been explored before. Considering that closures of educational institutions have affected billions of students worldwide and have been strongly debated in some countries, it seems particularly relevant to gain insights into which factors consistently influence conducive or maladaptive learning behavior in these circumstances in a wide range of countries and contextual settings.

Therefore, the overall goal of this study is to investigate the well-established relationship between the three basic needs for autonomy, competence, and social relatedness with intrinsic motivation in the new and specific situation of pandemic-induced distance learning. Firstly, we examine the relationship between each of the basic needs with intrinsic motivation. We expect that perceived satisfaction of the basic needs for autonomy (H1a), competence (H1b) and social relatedness (H1c) would be positively related to intrinsic motivation. In our second research question, we furthermore extend SDT’s predictions regarding two important aspects of learning behavior–procrastination (as a consequence of low or absent intrinsic motivation) and persistence (as the implementation of the volitional part of motivation) and hypothesize that each basic need will be positively related to persistence and negatively related to procrastination, both directly (procrastination: H2a –c; persistence: H3a –c) and mediated by intrinsic motivation (procrastination: H4a –c; persistence: H5a –c). We also proposed that perceived autonomy, competence, and social relatedness would have a direct negative relation with procrastination (H6a –c) and a direct positive relation with persistence (H7a –c). Finally, we investigate SDT’s claim of universality, and assume that the aforementioned relationships will emerge across countries we therefore expect a similar pattern of results in all observed countries (H8a –c). As previous studies have indicated that gender [ 4 , 17 , 38 ] and age [ 39 , 40 ]. May influence intrinsic motivation, persistence, and procrastination, we included participants’ gender and age as control variables.

Study design

Due to the circumstances, we opted for a cross-sectional study design across multiple countries, conducted as an online survey. We decided for an online-design due to the pandemic-related restrictions on physical contact with potential survey participants as well as due to the potential to reach a larger audience. As we were interested in the current situation in schools than in long-term development, and we were particularly interested in a large-scale section of the population in multiple countries, we decided on a cross-sectional design. In addition, a multi-country design is particularly interesting in a pandemic setting: During this global health crisis, educational institutions in all countries face the same challenge (to provide distance learning in a way that allows students to succeed) but do so within different frameworks depending on the specific measures each country has implemented. This provides a unique basis for comparing the effects of need fulfillment on students’ learning behavior cross-nationally, thus testing the universality of SDT.

Sample and procedure

The study was carried out across 17 countries, with central coordination taking place in Austria. It was approved and supported by the Austrian Federal Ministry of Education, Science and Research and conducted online. International cooperation partners were recruited from previously established research networks (e.g., European Family Support Network [COST Action 18123]; Transnational Collaboration on Bullying, Migration and Integration at School Level [COST Action 18115]; International Panel on Social), resulting in data collection in 16 countries (Albania, China, Croatia, Estonia, Finland, Germany, Iceland, Japan, Kosovo, Lithuania, Poland, Malta, North Macedonia, Romania, Sweden, USA) in addition to Austria. Data collection was carried out between April and August 2020. During this period, all participating countries were in some degree of pandemic-induced lockdown, which resulted in universities temporarily switching to distance learning. The online questionnaires were distributed among university students via online surveys by the research groups in each respective country. No restrictions were placed on participation other than being enrolled at a university in the sampling country. Participants were informed about the goals of the study, expected time it would take to fill out the questionnaire, voluntariness of participation and anonymity of the acquired data. All research partners ensured that all ethical and legal requirements related to data collection in their country context were met.

Only data from students who gave their written consent to participate, had reached the age of majority (18 or older) and filled out all questions regarding the study’s main variables were included in the analyses (for details on data cleaning rules and exclusion criteria, see [ 41 ]). Additional information on data collection in the various countries is provided in S1 Table in S1 File .

The overall sample of N = 15,462 students was predominantly female (71.7%, 27.4% male and 0.7% diverse) and ranged from 18 to 71 years, with the average participant age being 24.41 years ( SD = 6.93, Mdn = 22.00). Sample descriptives per country are presented in Table 1 .

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https://doi.org/10.1371/journal.pone.0257346.t001

The variables analyzed here were part of a more extensive questionnaire; the complete questionnaire, as well as the analysis code and the data set, can be found at OSF [ 42 ] In order to take the unique situation into account, existing scales were adapted to the current pandemic context (e.g., adding “In the current home-learning situation …”), and supplemented with a small number of newly developed items. Subsequently, the survey was revised based on expert judgements from our research group and piloted with cognitive interview testing. The items were sent to the research partners in English and translated separately by each respective research team either using the translation-back-translation method or by at least two native-speaking experts. Subsequently, any differences were discussed, and a consolidated version was established.

To assure the reliability of the scales, we analyzed them using alpha coefficients separately for each country (see S2–S18 Tables in S1 File ). All items were answered on a rating scale from 1 (= strongly agree) to 5 (= strongly disagree) and students were instructed to answer with regard to the current situation (distance learning during the COVID-19 lockdown). Analyses were conducted with recoded items so that higher values reflected higher agreement with the statements.

Perceived autonomy was measured with two newly constructed items (“Currently, I can define my own areas of focus in my studies” and “Currently, I can perform tasks in the way that best suits me”; average α = .78, ranging from .62 to .86).

Perceived competence was measured with three items, which were constructed based on the Work-related Basic Need Satisfaction Scale (W-BNS; [ 25 ]) and transferred to the learning context (“Currently, I am dealing well with the demands of my studies”, “Currently, I have no doubts about whether I am capable of doing well in my studies” and “Currently, I am managing to make progress in studying for university”; average α = .83, ranging from .74 to .91).

Perceived social relatedness was assessed with three items, based on the W-BNS [ 43 ], (“Currently, I feel connected with my fellow students”, “Currently, I feel supported by my fellow students”) and the German Basic Psychological Need Satisfaction and Frustration Scale [ 44 ]; “Currently, I feel connected with the people who are important to me (family, friends)”; average α = .73, ranging from .64 to .88).

Intrinsic motivation was measured with three items which were slightly adapted from the Scales for the Measurement of Motivational Regulation for Learning in University Students (SMR-LS; [ 45 ]; “Currently, doing work for university is really fun”, “Currently, I am really enjoying studying and doing work for university” and “Currently, I find studying for university really exciting”; average α = .91, ranging from .83 to .94).

Procrastination was measured with three items adapted from the Procrastination Questionnaire for Students (Prokrastinationsfragebogen für Studierende; PFS; [ 46 ]): “In the current home-learning situation, I postpone tasks until the last minute”, “In the current home-learning situation, I often do not manage to start a task when I set out to do so”, and “In the current home-learning situation, I only start working on a task when I really need to”; average α = .88, ranging from .74 to .91).

Persistence was measured with three items adapted from the EPOCH measure [ 47 ]: “In the current home-learning situation, I finish whatever task I begin”, “In the current home-learning situation, I keep at my tasks until I am done with them” and “In the current home-learning situation, once I make a plan to study, I stick to it”; average α = .81, ranging from .74 to .88).

Data analysis.

Data analyses were conducted using IBM SPSS version 26.0 and Mplus version 8.4. First, we tested for measurement invariance between countries prior to any substantial analyses. We conducted a multigroup confirmatory factor analysis (CFAs) for all scales individually to test for configural, metric, and scalar invariance [ 48 , 49 ] (see S19 Table in S1 File ). We used maximum likelihood parameter estimates with robust standard errors (MLR) to deal with the non-normality of the data. CFI and RMSEA were used as indicators for absolute goodness of model fit. In line with Hu and Bentler [ 50 ], the following cutoff scores were considered to reflect excellent and adequate fit to the data, respectively: (a) CFI > 0.95 and CFI > 0.90; (b) RMSEA < .06 and RMSEA < .08. Relative model fit was assessed by comparing BICs of the nested models, with smaller BIC values indicating a better trade-off between model fit and model complexity [ 51 ]. Configural invariance indicates a factor structure that is universally applicable to all subgroups in the analysis, metric invariance implies that participants across all groups attribute the same meaning to the latent constructs measured, and scalar invariance indicates that participants across groups attribute the same meaning to the levels of the individual items [ 51 ]. Consequently, the extent to which the results can be interpreted depends on the level of measurement invariance that can be established.

For the main analyses, three latent multiple group mediation models were computed, each including one of the basic psychological needs as a predictor, intrinsic motivation as the mediator and procrastination and persistence as the outcomes. These three models served to test the hypothesis that perceived autonomy, competence and social relatedness are related to levels of procrastination and persistence, both directly and mediated through intrinsic motivation. We used bootstrapping in order to provide analyses robust to non-normal distribution variations, specifying 5,000 bootstrap iterations [ 52 ]. Results were estimated using the maximum likelihood (ML) method. Bias-corrected bootstrap confidence intervals are reported.

Finally, in an exploratory step, we investigated the international applicability of the direct and mediated effects. To this end, an additional set of latent mediation models was computed where the path estimates were fixed in order to create an average model across all countries. This was prompted by the consistent patterns of results across countries we observed in the multigroup analyses. Model fit indices of these average models were compared to those of the multigroup models in order to establish the similarity of path coefficients between countries.

Statistical prerequisites

Table 2 provides overall descriptive statistics and correlations for all variables (see S2–S18 Tables in S1 File for descriptive statistics for the individual countries).

