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Tuesday, June 23, 2026

Educational technology

From Wikipedia, the free encyclopedia
A student using an interactive whiteboard

Educational technology (often abbreviated as edtech) encompasses computer hardware, software, along with educational theories and practices, used to facilitate learning and teaching. When referred to by its abbreviation, "EdTech," it often denotes the industry of companies that develop educational technology. Scholars such as Tanner Mirrlees and Shahid Alvi (2019) have described the edtech industry as consisting largely of privately owned companies involved in producing and distributing educational technologies for commercial purposes.

In addition to practical educational experience, educational technology draws on theoretical knowledge from various disciplines such as communication, education, psychology, sociology, artificial intelligence, and computer science. It encompasses several domains, including learning theory, computer-based training, online learning, and mobile learning (m-learning).

Definition

The Association for Educational Communications and Technology (AECT) has defined educational technology as "the study and ethical practice of facilitating learning and improving performance by creating, using, and managing appropriate technological processes and resources". It denotes instructional technology as "the theory and practice of design, development, utilization, management, and evaluation of processes and resources for learning". As such, Educational technology refers to various valid and reliable applications of educational science, such as equipment, as well as processes and procedures that are derived from scientific research, and in a given context may refer to theoretical, algorithmic or heuristic processes: it does not necessarily refer only to physical technology. Educational technology is the process of integrating technology into education in a positive manner that promotes a more diverse and inclusive learning environment, enabling students to develop both technological literacy and subject-specific knowledge alongside their regular academic assignments.

Accordingly, there are several discrete aspects to describing the intellectual and technical development of educational technology:

Early 20th-century abacus used in a Danish elementary school

Educational technology is an inclusive term that refers to both the tools and processes used to support learning and teaching, as well as the theoretical foundations behind them. It is not limited to modern digital systems; instead, it encompasses any resource or method that improves instructional delivery, whether through blended learning, traditional face-to-face teaching, or online environments.

Professionals trained in this field are known as educational technologists, responsible for analysing, designing, developing and evaluating learning systems and tools to enhance educational outcomes. The term learning technologist is commonly used in the United Kingdom, while Canada and several other regions use the term interchangeably. Modern electronic educational technology plays a significant role in contemporary education systems. Educational technology encompasses e-learning, instructional technology, information and communication technology (ICT) in education, edtech, learning technology, multimedia learning, technology-enhanced learning (TEL), computer-based instruction (CBI), computer managed instruction, computer-based training (CBT), computer-assisted instruction or computer-aided instruction (CAI), internet-based training (IBT), flexible learning, web-based training (WBT), online education, digital educational collaboration, distributed learning, computer-mediated communication, cyber-learning, and multi-modal instruction, virtual education, personal learning environments, networked learning, virtual learning environments (VLE) (which are also called learning platforms), m-learning, and digital education.

Each of these numerous terms has had its advocates, who point up potential distinctive features. However, many terms and concepts in educational technology have been defined nebulously. For example, Singh and Thurman cite over 45 definitions for online learning. Moreover, Moore saw these terminologies as emphasizing particular features such as digitization approaches, components, or delivery methods rather than being fundamentally dissimilar in concept or principle. For example, m-learning emphasizes mobility, which allows for altered timing, location, accessibility, and context of learning; nevertheless, its purpose and conceptual principles are those of educational technology.

In practice, as technology has advanced, the particular "narrowly defined" terminological aspect that was initially emphasized by name has blended into the general field of educational technology. Initially, "virtual learning" as narrowly defined in a semantic sense implied entering an environmental simulation within a virtual world, for example, in treating posttraumatic stress disorder (PTSD). In practice, a "virtual education course" refers to any instructional course in which all, or at least a significant portion, is delivered by the Internet. "Virtual" is used in that broader way to describe a course that is not taught in a classroom face-to-face but "virtually" with people not having to go to the physical classroom to learn. Accordingly, virtual education refers to a form of distance learning in which course content is delivered using various methods such as course management applications, multimedia resources, and videoconferencing. Virtual education and simulated learning such as games or dissections, inspire students to connect classroom content to authentic situations.

Educational content, pervasively embedded in objects, is all around the learner, who may not even be conscious of the learning process. The combination of adaptive learning, using an individualized interface and materials, which accommodate to an individual, who thus receives personally differentiated instruction, with ubiquitous access to digital resources and learning opportunities in a range of places and at various times, has been termed smart learning. Smart learning is a component of the smart city concept.