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Metric measurement variance, but not scalar measurement invariance could be established for a simple model including the three individual items and no inter-correlations between perceived competence, perceived social relatedness, intrinsic motivation, and procrastination. For these four variables, the metric invariance model had a good absolute fit, whereas the scalar model did not, due to too high RMSEA; moreover, the relative fit was best for the metric model compared to both the configural and scalar model (see S18 Table in S1 File ). Metric, but not scalar invariance could also be established for persistence after modelling residual correlations between items 1 and 2 and items 2 and 3 of the scale. This was necessary due to the similar wording of the items (see “Measures” section for item wordings). Consequently, the same residual correlations were incorporated into all mediation models.

Finally, as the perceived autonomy scale consisted of only two items, it had to be fitted in a model with a correlating factor in order to compute measurement invariance. Both perceived competence and perceived social relatedness were correlated with perceived autonomy ( r = .59** and r = .31**, respectively; see Table 2 ). Therefore, we fit two models combining perceived autonomy with each of these factors; in both cases, metric measurement invariance was established (see S19 Table in S1 File ).

In summary, these results suggest that the meaning of all constructs we aimed to measure was understood similarly by participants across different countries. Consequently, we were able to fit the same mediation model in all countries and compare the resulting path coefficients.

Both gender and age were statistically significantly correlated with perceived competence, perceived social relatedness, intrinsic motivation, procrastination, and persistence (see S20–S22 Tables in S1 File ).

Mediation analyses

Autonomy hypothesis..

We hypothesized that higher perceived autonomy would relate to less procrastination and more persistence, both directly and indirectly (mediated through intrinsic learning motivation). Indeed, perceived autonomy was related negatively to procrastination (H6a) in most countries. Confidence intervals did not include zero in 10 out of 17 countries, all effect estimates were negative and standardized effect estimates ranged from b stand = - .02 to -.46 (see Fig 1 ). Furthermore, perceived autonomy was directly positively related to persistence in most countries. Specifically, for the direct effect of perceived autonomy on persistence (H7a), all but one country (USA, b stand = -.02; p = .621; CI [-.13, .08]) exhibited distinctly positive effect estimates ranging from b stand = .18 to .72 and confidence intervals that did not include zero.

Countries are ordered by sample size from top (highest) to bottom (lowest).

https://doi.org/10.1371/journal.pone.0257346.g001

In terms of indirect effects of perceived autonomy on procrastination mediated by intrinsic motivation (H7a), confidence intervals did not include zero in 8 out of 17 countries and effect estimates were mostly negative, ranging from b stand = -.33 to .03. Indirect effects of perceived autonomy on persistence (mediated by intrinsic motivation; H5a) were distinctly positive and confidence intervals did not include zero in 12 out of 17 countries. The indirect effect estimates and confidence intervals for all remaining countries were consistently positive, with the standardized effect estimates ranging from b stand = .13 to .39, indicating a robust, positive mediated effect of autonomy on persistence. Fig 2 displays the unstandardized path coefficients and their two-sided 5% confidence intervals for the indirect effects of perceived autonomy on procrastination via intrinsic motivation (left) and of perceived autonomy on persistence via intrinsic motivation (right).

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Unstandardized and standardized path coefficients, standard errors, p-values and bias-corrected bootstrapped confidence intervals for the direct and indirect effects of perceived autonomy on procrastination and persistence for each country are provided in S23–S26 Tables in S1 File , respectively.

Competence hypothesis. Secondly, we hypothesized that higher perceived competence would relate to less procrastination and more persistence both directly and indirectly, mediated through intrinsic learning motivation. Direct effects on procrastination (H6b) were negative in most countries and confidence intervals did not include zero in 10 out of 17 countries (see Fig 3 ).

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Standardized effect estimates ranged from b stand = -.02 to -.60, with 10 out of 17 countries exhibiting at least a medium-sized effect. Correspondingly, effect estimates for the direct effects on persistence were positive everywhere except the USA and confidence intervals did not include zero in 14 out of 17 countries (see Fig 3 ). Standardized effect estimates ranged from b stand = -.05 to .64 with 14 out of 17 countries displaying an at least medium-sized positive effect.

The pattern of results for the indirect effects of perceived competence on procrastination mediated by learning motivation (H4b) is illustrated in Fig 4 : Effect estimates were negative with the exception of China and the USA. Confidence intervals did not include zero in 7 out of 17 countries. Standardized effect estimates range between b stand = .06 and -.46. Indirect effects of perceived competence on persistence were positive everywhere except for two countries and confidence intervals did not include zero in 7 out of 17 countries (see Fig 4 ). Standardized effect estimates varied between b stand = -.07 and .46 (see S23–S26 Tables in S1 File for unstandardized and standardized path coefficients).

https://doi.org/10.1371/journal.pone.0257346.g004

Social relatedness hypothesis.

Finally, we hypothesized that stronger perceived social relatedness would be both directly and indirectly (mediated through intrinsic learning motivation) related to less procrastination and more persistence. The pattern of results was more ambiguous here than for perceived autonomy and perceived competence. Direct effect estimates on procrastination (H6c) were negative in 12 countries; however, the confidence intervals included zero in 12 out of 17 countries (see Fig 5 ). Standardized effect estimates ranged from b stand = -.01 to b stand = .33. The direct relation between perceived social relatedness and persistence (H7c) yielded 14 negative and three positive effect estimates. Confidence intervals did not include zero in 7 out of 17 countries (see Fig 5 ), with standardized effect estimates ranging from b stand = -.01 to b stand = .31.

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In terms of indirect effects of perceived social relatedness being related to procrastination mediated by intrinsic motivation (H4c), the pattern of results was consistent: All effect estimates except those for the USA were clearly negative, and confidence intervals did not include zero in 15 out of 17 countries (see Fig 6 ). Standardized effect estimates ranged between b stand = .00 and b stand = -.46. Indirect paths of perceived social relatedness on persistence showed positive effect estimates and standardized effect estimates ranging from b stand = .00 to .44 and confidence intervals not including zero in 16 out of 17 countries (see Fig 6 ; see S23–S26 Tables in S1 File for unstandardized and standardized path coefficients).

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Meta-analytic approach

Due to the overall similarity of the results across many countries, we decided to compute, in an additional, exploratory step, the same models with path estimates fixed across countries. This resulted in three models with average path estimates across the entire sample. Standardized path coefficients for the direct and indirect effects of the basic psychological needs on procrastination and persistence are presented in S27 and S28 Tables in S1 File , respectively. We compared the model fits of these three average models to those of the multigroup mediation models: If the fit of the average model is better than that of the multigroup model, it indicates that the individual countries are similar enough to be combined into one model. The amount of explained variance per model, outcome variable and country are provided in S29 Table in S1 File for procrastination and S30 Table in S1 File for persistence.

Perceived autonomy.

Relative model fit was better for the perceived autonomy model with fixed paths (BIC = 432,707.89) compared to the multigroup model (BIC = 432,799.01). Absolute model fit was equally good in the multigroup model (RMSEA = 0.05, CFI = 0.98, TLI = 0.97) and in the fixed path model (RMSEA = 0.05, CFI = 0.97, TLI = 0.97). Consequently, the general model in Fig 7 describes the data from all 17 countries equally well. The average amount of explained variance, however, is slightly higher in the multigroup model, with 19.9% of the variance in procrastination and 33.7% of the variance in persistence explained, as compared to 18.3% and 27.6% in the fixed path model. The amount of variance explained increased substantially in some countries when fixing the paths: in the multigroup model, explained variance ranges from 2.2% to 44.4% for procrastination and from 0.9% to 69.9% for persistence, compared to 13.0% - 27.7% and 18.2% to 63.2% in the fixed path model. Notably, the amount of variance explained did not change much in the three countries with the largest samples, Austria, Sweden, and Finland; countries with much smaller samples and larger confidence intervals were more affected.

*** p = < .001.

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Overall, perceived autonomy had significant direct and indirect effects on both procrastination and persistence; higher perceived autonomy was related to less procrastination directly ( b unstand = -.27, SE = .02, p = < .001) and mediated by learning motivation ( b unstand = -.20, SE = .01, p = < .001) and to more persistence directly ( b unstand = .24, SE = .01, p = < .001) and mediated by learning motivation ( b unstand = .12, SE = .01, p = < .001). Direct effects for the autonomy model are shown in Fig 7 ; for the indirect effects see Table 3 .

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Effects of age and gender varied across countries (see S20 Table in S1 File ).

Perceived competence.

For the perceived competence model, relative fit decreased when fixing the path coefficient estimates (BIC = 465,830.44 to BIC = 466,020.70). The absolute fit indices were also better for the multigroup model (RMSEA = 0.05, CFI = 0.97, TLI = 0.96) than for the fixed path model (RMSEA = 0.06, CFI = 0.96, TLI = 0.96). Hence, multigroup modelling describes the data across all countries somewhat better than a fixed path model as depicted in Fig 8 . Correspondingly, the fixed path model explained less variance on average than did the multigroup model, with 23.2% instead of 24.3% of the variance in procrastination and 32.9% instead of 37.3% of the variance in persistence explained. Explained variance ranged from 1.0% to 51.9% for procrastination in the multigroup model, as compared to 13.9% - 34.4% in the fixed path model. The amount of variance in persistence explained ranged from 1.0% to 58.1% in the multigroup model and from 23.5% to 55.9% in the fixed path model (see S29 and S30 Tables in S1 File ).

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Overall, higher perceived competence was related to less procrastination ( b unstand = -.44, SE = .02, p = < .001) and to higher persistence ( b unstand = .32, SE = .01, p = < .001). These effects were partly mediated by intrinsic learning motivation ( b unstand = -.11, SE = .01, p = < .001, and b unstand = .07, SE = .01, p = < .001, respectively; see Table 3 ). Effects of gender and age varied between countries, see S21 Table in S1 File .

Perceived social relatedness.

Finally, the perceived social relatedness model with fixed paths had a relatively better model fit (BIC = 479,428.46) than the multigroup model (BIC = 479,604.61). Likewise, the absolute model fit was similar in the model with path coefficients fixed across countries (RMSEA = 0.05, CFI = 0.97, TLI = 0.96) and the multigroup model (RMSEA = 0.05, CFI = 0.97, TLI = 0.97). The multigroup model explained 17.6% of the variance in procrastination and 26.3% of the variance in persistence, as compared to 15.2% and 21.6%, respectively in the fixed path model. Explained variance for procrastination ranged between 0.5% and 48.1% in the multigroup model, and from 9.0% to 23.0% in the fixed path model. Similarly, the multigroup model explained between 1.0% and 56.5% of the variance in persistence across countries, while the fixed path model explained between 15.6% and 48.3% (see S29 and S30 Tables in S1 File ).

Hence, the fixed path model depicted in Fig 9 is well-suited for describing data across all 17 countries. Higher perceived social relatedness is related to less procrastination both directly ( b unstand = -.06, SE = .01, p = < .001) and indirectly through learning motivation ( b unstand = -.12, SE = .01, p = < .001). Likewise, it is related to higher persistence both directly ( b unstand = .07, SE = .01, p = < .001) and indirectly through learning motivation ( b unstand = .08, SE = .00, p = < .001; see Table 3 ). Effects of gender and age are shown in S22 Table in S1 File .

https://doi.org/10.1371/journal.pone.0257346.g009

The aim of this study was to extend current research on the association between the basic psychological needs for autonomy, competence, and social relatedness with intrinsic motivation and two important aspects of learning behavior—procrastination and persistence—in the new and unique situation of pandemic-induced distance learning. We also investigated SDT’s [ 7 ] postulate that the relation between basic psychological need satisfaction and active (persistence) as well as passive (procrastination) learning behavior is mediated by intrinsic motivation. To test the theory’s underlying claim of universality, we collected data from N = 15,462 students across 17 countries in Europe, Asia, and North America.

Confirming our hypothesis, we found that the three basic psychological needs were consistently and positively related to intrinsic motivation in all countries except for the USA (H1a - c). This consistent result is in line with self-determination theory [ 7 ] and other previous studies (e.g., 9), which have found that satisfaction of the three basic needs for autonomy, competence and social relatedness is related to higher intrinsic motivation. Notably, the association with intrinsic motivation was stronger for perceived autonomy and perceived competence than for perceived social relatedness. This also has been found in previous studies [ 4 , 9 , 28 ]. Pandemic-induced distance learning, where physical and subsequential social contact in all areas of life was severely constricted, might further exacerbate this discrepancy, as instructors may have not been able to establish adequate communication structures due to the rapid switch to distance learning [ 36 , 53 ]. As hypothesized, intrinsic motivation was in general negatively related to procrastination (H2a - c) and positively related to persistence (H3a - c), indicating that students who are intrinsically motivated are less prone to procrastination and more persistent when studying. This again underlines the importance of intrinsic motivation for adaptive learning behavior, even and particularly in a distance learning setting, where students are more prone to disengage from classes [ 34 ].

The mediating effect of intrinsic motivation on procrastination and persistence

Direct effects of the basic needs on the outcomes were consistently more ambiguous (with smaller effect estimates and larger confidence intervals, including zero in more countries) than indirect effects mediated by intrinsic motivation. This difference was particularly pronounced for perceived social relatedness, where a clear negative direct effect on procrastination (H6c) could be observed only in the three countries with the largest sample size (Austria, Sweden, Finland) and Romania, whereas the confidence interval in most countries included zero. Moreover, in Estonia there was even a clear positive effect. The unexpected effect in the Estonian sample may be attributed to the fact that this country collected data only from international exchange students. Since the lockdown in Estonia was declared only a few weeks after the start of the semester, international exchange students had only a very short period of time to establish contacts with fellow students on site. Accordingly, there was probably little integration into university structures and social contacts were maintained more on a personal level with contacts from the home country. Thus, such students’ fulfillment of this basic need might have required more time and effort, leading to higher procrastination and less persistence in learning.

A diametrically opposite pattern was observed for persistence (H7c), where some direct effects of social relatedness were unexpectedly negative or close to zero. We therefore conclude that evidence for a direct negative relationship between social relatedness and procrastination and a direct positive relationship between social relatedness and persistence is lacking. This could be due to the specificity of the COVID-19 situation and resulting lockdowns, in which maintaining social contact took students’ focus off learning. In line with SDT, however, indirect effects of perceived social relatedness on procrastination (H4c) and persistence (H5c) mediated via intrinsic motivation were much more visible and in the expected directions. We conclude that, while the direct relation between perceived social relatedness and procrastination is ambiguous, there is strong evidence that the relationship between social relatedness and the measured learning behaviors is mediated by intrinsic motivation. Our results strongly underscore SDT’s assumption that close social relations promote intrinsic motivation, which in turn has a positive effect on learning behavior (e.g., [ 6 , 14 ]). The effects for perceived competence exhibited a somewhat clearer and hypothesis-conforming pattern. All direct effects of perceived competence on procrastination (H6b) were in the expected negative direction, albeit with confidence intervals spanning zero in 7 out of 17 countries. Direct effects of perceived competence on persistence (H7b) were consistently positive with the exception of the USA, where we observed a very small and non-significant negative effect. Indirect effects of perceived competence on procrastination (H4b) and persistence (H5b) as mediated by intrinsic motivation were mostly consistent with our expectations as well. Considering this overall pattern of results, we conclude that there is strong evidence that perceived competence is negatively associated with procrastination and positively associated with persistence. Furthermore, our results also support SDT’s postulate that the relationship between perceived competence and the measured learning behaviors is mediated by intrinsic motivation.

It is notable that the estimated direct effects of perceived competence on procrastination and persistence were higher than the indirect effects in most countries we investigated. Although SDT proposes that perceived competence leads to higher intrinsic motivation, Deci and Ryan [ 8 ] also argue that it affects all types of motivation and regulation, including less autonomous forms such as introjected and identified motivation, indicating that if the need for competence is not satisfied, all types of motivation are negatively affected. This may result in a general amotivation and lack of action. In our study, we only investigated intrinsic motivation as a mediator. For future research, it might be advantageous to further differentiate between different types of externally and internally controlled behavior. Furthermore, perceived competence increases when tasks are experienced as optimally challenging [ 7 , 54 ]. However, in order for instructors to provide the optimal level of difficulty and support needed, frequent communication with students is essential. Considering that data collection for the present study took place at a time of great uncertainty, when many countries had only transitioned to distance learning a few weeks prior, it is reasonable to assume that both structural support as well as communication and feedback mechanisms had not yet matured to a degree that would favor individualized and competency-based work.

However, our findings corroborate those from earlier studies insofar as they underline the associations between perceived competence and positive learning behavior (e.g., [ 19 ]), that is, lower procrastination [ 18 ] and higher persistence (e.g., [ 21 ]), even in an exceptional situation like pandemic-induced distance learning.

Turning to perceived autonomy, although the confidence intervals for the direct effects of perceived autonomy on procrastination (H6a) did span zero in most countries with smaller sample sizes, all effect estimates indicated a negative relation with procrastination. We expected these relationships from previous studies [ 18 , 23 ]; however, the effect might have been even more pronounced in the relatively autonomous learning situation of distance learning, where students usually have increased autonomy in deciding when, where, and how to learn. While this bears the risk of procrastination, it also comes with the opportunity to consciously delay less pressing tasks in favor of other, more important or urgent tasks (also called strategic delay ) [ 5 ], resulting in lower procrastination. In future studies, it might be beneficial to differentiate between passive forms of procrastination and active strategic delay in order to obtain more detailed information on the mechanisms behind this relationship. Direct effects of autonomy on persistence (H7a) were consistently positive. Students who are free to choose their preferred time and place to study may engage more with their studies and therefore be more persistent.

Indirect effects of perceived autonomy on procrastination mediated by intrinsic motivation (H4a) were negative in all but two countries (China and the USA), which is generally consistent with our hypothesis and in line with previous research (e.g., [ 23 ]). Additionally, we found a positive indirect effect of autonomy on persistence (H5a), indicating that autonomy and intrinsic motivation play a crucial role in students’ persistence in a distance learning setting. Based on our results, we conclude that perceived autonomy is negatively related to procrastination and positively related to persistence, and that this relationship is mediated by intrinsic motivation. It is worth noting that, unlike with perceived competence, the direct and indirect effects of perceived autonomy on the outcomes procrastination and persistence were similarly strong, suggesting that perceived autonomy is important not only as a driver of intrinsic motivation but also at a more direct level. It is important to make the best possible use of the opportunity for greater autonomy that distance learning offers. However, autonomy is not to be equated with a lack of structure; instead, learners should be given the opportunity to make their own decisions within certain framework conditions.

The applicability of self-determination theory across countries

Overall, the results of our mediation analysis for the separate countries support the claim posited by SDT that basic need satisfaction is essential for intrinsic motivation and learning across different countries and settings. In an exploratory analysis, we tested a fixed path model including all countries at once, in order to test whether a simplified general model would yield a similar amount of explained variance. For perceived autonomy and social relatedness, the model fit increased, whereas for perceived competence it decreased slightly compared to the multigroup model. However, all fixed path models exhibited adequate model fit. Considering that the circumstances in which distance learning took place in different countries varied to some degree (see also Limitations), these findings are a strong indicator for the universality of SDT.