History

19th-century classroom, Auckland

Helping people and children learn in ways that are easier, faster, more accurate, or less expensive can be traced back to the emergence of very early tools, such as paintings on cave walls. Various types of abacus have been used. Writing slates and blackboards have been used for at least a millennium. Since their introduction, books and pamphlets have played a prominent role in education. From the early twentieth century, duplicating machines such as the mimeograph and Gestetner stencil devices were used to produce short copy runs (typically 10–50 copies) for classroom or home use. The use of media for instructional purposes is generally traced back to the first decade of the 20th century with the introduction of educational films (the 1900s) and Sidney Pressey's mechanical teaching machines (1920s).

Cuisenaire rods

In the mid-1960s, Stanford University psychology professors, Patrick Suppes and Richard C. Atkinson, experimented with using computers to teach arithmetic and spelling via Teletypes to elementary school students in the Palo Alto Unified School District in California.

Online education originated from the University of Illinois in 1960. Although the internet would not be created for another decade, students were able to access class information with linked computer terminals. Online learning emerged in 1982 when the Western Behavioral Sciences Institute in La Jolla, California, opened its School of Management and Strategic Studies. The school employed computer conferencing through the New Jersey Institute of Technology's Electronic Information Exchange System (EIES) to deliver a distance education program to business executives. Starting in 1985, Connected Education offered the first totally online master's degree in media studies, through The New School in New York City, also via the EIES computer conferencing system. Subsequent courses were offered in 1986 by the Electronic University Network for DOS and Commodore 64 computers. In 2002, MIT began providing online classes free of charge. As of 2009, approximately 5.5  million students were taking at least one class online. Currently, one out of three college students takes at least one online course while in college. At DeVry University, out of all students that are earning a bachelor's degree, 80% earn two-thirds of their requirements online. Also, in 2014, 2.85 million students out of 5.8 million students that took courses online, took all of their courses online. From this information, it can be concluded that the number of students taking classes online is on a steady increase.

In 1971, Ivan Illich published a hugely influential book, Deschooling Society, in which he envisioned "learning webs" as a model for people to network the learning they needed. The 1970s and 1980s saw notable contributions in computer-based learning by Murray Turoff and Starr Roxanne Hiltz at the New Jersey Institute of Technology as well as developments at the University of Guelph in Canada. In the UK, the Council for Educational Technology supported the use of educational technology, in particular administering the government's National Development Programme in Computer Aided Learning (1973–1977) and the Microelectronics Education Programme (1980–1986).

Videoconferencing was an important forerunner to the educational technologies known today. This work was especially popular with museum education. Even in recent years, videoconferencing has risen in popularity to reach over 20,000 students across the United States and Canada in 2008–2009. Disadvantages of this form of educational technology are readily apparent: image and sound quality are often grainy or pixelated; videoconferencing requires setting up a type of mini-television studio within the museum for broadcast; space becomes an issue; and specialized equipment is required for both the provider and the participant.

The Open University in Britain and the University of British Columbia (where Web CT, now incorporated into Blackboard Inc., was first developed) began a revolution of using the Internet to deliver learning, making heavy use of web-based training, online distance learning, and online discussion between students. Practitioners such as Harasim (1995) put heavy emphasis on the use of learning networks.

By 1994, the first online high school had been founded. In 1997, Graziadei described criteria for evaluating products and developing technology-based courses that include being portable, replicable, scalable, affordable, and having a high probability of long-term cost-effectiveness.

Improved Internet functionality enabled new schemes of communication with multimedia or webcams. The National Center for Education Statistics estimates the number of K-12 students enrolled in online distance learning programs increased by 65% from 2002 to 2005, with greater flexibility, ease of communication between teacher and student, and quick lecture and assignment feedback.

According to a 2008 study conducted by the U.S. Department of Education, during the 2006–2007 academic year, about 66% of postsecondary public and private schools participating in student financial aid programs offered some distance learning courses; records show 77% of enrollment in for-credit courses with an online component. In 2008, the Council of Europe passed a statement endorsing e-learning's potential to drive equality and education improvements across the EU.

Computer-mediated communication (CMC) is between learners and instructors, mediated by the computer. In contrast, CBT/CBL usually means individualized (self-study) learning, while CMC involves educator/tutor facilitation and requires the scalarization of flexible learning activities. In addition, modern ICT provides education with tools for sustaining learning communities and associated knowledge management tasks.

Students growing up in this digital age have extensive exposure to a variety of media. Major high-tech companies have funded schools to provide them with the ability to teach their students through technology.

2015 was the first year that private nonprofit organizations enrolled more online students than for-profits, although public universities still enrolled the highest number of online students. In the fall of 2015, more than 6 million students enrolled in at least one online course.

In 2020, due to the COVID-19 pandemic, many schools across the world were forced to close, which left more and more grade-school students participating in online learning, and university-level students enrolling in online courses to enforce distance learning. Organizations such as Unesco have enlisted educational technology solutions to help schools facilitate distance education. The pandemic's extended lockdowns and focus on distance learning has attracted record-breaking amounts of venture capital to the ed-tech sector. In 2020, in the United States alone, ed-tech startups raised $1.78 billion in venture capital spanning 265 deals, compared to $1.32 billion in 2019.

Post pandemic, teachers use digital technologies less but still use them more than they did prior to the COVID-19 pandemic.

Theory

Behaviorism

This theoretical framework was developed in the early 20th century based on animal learning experiments by Ivan Pavlov, Edward Thorndike, Edward C. Tolman, Clark L. Hull, and B.F. Skinner. Many psychologists used these results to develop theories of human learning, but modern educators generally see behaviorism as one aspect of a holistic synthesis. Teaching in behaviorism has been linked to training, emphasizing animal learning experiments. Since behaviorism consists of the view of teaching people how to do something with rewards and punishments, it is related to training people.

B.F. Skinner wrote extensively on improvements in teaching based on his functional analysis of verbal behavior and wrote "The Technology of Teaching", an attempt to dispel the myths underlying contemporary education as well as promote his system he called programmed instruction. Ogden Lindsley developed a learning system, named Celeration, which was based on behavior analysis but substantially differed from Keller's and Skinner's models.

Cognitivism

Cognitive science underwent significant change in the 1960s and 1970s to the point that some described the period as a "cognitive revolution", particularly in reaction to behaviorism. While retaining the empirical framework of behaviorism, cognitive psychology theories look beyond behavior to explain brain-based learning by considering how human memory works to promote learning. It refers to learning as "all processes by which the sensory input is transformed, reduced, elaborated, stored, recovered, and used" by the human mind. The Atkinson-Shiffrin memory model and Baddeley's working memory model were established as theoretical frameworks. Computer science and information technology have had a major influence on cognitive science theory. The cognitive concepts of working memory (formerly known as short-term memory) and long-term memory have been facilitated by research and technology from the field of computer science. Another major influence on the field of cognitive science is Noam Chomsky. Today researchers are concentrating on topics like cognitive load, information processing, and media psychology. These theoretical perspectives influence instructional design.

There are two separate schools of cognitivism, and these are the cognitivist and social cognitivist. The former focuses on the understanding of the thinking or cognitive processes of an individual while the latter includes social processes as influences in learning besides cognition. These two schools, however, share the view that learning is more than a behavioral change but is rather a mental process used by the learner.

Constructivism

Educational psychologists distinguish between several types of constructivism: individual (or psychological) constructivism, such as Piaget's theory of cognitive development, and social constructivism. This form of constructivism has a primary focus on how learners construct their own meaning from new information, as they interact with reality and with other learners who bring different perspectives. Constructivist learning environments require students to use their prior knowledge and experiences to formulate new, related, and/or adaptive concepts in learning. Under this framework, the role of the teacher becomes that of a facilitator, providing guidance so that learners can construct their own knowledge. Constructivist educators must make sure that the prior learning experiences are appropriate and related to the concepts being taught. Jonassen (1997) suggests "well-structured" learning environments are useful for novice learners and that "ill-structured" environments are only useful for more advanced learners. Educators utilizing a constructivist perspective may emphasize an active learning environment that may incorporate learner-centered problem-based learning, project-based learning, and inquiry-based learning, ideally involving real-world scenarios, in which students are actively engaged in critical thinking activities. An illustrative discussion and example can be found in the 1980s deployment of constructivist cognitive learning in computer literacy, which involved programming as an instrument of learning. LOGO, a programming language, embodied an attempt to integrate Piagetian ideas with computers and technology. Initially there were broad, hopeful claims, including "perhaps the most controversial claim" that it would "improve general problem-solving skills" across disciplines. However, LOGO programming skills did not consistently yield cognitive benefits. It was "not as concrete" as advocates claimed, it privileged "one form of reasoning over all others", and it was difficult to apply the thinking activity to non-LOGO-based activities. By the late 1980s, LOGO and other similar programming languages had lost their novelty and dominance and were gradually de-emphasized amid criticisms.