Study strengths and limitations

Although the current study has several strengths, including a large sample size and data from multiple countries, three limitations must be considered. First, it must be noted that sample sizes varied widely across the 17 countries in our study, with one country above 6,000 (Austria), two above 1,000 (Finland and Sweden) and the rest ranging between 104 and 905. Random sampling effects are more problematic in smaller samples; hence, this large variation weakens our ability to conduct cross-country comparisons. At the same time, small sample sizes weaken the interpretability of results within each country; thus, our results for Austria, Finland and Sweden are considerably more robust than for the remaining fourteen countries. Additionally, two participating countries collected specific subsamples: In China, participants were only recruited from one university, a nursing school. In Estonia, only international exchange students were invited to participate. Nevertheless, with the exception of the unexpected positive direct relationship between social relatedness and procrastination, all observed divergent effects were non-significant. Indeed, this adds to the support for SDT’s claims to universality regarding the relationship between perceived autonomy, competence, and social relatedness with intrinsic motivation: Results in the included countries were, despite their differing subsamples, in line with the overall trend of results, supporting the idea that SDT applies equally to different groups of learners.

Second, due to the large number of countries in our sample and the overall volatility of the situation, learning circumstances were not identical for all participants. Due to factors such as COVID-19 case counts and national governments’ political priorities, lockdown measures varied in their strictness across settings. Some universities were fully closed, some allowed on-site teaching for particular groups (e.g., students in the middle of a laboratory internship), and some switched to distance learning but held exams on site (see S1 Table in S1 File for further information). Therefore, learning conditions were not as comparable as in a strict experimental setting. On the other hand, this strengthens the ecological validity of our study. The fact that the pattern of results was similar across contexts with certain variation in learning conditions further supports the universal applicability of SDT.

Finally, due to the novelty of the COVID-19 situation, some of the measures were newly developed for this study. Due to the need to react swiftly and collect data on the constantly evolving situation, it was not possible to conduct a comprehensive validation study of the instruments. Nevertheless, we were able to confirm the validity of our instruments in several ways, including cognitive interview testing, CFAs, CR, and measurement invariance testing.

Conclusion and future directions

In general, our results further support previous research on the relation between basic psychological need fulfilment and intrinsic motivation, as proposed in self-determination theory. It also extends past findings by applying this well-established theory to the new and unique situation of pandemic-induced distance learning across 17 different countries. Moreover, it underlines the importance of perceived autonomy and competence for procrastination and persistence in this setting. However, various other directions for further research remain to be pursued. While our findings point to the relevance of social relatedness for intrinsic motivation in addition to perceived competence and autonomy, further research should explore the specific mechanisms necessary to promote social connectedness in distance learning. Furthermore, in our study, we investigated intrinsic motivation, as the most autonomous form of motivation. Future research might address different types of externally and internally regulated motivation in order to further differentiate our results regarding the relations between basic need satisfaction and motivation. Finally, a longitudinal study design could provide deeper insights into the trajectory of need satisfaction, intrinsic motivation and learning behavior during extended periods of social distancing and could provide insights into potential forms of support implemented by teachers and coping mechanisms developed by students.

Supporting information

https://doi.org/10.1371/journal.pone.0257346.s001

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The effects of online education on academic success: A meta-analysis study

• Published: 06 September 2021
• Volume 27 , pages 429–450, ( 2022 )

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• Hakan Ulum   ORCID: orcid.org/0000-0002-1398-6935 1  

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The purpose of this study is to analyze the effect of online education, which has been extensively used on student achievement since the beginning of the pandemic. In line with this purpose, a meta-analysis of the related studies focusing on the effect of online education on students’ academic achievement in several countries between the years 2010 and 2021 was carried out. Furthermore, this study will provide a source to assist future studies with comparing the effect of online education on academic achievement before and after the pandemic. This meta-analysis study consists of 27 studies in total. The meta-analysis involves the studies conducted in the USA, Taiwan, Turkey, China, Philippines, Ireland, and Georgia. The studies included in the meta-analysis are experimental studies, and the total sample size is 1772. In the study, the funnel plot, Duval and Tweedie’s Trip and Fill Analysis, Orwin’s Safe N Analysis, and Egger’s Regression Test were utilized to determine the publication bias, which has been found to be quite low. Besides, Hedge’s g statistic was employed to measure the effect size for the difference between the means performed in accordance with the random effects model. The results of the study show that the effect size of online education on academic achievement is on a medium level. The heterogeneity test results of the meta-analysis study display that the effect size does not differ in terms of class level, country, online education approaches, and lecture moderators.

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1 Introduction

Information and communication technologies have become a powerful force in transforming the educational settings around the world. The pandemic has been an important factor in transferring traditional physical classrooms settings through adopting information and communication technologies and has also accelerated the transformation. The literature supports that learning environments connected to information and communication technologies highly satisfy students. Therefore, we need to keep interest in technology-based learning environments. Clearly, technology has had a huge impact on young people's online lives. This digital revolution can synergize the educational ambitions and interests of digitally addicted students. In essence, COVID-19 has provided us with an opportunity to embrace online learning as education systems have to keep up with the rapid emergence of new technologies.

Information and communication technologies that have an effect on all spheres of life are also actively included in the education field. With the recent developments, using technology in education has become inevitable due to personal and social reasons (Usta, 2011a ). Online education may be given as an example of using information and communication technologies as a consequence of the technological developments. Also, it is crystal clear that online learning is a popular way of obtaining instruction (Demiralay et al., 2016 ; Pillay et al., 2007 ), which is defined by Horton ( 2000 ) as a way of education that is performed through a web browser or an online application without requiring an extra software or a learning source. Furthermore, online learning is described as a way of utilizing the internet to obtain the related learning sources during the learning process, to interact with the content, the teacher, and other learners, as well as to get support throughout the learning process (Ally, 2004 ). Online learning has such benefits as learning independently at any time and place (Vrasidas & MsIsaac, 2000 ), granting facility (Poole, 2000 ), flexibility (Chizmar & Walbert, 1999 ), self-regulation skills (Usta, 2011b ), learning with collaboration, and opportunity to plan self-learning process.

Even though online education practices have not been comprehensive as it is now, internet and computers have been used in education as alternative learning tools in correlation with the advances in technology. The first distance education attempt in the world was initiated by the ‘Steno Courses’ announcement published in Boston newspaper in 1728. Furthermore, in the nineteenth century, Sweden University started the “Correspondence Composition Courses” for women, and University Correspondence College was afterwards founded for the correspondence courses in 1843 (Arat & Bakan, 2011 ). Recently, distance education has been performed through computers, assisted by the facilities of the internet technologies, and soon, it has evolved into a mobile education practice that is emanating from progress in the speed of internet connection, and the development of mobile devices.

With the emergence of pandemic (Covid-19), face to face education has almost been put to a halt, and online education has gained significant importance. The Microsoft management team declared to have 750 users involved in the online education activities on the 10 th March, just before the pandemic; however, on March 24, they informed that the number of users increased significantly, reaching the number of 138,698 users (OECD, 2020 ). This event supports the view that it is better to commonly use online education rather than using it as a traditional alternative educational tool when students do not have the opportunity to have a face to face education (Geostat, 2019 ). The period of Covid-19 pandemic has emerged as a sudden state of having limited opportunities. Face to face education has stopped in this period for a long time. The global spread of Covid-19 affected more than 850 million students all around the world, and it caused the suspension of face to face education. Different countries have proposed several solutions in order to maintain the education process during the pandemic. Schools have had to change their curriculum, and many countries supported the online education practices soon after the pandemic. In other words, traditional education gave its way to online education practices. At least 96 countries have been motivated to access online libraries, TV broadcasts, instructions, sources, video lectures, and online channels (UNESCO, 2020 ). In such a painful period, educational institutions went through online education practices by the help of huge companies such as Microsoft, Google, Zoom, Skype, FaceTime, and Slack. Thus, online education has been discussed in the education agenda more intensively than ever before.

Although online education approaches were not used as comprehensively as it has been used recently, it was utilized as an alternative learning approach in education for a long time in parallel with the development of technology, internet and computers. The academic achievement of the students is often aimed to be promoted by employing online education approaches. In this regard, academicians in various countries have conducted many studies on the evaluation of online education approaches and published the related results. However, the accumulation of scientific data on online education approaches creates difficulties in keeping, organizing and synthesizing the findings. In this research area, studies are being conducted at an increasing rate making it difficult for scientists to be aware of all the research outside of their ​​expertise. Another problem encountered in the related study area is that online education studies are repetitive. Studies often utilize slightly different methods, measures, and/or examples to avoid duplication. This erroneous approach makes it difficult to distinguish between significant differences in the related results. In other words, if there are significant differences in the results of the studies, it may be difficult to express what variety explains the differences in these results. One obvious solution to these problems is to systematically review the results of various studies and uncover the sources. One method of performing such systematic syntheses is the application of meta-analysis which is a methodological and statistical approach to draw conclusions from the literature. At this point, how effective online education applications are in increasing the academic success is an important detail. Has online education, which is likely to be encountered frequently in the continuing pandemic period, been successful in the last ten years? If successful, how much was the impact? Did different variables have an impact on this effect? Academics across the globe have carried out studies on the evaluation of online education platforms and publishing the related results (Chiao et al., 2018 ). It is quite important to evaluate the results of the studies that have been published up until now, and that will be published in the future. Has the online education been successful? If it has been, how big is the impact? Do the different variables affect this impact? What should we consider in the next coming online education practices? These questions have all motivated us to carry out this study. We have conducted a comprehensive meta-analysis study that tries to provide a discussion platform on how to develop efficient online programs for educators and policy makers by reviewing the related studies on online education, presenting the effect size, and revealing the effect of diverse variables on the general impact.