Practice

The extent to which e-learning assists or replaces other learning and teaching approaches is variable, ranging on a continuum from none to fully online distance learning. A variety of descriptive terms have been employed (somewhat inconsistently) to categorize the extent to which technology is used. For example, "hybrid learning" or "blended learning" may refer to classroom aids and laptops, or may refer to approaches in which traditional classroom time is reduced but not eliminated, and is replaced with some online learning. "Distributed learning" may describe either the e-learning component of a hybrid approach, or fully online distance learning environments.

Synchronous and asynchronous

E-learning may either be synchronous or asynchronous. Synchronous learning occurs in real-time, with all participants interacting at the same time. In contrast, asynchronous learning is self-paced and allows participants to engage in the exchange of ideas or information without the dependency on other participants' involvement at the same time.

Synchronous learning refers to exchanging ideas and information with one or more participants during the same period. Examples are face-to-face discussion, online real-time live teacher instruction and feedback, Skype conversations, and chat rooms or virtual classrooms where everyone is online and working collaboratively at the same time. Since students are working collaboratively, synchronized learning helps students become more open-minded because they have to actively listen and learn from their peers. Synchronized learning fosters online awareness and improves many students' writing skills.

Asynchronous learning may use technologies such as learning management systems, email, blogs, wikis, and discussion boards, as well as web-supported textbooks, hypertext documents, audio video courses, and social networking using web 2.0. At the professional educational level, training may include virtual operating rooms. Asynchronous learning is beneficial for students who have health problems or who have childcare responsibilities. They have the opportunity to complete their work in a low-stress environment and within a more flexible time frame. In asynchronous online courses, students are allowed the freedom to complete work at their own pace. Being non-traditional students, they can manage their daily life and school and still have the social aspect. Asynchronous collaborations allow the student to reach out for help when needed and provide helpful guidance, depending on how long it takes them to complete the assignment. Many tools used for these courses are but are not limited to: videos, class discussions, and group projects.

Linear learning

Computer-based training (CBT) refers to self-paced learning activities delivered on a computer or handheld devices such as a tablet or smartphone. CBT initially delivered content via CD-ROM, and typically presented content linearly, much like reading an online book or manual. For this reason, CBT is often used to teach static processes, such as using software or completing mathematical equations. Computer-based training is conceptually similar to web-based training (WBT), which is delivered via Internet using a web browser.

Assessing learning in a CBT is often by assessments that can be easily scored by a computer such as multiple-choice questions, drag-and-drop, radio button, simulation, or other interactive means. Assessments are easily scored and recorded via online software, providing immediate end-user feedback and completion status. Users are often able to print completion records in the form of certificates.

CBTs provide learning stimulus beyond traditional learning methodology from textbook, manual, or classroom-based instruction. CBTs can be a good alternative to printed learning materials since rich media, including videos or animations, can be embedded to enhance learning.

However, CBTs pose some learning challenges. Typically, creating effective CBTs requires enormous resources. The software for developing CBTs is often more complex than what a subject matter expert or teacher is able to use.

Collaborative learning

Computer-supported collaborative learning (CSCL) uses instructional methods designed to encourage or require students to work together on learning tasks, allowing social learning. CSCL is similar in concept to the terminology, "e-learning 2.0" and "networked collaborative learning" (NCL). With Web 2.0 advances, sharing information between multiple people in a network has become much easier and use has increased. One of the main reasons cited for its usage is that it serves as "a breeding ground for creative and engaging educational endeavors." Learning takes place through conversations about content and grounded interaction about problems and actions. This collaborative learning differs from instruction in which the instructor is the principal source of knowledge and skills. The neologism "e-learning 1.0" refers to direct instruction used in early computer-based learning and training systems (CBL). In contrast to that linear delivery of content, often directly from the instructor's material, CSCL uses social software such as blogs, social media, wikis, podcasts, cloud-based document portals, discussion groups and virtual worlds. This phenomenon has been described as "long-tail learning". Advocates of social learning claim that one of the best ways to learn something is to teach it to others. Social networks have been used to foster online learning communities around subjects as diverse as test preparation and language education. Mobile-assisted language learning (MALL) is the use of handheld computers or cell phones to assist in language learning.

Collaborative apps allow students and teachers to interact while studying. Apps are designed after games, which provide a fun way to revise. When the experience is enjoyable, the students become more engaged. Games also usually come with a sense of progression, which can help keep students motivated and consistent while trying to improve.

Classroom 2.0 refers to online multi-user virtual environments (MUVEs) that connect schools across geographical frontiers. Known as "eTwinning", computer-supported collaborative learning (CSCL) allows learners in one school to communicate with learners in another that they would not get to know otherwise, enhancing educational outcomes and cultural integration.

Further, many researchers distinguish between collaborative and cooperative approaches to group learning. For example, Roschelle and Teasley (1995) argue that "cooperation is accomplished by the division of labor among participants, as an activity where each person is responsible for a portion of the problem solving", in contrast with collaboration that involves the "mutual engagement of participants in a coordinated effort to solve the problem together."

Social technology, and social media specifically, provides avenues for student learning that would not be available otherwise. For example, it provides ordinary students a chance to exist in the same room as, and share a dialogue with researchers, politicians, and activists. This is because it vaporizes the geographical barriers that would otherwise separate people. Simplified, social media gives students a reach that provides them with opportunities and conversations that allow them to grow as communicators.

Social technologies like Twitter can provide students with an archive of free data that goes back multiple decades. Many classrooms and educators are already taking advantage of this free resource—for example, researchers and educators at the University of Central Florida in 2011 used Tweets posted relating to emergencies like Hurricane Irene as data points, in order to teach their students how to code data. Social media technologies also allow instructors the ability to show students how professional networks facilitate work on a technical level.

Flipped classroom

This is an instructional strategy where the majority of the initial learning occurs first at home using technology. Then, students will engage with higher-order learning tasks in the classroom with the teacher. Often, online tools are used for the individual at-home learning, such as: educational videos, learning management systems, interactive tools, and other web-based resources. Some advantages of flipped learning include improved learning performance, enhanced student satisfaction and engagement, flexibility in learning, and increased interaction opportunities between students and instructors. On the other hand, the disadvantages of flipped learning involve challenges related to student motivation, internet accessibility, quality of videos, and increased workload for teachers.

Technologies

A 2.5 m teaching slide rule compared to a normal sized model

Numerous types of physical technology are currently used: digital cameras, video cameras, interactive whiteboard tools, document cameras, electronic media, and LCD projectors. Combinations of these techniques include blogs, collaborative software, ePortfolios, and virtual classrooms.

The current design of this type of application includes the evaluation through tools of cognitive analysis that allow one to identify which elements optimize the use of these platforms.

Audio and video

Video technology has included VHS tapes and DVDs, as well as on-demand and synchronous methods with digital video via server or web-based options such as streamed video and webcams. Videotelephony can connect with speakers and other experts. Interactive digital video games are being used at K-12 and higher education institutions.

Screencasting allows users to share their screens directly from their browser and make the video available online so that other viewers can stream the video directly.

Webcams and webcasting have enabled the creation of virtual classrooms and virtual learning environments. Webcams are also being used to counter plagiarism and other forms of academic dishonesty that might occur in an e-learning environment.

Computers, tablets, and mobile devices

Teaching and learning online

Computers and tablets enable learners and educators to access websites as well as applications. Many mobile devices support m-learning.

Mobile devices such as clickers and smartphones can be used for interactive audience response feedback. Mobile learning can provide performance support for checking the time, setting reminders, retrieving worksheets, and instruction manuals.

Such devices as iPads are used for helping disabled (visually impaired or with multiple disabilities) children in communication development as well as in improving physiological activity, according to the stimulation Practice Report.