There have been many critical discussions and comprehensive studies on the differences between online and face to face learning; however, the focus of this paper is different in the sense that it clarifies the magnitude of the effect of online education and teaching process, and it represents what factors should be controlled to help increase the effect size. Indeed, the purpose here is to provide conscious decisions in the implementation of the online education process.

The general impact of online education on the academic achievement will be discovered in the study. Therefore, this will provide an opportunity to get a general overview of the online education which has been practiced and discussed intensively in the pandemic period. Moreover, the general impact of online education on academic achievement will be analyzed, considering different variables. In other words, the current study will allow to totally evaluate the study results from the related literature, and to analyze the results considering several cultures, lectures, and class levels. Considering all the related points, this study seeks to answer the following research questions:

What is the effect size of online education on academic achievement?

How do the effect sizes of online education on academic achievement change according to the moderator variable of the country?

How do the effect sizes of online education on academic achievement change according to the moderator variable of the class level?

How do the effect sizes of online education on academic achievement change according to the moderator variable of the lecture?

How do the effect sizes of online education on academic achievement change according to the moderator variable of the online education approaches?

This study aims at determining the effect size of online education, which has been highly used since the beginning of the pandemic, on students’ academic achievement in different courses by using a meta-analysis method. Meta-analysis is a synthesis method that enables gathering of several study results accurately and efficiently, and getting the total results in the end (Tsagris & Fragkos, 2018 ).

2.1 Selecting and coding the data (studies)

The required literature for the meta-analysis study was reviewed in July, 2020, and the follow-up review was conducted in September, 2020. The purpose of the follow-up review was to include the studies which were published in the conduction period of this study, and which met the related inclusion criteria. However, no study was encountered to be included in the follow-up review.

In order to access the studies in the meta-analysis, the databases of Web of Science, ERIC, and SCOPUS were reviewed by utilizing the keywords ‘online learning and online education’. Not every database has a search engine that grants access to the studies by writing the keywords, and this obstacle was considered to be an important problem to be overcome. Therefore, a platform that has a special design was utilized by the researcher. With this purpose, through the open access system of Cukurova University Library, detailed reviews were practiced using EBSCO Information Services (EBSCO) that allow reviewing the whole collection of research through a sole searching box. Since the fundamental variables of this study are online education and online learning, the literature was systematically reviewed in the related databases (Web of Science, ERIC, and SCOPUS) by referring to the keywords. Within this scope, 225 articles were accessed, and the studies were included in the coding key list formed by the researcher. The name of the researchers, the year, the database (Web of Science, ERIC, and SCOPUS), the sample group and size, the lectures that the academic achievement was tested in, the country that the study was conducted in, and the class levels were all included in this coding key.

The following criteria were identified to include 225 research studies which were coded based on the theoretical basis of the meta-analysis study: (1) The studies should be published in the refereed journals between the years 2020 and 2021, (2) The studies should be experimental studies that try to determine the effect of online education and online learning on academic achievement, (3) The values of the stated variables or the required statistics to calculate these values should be stated in the results of the studies, and (4) The sample group of the study should be at a primary education level. These criteria were also used as the exclusion criteria in the sense that the studies that do not meet the required criteria were not included in the present study.

After the inclusion criteria were determined, a systematic review process was conducted, following the year criterion of the study by means of EBSCO. Within this scope, 290,365 studies that analyze the effect of online education and online learning on academic achievement were accordingly accessed. The database (Web of Science, ERIC, and SCOPUS) was also used as a filter by analyzing the inclusion criteria. Hence, the number of the studies that were analyzed was 58,616. Afterwards, the keyword ‘primary education’ was used as the filter and the number of studies included in the study decreased to 3152. Lastly, the literature was reviewed by using the keyword ‘academic achievement’ and 225 studies were accessed. All the information of 225 articles was included in the coding key.

It is necessary for the coders to review the related studies accurately and control the validity, safety, and accuracy of the studies (Stewart & Kamins, 2001 ). Within this scope, the studies that were determined based on the variables used in this study were first reviewed by three researchers from primary education field, then the accessed studies were combined and processed in the coding key by the researcher. All these studies that were processed in the coding key were analyzed in accordance with the inclusion criteria by all the researchers in the meetings, and it was decided that 27 studies met the inclusion criteria (Atici & Polat, 2010 ; Carreon, 2018 ; Ceylan & Elitok Kesici, 2017 ; Chae & Shin, 2016 ; Chiang et al. 2014 ; Ercan, 2014 ; Ercan et al., 2016 ; Gwo-Jen et al., 2018 ; Hayes & Stewart, 2016 ; Hwang et al., 2012 ; Kert et al., 2017 ; Lai & Chen, 2010 ; Lai et al., 2015 ; Meyers et al., 2015 ; Ravenel et al., 2014 ; Sung et al., 2016 ; Wang & Chen, 2013 ; Yu, 2019 ; Yu & Chen, 2014 ; Yu & Pan, 2014 ; Yu et al., 2010 ; Zhong et al., 2017 ). The data from the studies meeting the inclusion criteria were independently processed in the second coding key by three researchers, and consensus meetings were arranged for further discussion. After the meetings, researchers came to an agreement that the data were coded accurately and precisely. Having identified the effect sizes and heterogeneity of the study, moderator variables that will show the differences between the effect sizes were determined. The data related to the determined moderator variables were added to the coding key by three researchers, and a new consensus meeting was arranged. After the meeting, researchers came to an agreement that moderator variables were coded accurately and precisely.

2.2 Study group

27 studies are included in the meta-analysis. The total sample size of the studies that are included in the analysis is 1772. The characteristics of the studies included are given in Table 1 .

2.3 Publication bias

Publication bias is the low capability of published studies on a research subject to represent all completed studies on the same subject (Card, 2011 ; Littell et al., 2008 ). Similarly, publication bias is the state of having a relationship between the probability of the publication of a study on a subject, and the effect size and significance that it produces. Within this scope, publication bias may occur when the researchers do not want to publish the study as a result of failing to obtain the expected results, or not being approved by the scientific journals, and consequently not being included in the study synthesis (Makowski et al., 2019 ). The high possibility of publication bias in a meta-analysis study negatively affects (Pecoraro, 2018 ) the accuracy of the combined effect size, causing the average effect size to be reported differently than it should be (Borenstein et al., 2009 ). For this reason, the possibility of publication bias in the included studies was tested before determining the effect sizes of the relationships between the stated variables. The possibility of publication bias of this meta-analysis study was analyzed by using the funnel plot, Orwin’s Safe N Analysis, Duval and Tweedie’s Trip and Fill Analysis, and Egger’s Regression Test.

2.4 Selecting the model

After determining the probability of publication bias of this meta-analysis study, the statistical model used to calculate the effect sizes was selected. The main approaches used in the effect size calculations according to the differentiation level of inter-study variance are fixed and random effects models (Pigott, 2012 ). Fixed effects model refers to the homogeneity of the characteristics of combined studies apart from the sample sizes, while random effects model refers to the parameter diversity between the studies (Cumming, 2012 ). While calculating the average effect size in the random effects model (Deeks et al., 2008 ) that is based on the assumption that effect predictions of different studies are only the result of a similar distribution, it is necessary to consider several situations such as the effect size apart from the sample error of combined studies, characteristics of the participants, duration, scope, and pattern of the study (Littell et al., 2008 ). While deciding the model in the meta-analysis study, the assumptions on the sample characteristics of the studies included in the analysis and the inferences that the researcher aims to make should be taken into consideration. The fact that the sample characteristics of the studies conducted in the field of social sciences are affected by various parameters shows that using random effects model is more appropriate in this sense. Besides, it is stated that the inferences made with the random effects model are beyond the studies included in the meta-analysis (Field, 2003 ; Field & Gillett, 2010 ). Therefore, using random effects model also contributes to the generalization of research data. The specified criteria for the statistical model selection show that according to the nature of the meta-analysis study, the model should be selected just before the analysis (Borenstein et al., 2007 ; Littell et al., 2008 ). Within this framework, it was decided to make use of the random effects model, considering that the students who are the samples of the studies included in the meta-analysis are from different countries and cultures, the sample characteristics of the studies differ, and the patterns and scopes of the studies vary as well.

2.5 Heterogeneity

Meta-analysis facilitates analyzing the research subject with different parameters by showing the level of diversity between the included studies. Within this frame, whether there is a heterogeneous distribution between the studies included in the study or not has been evaluated in the present study. The heterogeneity of the studies combined in this meta-analysis study has been determined through Q and I 2 tests. Q test evaluates the random distribution probability of the differences between the observed results (Deeks et al., 2008 ). Q value exceeding 2 value calculated according to the degree of freedom and significance, indicates the heterogeneity of the combined effect sizes (Card, 2011 ). I 2 test, which is the complementary of the Q test, shows the heterogeneity amount of the effect sizes (Cleophas & Zwinderman, 2017 ). I 2 value being higher than 75% is explained as high level of heterogeneity.

In case of encountering heterogeneity in the studies included in the meta-analysis, the reasons of heterogeneity can be analyzed by referring to the study characteristics. The study characteristics which may be related to the heterogeneity between the included studies can be interpreted through subgroup analysis or meta-regression analysis (Deeks et al., 2008 ). While determining the moderator variables, the sufficiency of the number of variables, the relationship between the moderators, and the condition to explain the differences between the results of the studies have all been considered in the present study. Within this scope, it was predicted in this meta-analysis study that the heterogeneity can be explained with the country, class level, and lecture moderator variables of the study in terms of the effect of online education, which has been highly used since the beginning of the pandemic, and it has an impact on the students’ academic achievement in different lectures. Some subgroups were evaluated and categorized together, considering that the number of effect sizes of the sub-dimensions of the specified variables is not sufficient to perform moderator analysis (e.g. the countries where the studies were conducted).

2.6 Interpreting the effect sizes

Effect size is a factor that shows how much the independent variable affects the dependent variable positively or negatively in each included study in the meta-analysis (Dinçer, 2014 ). While interpreting the effect sizes obtained from the meta-analysis, the classifications of Cohen et al. ( 2007 ) have been utilized. The case of differentiating the specified relationships of the situation of the country, class level, and school subject variables of the study has been identified through the Q test, degree of freedom, and p significance value Fig.  1 and 2 .

3 Findings and results

The purpose of this study is to determine the effect size of online education on academic achievement. Before determining the effect sizes in the study, the probability of publication bias of this meta-analysis study was analyzed by using the funnel plot, Orwin’s Safe N Analysis, Duval and Tweedie’s Trip and Fill Analysis, and Egger’s Regression Test.

When the funnel plots are examined, it is seen that the studies included in the analysis are distributed symmetrically on both sides of the combined effect size axis, and they are generally collected in the middle and lower sections. The probability of publication bias is low according to the plots. However, since the results of the funnel scatter plots may cause subjective interpretations, they have been supported by additional analyses (Littell et al., 2008 ). Therefore, in order to provide an extra proof for the probability of publication bias, it has been analyzed through Orwin’s Safe N Analysis, Duval and Tweedie’s Trip and Fill Analysis, and Egger’s Regression Test (Table 2 ).

Table 2 consists of the results of the rates of publication bias probability before counting the effect size of online education on academic achievement. According to the table, Orwin Safe N analysis results show that it is not necessary to add new studies to the meta-analysis in order for Hedges g to reach a value outside the range of ± 0.01. The Duval and Tweedie test shows that excluding the studies that negatively affect the symmetry of the funnel scatter plots for each meta-analysis or adding their exact symmetrical equivalents does not significantly differentiate the calculated effect size. The insignificance of the Egger tests results reveals that there is no publication bias in the meta-analysis study. The results of the analysis indicate the high internal validity of the effect sizes and the adequacy of representing the studies conducted on the relevant subject.

In this study, it was aimed to determine the effect size of online education on academic achievement after testing the publication bias. In line with the first purpose of the study, the forest graph regarding the effect size of online education on academic achievement is shown in Fig.  3 , and the statistics regarding the effect size are given in Table 3 .

The flow chart of the scanning and selection process of the studies

Funnel plot graphics representing the effect size of the effects of online education on academic success

Forest graph related to the effect size of online education on academic success

The square symbols in the forest graph in Fig.  3 represent the effect sizes, while the horizontal lines show the intervals in 95% confidence of the effect sizes, and the diamond symbol shows the overall effect size. When the forest graph is analyzed, it is seen that the lower and upper limits of the combined effect sizes are generally close to each other, and the study loads are similar. This similarity in terms of study loads indicates the similarity of the contribution of the combined studies to the overall effect size.

Figure  3 clearly represents that the study of Liu and others (Liu et al., 2018 ) has the lowest, and the study of Ercan and Bilen ( 2014 ) has the highest effect sizes. The forest graph shows that all the combined studies and the overall effect are positive. Furthermore, it is simply understood from the forest graph in Fig.  3 and the effect size statistics in Table 3 that the results of the meta-analysis study conducted with 27 studies and analyzing the effect of online education on academic achievement illustrate that this relationship is on average level (= 0.409).

After the analysis of the effect size in the study, whether the studies included in the analysis are distributed heterogeneously or not has also been analyzed. The heterogeneity of the combined studies was determined through the Q and I 2 tests. As a result of the heterogeneity test, Q statistical value was calculated as 29.576. With 26 degrees of freedom at 95% significance level in the chi-square table, the critical value is accepted as 38.885. The Q statistical value (29.576) counted in this study is lower than the critical value of 38.885. The I 2 value, which is the complementary of the Q statistics, is 12.100%. This value indicates that the accurate heterogeneity or the total variability that can be attributed to variability between the studies is 12%. Besides, p value is higher than (0.285) p = 0.05. All these values [Q (26) = 29.579, p = 0.285; I2 = 12.100] indicate that there is a homogeneous distribution between the effect sizes, and fixed effects model should be used to interpret these effect sizes. However, some researchers argue that even if the heterogeneity is low, it should be evaluated based on the random effects model (Borenstein et al., 2007 ). Therefore, this study gives information about both models. The heterogeneity of the combined studies has been attempted to be explained with the characteristics of the studies included in the analysis. In this context, the final purpose of the study is to determine the effect of the country, academic level, and year variables on the findings. Accordingly, the statistics regarding the comparison of the stated relations according to the countries where the studies were conducted are given in Table 4 .

As seen in Table 4 , the effect of online education on academic achievement does not differ significantly according to the countries where the studies were conducted in. Q test results indicate the heterogeneity of the relationships between the variables in terms of countries where the studies were conducted in. According to the table, the effect of online education on academic achievement was reported as the highest in other countries, and the lowest in the US. The statistics regarding the comparison of the stated relations according to the class levels are given in Table 5 .

As seen in Table 5 , the effect of online education on academic achievement does not differ according to the class level. However, the effect of online education on academic achievement is the highest in the 4 th class. The statistics regarding the comparison of the stated relations according to the class levels are given in Table 6 .

As seen in Table 6 , the effect of online education on academic achievement does not differ according to the school subjects included in the studies. However, the effect of online education on academic achievement is the highest in ICT subject.

The obtained effect size in the study was formed as a result of the findings attained from primary studies conducted in 7 different countries. In addition, these studies are the ones on different approaches to online education (online learning environments, social networks, blended learning, etc.). In this respect, the results may raise some questions about the validity and generalizability of the results of the study. However, the moderator analyzes, whether for the country variable or for the approaches covered by online education, did not create significant differences in terms of the effect sizes. If significant differences were to occur in terms of effect sizes, we could say that the comparisons we will make by comparing countries under the umbrella of online education would raise doubts in terms of generalizability. Moreover, no study has been found in the literature that is not based on a special approach or does not contain a specific technique conducted under the name of online education alone. For instance, one of the commonly used definitions is blended education which is defined as an educational model in which online education is combined with traditional education method (Colis & Moonen, 2001 ). Similarly, Rasmussen ( 2003 ) defines blended learning as “a distance education method that combines technology (high technology such as television, internet, or low technology such as voice e-mail, conferences) with traditional education and training.” Further, Kerres and Witt (2003) define blended learning as “combining face-to-face learning with technology-assisted learning.” As it is clearly observed, online education, which has a wider scope, includes many approaches.

As seen in Table 7 , the effect of online education on academic achievement does not differ according to online education approaches included in the studies. However, the effect of online education on academic achievement is the highest in Web Based Problem Solving Approach.

4 Conclusions and discussion

Considering the developments during the pandemics, it is thought that the diversity in online education applications as an interdisciplinary pragmatist field will increase, and the learning content and processes will be enriched with the integration of new technologies into online education processes. Another prediction is that more flexible and accessible learning opportunities will be created in online education processes, and in this way, lifelong learning processes will be strengthened. As a result, it is predicted that in the near future, online education and even digital learning with a newer name will turn into the main ground of education instead of being an alternative or having a support function in face-to-face learning. The lessons learned from the early period online learning experience, which was passed with rapid adaptation due to the Covid19 epidemic, will serve to develop this method all over the world, and in the near future, online learning will become the main learning structure through increasing its functionality with the contribution of new technologies and systems. If we look at it from this point of view, there is a necessity to strengthen online education.

In this study, the effect of online learning on academic achievement is at a moderate level. To increase this effect, the implementation of online learning requires support from teachers to prepare learning materials, to design learning appropriately, and to utilize various digital-based media such as websites, software technology and various other tools to support the effectiveness of online learning (Rolisca & Achadiyah, 2014 ). According to research conducted by Rahayu et al. ( 2017 ), it has been proven that the use of various types of software increases the effectiveness and quality of online learning. Implementation of online learning can affect students' ability to adapt to technological developments in that it makes students use various learning resources on the internet to access various types of information, and enables them to get used to performing inquiry learning and active learning (Hart et al., 2019 ; Prestiadi et al., 2019 ). In addition, there may be many reasons for the low level of effect in this study. The moderator variables examined in this study could be a guide in increasing the level of practical effect. However, the effect size did not differ significantly for all moderator variables. Different moderator analyzes can be evaluated in order to increase the level of impact of online education on academic success. If confounding variables that significantly change the effect level are detected, it can be spoken more precisely in order to increase this level. In addition to the technical and financial problems, the level of impact will increase if a few other difficulties are eliminated such as students, lack of interaction with the instructor, response time, and lack of traditional classroom socialization.

In addition, COVID-19 pandemic related social distancing has posed extreme difficulties for all stakeholders to get online as they have to work in time constraints and resource constraints. Adopting the online learning environment is not just a technical issue, it is a pedagogical and instructive challenge as well. Therefore, extensive preparation of teaching materials, curriculum, and assessment is vital in online education. Technology is the delivery tool and requires close cross-collaboration between teaching, content and technology teams (CoSN, 2020 ).

Online education applications have been used for many years. However, it has come to the fore more during the pandemic process. This result of necessity has brought with it the discussion of using online education instead of traditional education methods in the future. However, with this research, it has been revealed that online education applications are moderately effective. The use of online education instead of face-to-face education applications can only be possible with an increase in the level of success. This may have been possible with the experience and knowledge gained during the pandemic process. Therefore, the meta-analysis of experimental studies conducted in the coming years will guide us. In this context, experimental studies using online education applications should be analyzed well. It would be useful to identify variables that can change the level of impacts with different moderators. Moderator analyzes are valuable in meta-analysis studies (for example, the role of moderators in Karl Pearson's typhoid vaccine studies). In this context, each analysis study sheds light on future studies. In meta-analyses to be made about online education, it would be beneficial to go beyond the moderators determined in this study. Thus, the contribution of similar studies to the field will increase more.

The purpose of this study is to determine the effect of online education on academic achievement. In line with this purpose, the studies that analyze the effect of online education approaches on academic achievement have been included in the meta-analysis. The total sample size of the studies included in the meta-analysis is 1772. While the studies included in the meta-analysis were conducted in the US, Taiwan, Turkey, China, Philippines, Ireland, and Georgia, the studies carried out in Europe could not be reached. The reason may be attributed to that there may be more use of quantitative research methods from a positivist perspective in the countries with an American academic tradition. As a result of the study, it was found out that the effect size of online education on academic achievement (g = 0.409) was moderate. In the studies included in the present research, we found that online education approaches were more effective than traditional ones. However, contrary to the present study, the analysis of comparisons between online and traditional education in some studies shows that face-to-face traditional learning is still considered effective compared to online learning (Ahmad et al., 2016 ; Hamdani & Priatna, 2020 ; Wei & Chou, 2020 ). Online education has advantages and disadvantages. The advantages of online learning compared to face-to-face learning in the classroom is the flexibility of learning time in online learning, the learning time does not include a single program, and it can be shaped according to circumstances (Lai et al., 2019 ). The next advantage is the ease of collecting assignments for students, as these can be done without having to talk to the teacher. Despite this, online education has several weaknesses, such as students having difficulty in understanding the material, teachers' inability to control students, and students’ still having difficulty interacting with teachers in case of internet network cuts (Swan, 2007 ). According to Astuti et al ( 2019 ), face-to-face education method is still considered better by students than e-learning because it is easier to understand the material and easier to interact with teachers. The results of the study illustrated that the effect size (g = 0.409) of online education on academic achievement is of medium level. Therefore, the results of the moderator analysis showed that the effect of online education on academic achievement does not differ in terms of country, lecture, class level, and online education approaches variables. After analyzing the literature, several meta-analyses on online education were published (Bernard et al., 2004 ; Machtmes & Asher, 2000 ; Zhao et al., 2005 ). Typically, these meta-analyzes also include the studies of older generation technologies such as audio, video, or satellite transmission. One of the most comprehensive studies on online education was conducted by Bernard et al. ( 2004 ). In this study, 699 independent effect sizes of 232 studies published from 1985 to 2001 were analyzed, and face-to-face education was compared to online education, with respect to success criteria and attitudes of various learners from young children to adults. In this meta-analysis, an overall effect size close to zero was found for the students' achievement (g +  = 0.01).

In another meta-analysis study carried out by Zhao et al. ( 2005 ), 98 effect sizes were examined, including 51 studies on online education conducted between 1996 and 2002. According to the study of Bernard et al. ( 2004 ), this meta-analysis focuses on the activities done in online education lectures. As a result of the research, an overall effect size close to zero was found for online education utilizing more than one generation technology for students at different levels. However, the salient point of the meta-analysis study of Zhao et al. is that it takes the average of different types of results used in a study to calculate an overall effect size. This practice is problematic because the factors that develop one type of learner outcome (e.g. learner rehabilitation), particularly course characteristics and practices, may be quite different from those that develop another type of outcome (e.g. learner's achievement), and it may even cause damage to the latter outcome. While mixing the studies with different types of results, this implementation may obscure the relationship between practices and learning.

Some meta-analytical studies have focused on the effectiveness of the new generation distance learning courses accessed through the internet for specific student populations. For instance, Sitzmann and others (Sitzmann et al., 2006 ) reviewed 96 studies published from 1996 to 2005, comparing web-based education of job-related knowledge or skills with face-to-face one. The researchers found that web-based education in general was slightly more effective than face-to-face education, but it is insufficient in terms of applicability ("knowing how to apply"). In addition, Sitzmann et al. ( 2006 ) revealed that Internet-based education has a positive effect on theoretical knowledge in quasi-experimental studies; however, it positively affects face-to-face education in experimental studies performed by random assignment. This moderator analysis emphasizes the need to pay attention to the factors of designs of the studies included in the meta-analysis. The designs of the studies included in this meta-analysis study were ignored. This can be presented as a suggestion to the new studies that will be conducted.

Another meta-analysis study was conducted by Cavanaugh et al. ( 2004 ), in which they focused on online education. In this study on internet-based distance education programs for students under 12 years of age, the researchers combined 116 results from 14 studies published between 1999 and 2004 to calculate an overall effect that was not statistically different from zero. The moderator analysis carried out in this study showed that there was no significant factor affecting the students' success. This meta-analysis used multiple results of the same study, ignoring the fact that different results of the same student would not be independent from each other.

In conclusion, some meta-analytical studies analyzed the consequences of online education for a wide range of students (Bernard et al., 2004 ; Zhao et al., 2005 ), and the effect sizes were generally low in these studies. Furthermore, none of the large-scale meta-analyzes considered the moderators, database quality standards or class levels in the selection of the studies, while some of them just referred to the country and lecture moderators. Advances in internet-based learning tools, the pandemic process, and increasing popularity in different learning contexts have required a precise meta-analysis of students' learning outcomes through online learning. Previous meta-analysis studies were typically based on the studies, involving narrow range of confounding variables. In the present study, common but significant moderators such as class level and lectures during the pandemic process were discussed. For instance, the problems have been experienced especially in terms of eligibility of class levels in online education platforms during the pandemic process. It was found that there is a need to study and make suggestions on whether online education can meet the needs of teachers and students.

Besides, the main forms of online education in the past were to watch the open lectures of famous universities and educational videos of institutions. In addition, online education is mainly a classroom-based teaching implemented by teachers in their own schools during the pandemic period, which is an extension of the original school education. This meta-analysis study will stand as a source to compare the effect size of the online education forms of the past decade with what is done today, and what will be done in the future.

Lastly, the heterogeneity test results of the meta-analysis study display that the effect size does not differ in terms of class level, country, online education approaches, and lecture moderators.

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Home — Essay Samples — Education — Online Vs. Traditional Classes — New Reality: Online Distance Learning in Philippines

New Reality: Online Distance Learning in Philippines

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Online learning, advantages and disadvantages of online learning, philippines and online learning.

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Distance learning, as it is known to many students, is the online learning and teaching programs offered by world class institutions of learning. Unlike traditional classroom education, students are virtually enrolled in their programs and respective classes online. Statistical data taken from the leading institutions of higher learning in the United States and United Kingdom show that the number of students registering for distance learning programs is increasing day and night. In the United States alone, the number of students taking courses through distance education has since risen from 3.9 million in 2010 to approximately 8.9 million students in 2013. Whereas distance learning is applauded for its inherent ability to reduce illiteracy amongst the Americans through promotion of cheaper internet enabled computer programs, the mode of education has been found out to compromise the quality of learning outcomes.

Although traditional classroom education remains the mode of learning which is widely practiced and offered by most of the institutions such as colleges and universities across the world, distance learning is increasingly becoming more popular in the age of information technology. Distance learning is no longer an alternative mode of learning to traditional education but a preferred mode of learning across the world. The most recent survey conducted among college students revealed that 80% of the college and university students are in favor of distance learning because of its flexibility. The subsequent popularity of distance learning is attributed to fact it is the only mode of education that gives students freedom to choose the convenient time of the night or day to take classes. Unlike the subjective traditional face-to-face education with its fixed teaching and learning schedule, the highly individualized distance learning gives students full freedom on when and what they want to learn.

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Secondly, distance learning reaches the highest number of students within the shortest period of time as opposed to the traditional face-to-face learning. The number of students graduating from various institutions of education after undertaking distance learning programs is increasing every year. Statistics show that about there are about 9 million students registered for various distance learning programs in the United States last year and the figures are on an upward trend. The flaring number of students opting for the distance learning implies that larger segments of illiterate populations are effectively reached. Consequently, the mode of learning has proven to the most effective and convenient method of combating higher rates of illiteracy across the continents. Apart from its accessibility, multitudes of learners successfully complete their courses because distance learning programs are far cheaper than compared to traditional learning programs.

Suffice it to say, there is substantial evidence that distance learning has proven to be more effective tool in promoting literacy amongst the adult populations. It is more suitable for the adult learners who are either in full time employment or committed in their domestic duties thus, cannot manage to fit in traditional mode of education with fixed schedule. With the full knowledge that the internet-enabled mode of learning takes place in the comfort of living rooms, many mature learners find distance learning more palatable because it upholds their confidentiality and privacy. In this regard, the electronic mode of learning renders education a private affair compared to traditional education that makes education a public affair. It therefore goes without saying that distance learning has adequately counteracted shame that most adult students face in their efforts to access education programs in traditional institutions.

Most importantly, distance learning programs are designed to meet the diverse needs of learners like no other. For instance, the programs are scheduled to ensure that learners who are in active job with tight work schedule, parents taking care of their children, and persons living outside the catchment areas of the learning institutions can create time and study at their own convenient time. Both the young and old; men and women; the rich and poor are satisfactorily accommodated by the distance learning education programs. In addition to this, distance learning educational programs are designed in a way that individual learners can study at their pace; students are at liberty to start, break and resume personalized studies at their own discretion. This rare phenomenon gives distance learning an upper hand above traditional classroom face-to-face learning.

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Despite the numerous gains and advantages that come with the distance education on the students' side, it has been established that learning at home behind an internet-enabled computer cannot replace face to face education existing in institutions of higher learning such as universities and colleges. On many occasions, educational experts have raised their concern on the effectiveness of distance learning on pedagogical delivery of complex concepts especially in science-oriented subjects such as chemistry and mathematics. According to the latest research finding, distance learning is limited to the kind of courses they offer to students. For instance, technical courses such as engineering, applied technology and mechanics that require the instructors to impart psychomotor and manipulative skills to learners could not be delivered via distance learning programs. The much desired delivery of technical courses of this nature is therefore an exclusive reserve of the traditional face-to-face education. At the end of it all, It emerges that traditional face-to-face education produces better results in technical subjects that requires practical skills.

It has been proven over and over again that there are a lot of difficulties in self-directed learning which is demanded by the online education. Many a times, students undertaking online courses do not have set schedule for their studies thus, leaving much room for distracters that altogether work to the detriment of students' academic performance. Taking into consideration that students are left to study on their own while at the same time being least supervised by their course instructors, most of the students do not see the need to delve into their studies before the examination period. The reduced contact hours between instructors and students due to exclusive use of virtual interactive platform, instructors will not be able to constantly monitor students' learning progress. In this case, the outcome of the learning process in learners is compromised because instructors often fail to identify students' weaknesses in distance learning. On the other hand, instructors quickly identify individual learner's areas of weaknesses and fix them in time to bring about desirable learning outcome in learners.

Lack of the physical interaction between students and course instructors in the distance learning programs leads to gross instructional misunderstanding. This could have unbearable detrimental effects on the accuracy and effectiveness with which learning objectives are met. Contrary to the traditional face-to-face form of education, distance learning deprives students of the adequate opportunity to be in constant contact with their course instructors. Therefore, they are bound to experience instruction breakdown from the internet learning interface. It is imperative to note, however, that distance education leads to increased incidences of cheating alongside other host of irregularities in online examinations.

In conclusion, distance learning has proven to be more convenient, cheaper and confidential learner-friendly mode of learning. The global enrolment rates in the institutions of higher learning have shot up tremendously since the rolling out of distance learning educational programs. Judging from the ongoing trends, it is evident that distance learning will continue to gain prominence over the traditional face-to-face education.

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Impact of Online Classes on Students Essay

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• Introduction
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Background study

• Impacts of online education

Introduction to Online Education

Online learning is one of the new innovative study methods that have been introduced in the pedagogy field. In the last few years, there has been a great shift in the training methods. Students can now learn remotely using the internet and computers.

Online learning comes in many forms and has been developing with the introduction of new technologies. Most universities, high schools, and other institutions in the world have all instituted this form of learning, and the student population in the online class is increasing fast. There has been a lot of research on the impacts of online education as compared to ordinary classroom education.

If the goal is to draw a conclusion of online education, considerable differences between the online learning environment and classroom environment should be acknowledged. In the former, teachers and students don’t meet physically as opposed to the latter, where they interact face to face. In this essay, the challenges and impact of online classes on students, teachers, and institutions involved were examined.

Thesis Statement about Online Classes

Thus, the thesis statement about online classes will be as follows:

Online learning has a positive impact on the learners, teachers, and the institution offering these courses.

Online learning or E learning is a term used to describe various learning environments that are conducted and supported by the use of computers and the internet. There are a number of definitions and terminologies that are used to describe online learning.

These include E learning, distance learning, and computer learning, among others (Anon, 2001). Distant learning is one of the terminologies used in E learning and encompasses all learning methods that are used to train students that are geographically away from the training school. Online learning, on the other hand, is used to describe all the learning methods that are supported by the Internet (Moore et al., 2011).

Another terminology that is used is E learning which most authors have described as a learning method that is supported by the use of computers, web-enabled communication, and the use of new technological tools that enhance communication (Spector, 2008). Other terminologies that are used to describe this form of online learning are virtual learning, collaborative learning, web-based learning, and computer-supported collaborative learning (Conrad, 2006).

Impacts of Online Classes on Students

Various studies and articles document the merits, demerits, and challenges of online studies. These studies show that online study is far beneficial to the students, teachers, and the institution in general and that the current challenges can be overcome through technological advancement and increasing efficiency of the learning process.

One of the key advantages of online learning is the ability of students to study in their own comfort. For a long time, students had to leave their comfort areas and attend lectures. This change in environment causes a lack of concentration in students. In contrast, E-learning enables the students to choose the best environment for study, and this promotes their ability to understand. As a result, students enjoy the learning process as compared to conventional classroom learning.

Another benefit is time and cost savings. Online students are able to study at home, and this saves them travel and accommodation costs. This is in contrast with the classroom environment, where learners have to pay for transport and accommodation costs as well as any other costs associated with the learning process.

Online study has been found to reduce the workload on the tutors. Most of the online notes and books are availed to the students, and this reduces the teacher’s workload. Due to the availability of teaching materials online, tutors are not required to search for materials. Teachers usually prepare lessons, and this reduces the task of training students over and over again.

Accessibility to learning materials is another benefit of online learning. Students participating in online study have unlimited access to learning materials, which gives them the ability to study effectively and efficiently. On the other hand, students in the classroom environment have to take notes as the lecture progress, and these notes may not be accurate as compared to the materials uploaded on the websites.

Unlimited resources are another advantage of online study. Traditionally, learning institutions were limited in the number of students that could study in the classroom environment. The limitations of facilities such as lecture theaters and teachers limited student enrollment in schools (Burgess & Russell, 2003).

However, with the advent of online studies, physical limitations imposed by classrooms, tutors, and other resources have been eliminated. A vast number of students can now study in the same institution and be able to access the learning materials online. The use of online media for training enables a vast number of students to access materials online, and this promotes the learning process.

Promoting online study has been found by most researchers to open the students to vast resources that are found on the internet. Most of the students in the classroom environment rely on the tutors’ notes and explanations for them to understand a given concept.

However, students using the web to study most of the time are likely to be exposed to the vast online educational resources that are available. This results in the students gaining a better understanding of the concept as opposed to those in the classroom environment (Berge & Giles, 2008).

An online study environment allows tutors to update their notes and other materials much faster as compared to the classroom environment. This ensures that the students receive up-to-date information on a given study area.

One of the main benefits of E-learning to institutions is the ability to provide training to a large number of students located in any corner of the world. These students are charged training fees, and this increases the money available to the institution. This extra income can be used to develop new educational facilities, and these will promote education further (Gilli et al., 2002).

Despite the many advantages that online study has in transforming the learning process, there are some challenges imposed by the method. One of the challenges is the technological limitations of the current computers, which affect the quality of the learning materials and the learning process in general.

Low download speed and slow internet connectivity affect the availability of learning materials. This problem is, however, been reduced through the application of new software and hardware elements that have high access speeds. This makes it easier to download learning materials and applications. As computing power increases, better and faster computers are being unveiled, and these will enable better access to online study facilities.

Another disadvantage of online learning as compared to the classroom environment is the lack of feedback from the students. In the classroom environment, students listen to the lecture and ask the tutors questions and clarifications any issues they didn’t understand. In the online environment, the response by the teacher may not be immediate, and students who don’t understand a given concept may find it hard to liaise with the teachers.

The problem is, however, been circumvented by the use of simple explanation methods, slideshows, and encouraging discussion forums between the teachers and students. In the discussion forums, students who don’t understand a concept can leave a comment or question, which will be answered by the tutor later.

Like any other form of learning, online studies have a number of benefits and challenges. It is, therefore, not logical to discredit online learning due to the negative impacts of this training method. Furthermore, the benefits of e-learning far outweigh the challenges.

Conclusion about Online Education

In culmination, a comparative study between classroom study and online study was carried out. The study was done by examining the findings recorded in books and journals on the applicability of online learning to students. The study revealed that online learning has many benefits as compared to conventional learning in the classroom environment.

Though online learning has several challenges, such as a lack of feedback from students and a lack of the proper technology to effectively conduct online learning, these limitations can be overcome by upgrading the E-Leaning systems and the use of online discussion forums and new web-based software.

In conclusion, online learning is beneficial to the students, tutors, and the institution offering these courses. I would therefore recommend that online learning be implemented in all learning institutions, and research on how to improve this learning process should be carried out.

Anon, C. (2001). E-learning is taking off in Europe. Industrial and Commercial Training , 33 (7), 280-282.

Berge, Z., & Giles, L. (2008). Implementing and sustaining e-learning in the workplace. International Journal of Web-Based Learning and Teaching Technologies , 3(3), 44-53.

Burgess, J. & Russell, J. (2003).The effectiveness of distance learning initiatives in organizations. Journal of Vocational Behaviour , 63 (2),289-303.

Conrad, D. (2006). E-Learning and social change, Perspectives on higher education in the digital age . New York: Nova Science Publishers.

Gilli, R., Pulcini, M., Tonchia, S. & Zavagno, M. (2002), E-learning: A strategic Instrument. International Journal of Business Performance Management , 4 (1), 2-4.

Moore, J. L., Camille, D. & Galyen, K. (2011). E-Learning, online learning and distance learning environments: Are they the same? Internet and Higher Education, 14(1), 129-135.

Spector, J., Merrill, M., Merrienboer, J. & Driscoll, M. P. (2008). Handbook of research on educational communications and technology (3rd ed.), New York: Lawrence Erlbaum Associates.

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