Studies in pre-school (early learning), primary and secondary education have explored how digital devices are used to enable effective learning outcomes, and create systems that can support teachers. Digital technology can improve teaching and learning by motivating students with engaging, interactive, and fun learning environments. These online interactions enable further opportunities to develop digital literacy, 21st century skills, and digital citizenship.

Single-board computers and Internet of Things

Embedded single-board computers and microcontrollers such as Raspberry Pi, Arduino and BeagleBone are easy to program, some can run Linux and connect to devices such as sensors, displays, LEDs and robotics. These are cost effective computing devices ideal for learning programming, which work with cloud computing and the Internet of Things. The Internet of things refers to a type of network to connect anything with the Internet-based on stipulated protocols through information sensing equipment to conduct information exchange and communications to achieve smart recognitions, positioning, tracking, monitoring, and administration. These devices are part of a Maker culture that embraces tinkering with electronics and programming to achieve software and hardware solutions. The Maker Culture means there is a huge amount of training and support available.

Collaborative and social learning

Group webpages, blogs, wikis, and Twitter allow learners and educators to post thoughts, ideas, and comments on a website in an interactive learning environment. Social networking sites are virtual communities for people interested in a particular subject to communicate by voice, chat, instant message, video conference, or blogs. The National School Boards Association found that 96% of students with online access have used social networking technologies and more than 50% talk online about schoolwork. Social networking encourages collaboration and engagement and can be a motivational tool for self-efficacy amongst students.

Whiteboards

Combination whiteboard and bulletin board

There are three types of whiteboards. The initial whiteboards, analogous to blackboards, date from the late 1950s. The term whiteboard is also used metaphorically to refer to virtual whiteboards in which computer software applications simulate whiteboards by allowing writing or drawing. This is a common feature of groupware for virtual meetings, collaboration, and instant messaging. Interactive whiteboards allow learners and instructors to write on the touch screen. The screen markup can be on either a blank whiteboard or any computer screen content. Depending on permission settings, this visual learning can be interactive and participatory, including writing and manipulating images on the interactive whiteboard.

Virtual classroom

A virtual learning environment (VLE), also known as a learning platform, simulates a virtual classroom or meeting by simultaneously mixing several communication technologies. Web conferencing software enables students and instructors to communicate with each other via webcam, microphone, and real-time chatting in a group setting. Participants can raise their hands, answer polls, or take tests. Students can whiteboard and screencast when given rights by the instructor, who sets permission levels for text notes, microphone rights, and mouse control.

A virtual classroom provides an opportunity for students to receive direct instruction from a qualified teacher in an interactive environment. Learners can have direct and immediate access to their instructor for instant feedback and direction. The virtual classroom provides a structured schedule of classes, which can be helpful for students who may find the freedom of asynchronous learning to be overwhelming. Besides, the virtual classroom provides a social learning environment that replicates the traditional "brick and mortar" classroom. In addition to formal virtual classrooms, students may use online platforms such as StudySoup to access and share class notes and study guides.

In higher education especially, a virtual learning environment (VLE) is sometimes combined with a management information system (MIS) to create a managed learning environment, in which all aspects of a course are handled through a consistent user interface throughout the institution. Physical universities and newer online-only colleges offer to select academic degrees and certificate programs via the Internet. Some programs require students to attend some campus classes or orientations, but many are delivered completely online. Several universities offer online student support services, such as online advising and registration, e-counseling, online textbook purchases, student governments, and student newspapers.

Due to the COVID-19 pandemic, many schools have been forced to move online. As of April 2020, an estimated 90% of high-income countries are offering online learning, with only 25% of low-income countries offering the same.

Augmented reality

AR technology plays an important role in the future of the classroom where human co-orchestration takes place seamlessly.

Learning management system

Learning management system

A learning management system (LMS) is software used for delivering, tracking, and managing training and education. It tracks data about attendance, time on task, and student progress. Educators can post announcements, grade assignments, check on course activities, and participate in class discussions. Students can submit their work, read and respond to discussion questions, and take quizzes. An LMS may allow teachers, administrators, and students, and permitted additional parties (such as parents, if appropriate) to track various metrics. LMSs range from systems for managing training/educational records to software for distributing courses over the Internet and offering features for online collaboration. The creation and maintenance of comprehensive learning content require substantial initial and ongoing investments in human labor. Effective translation into other languages and cultural contexts requires even more investment by knowledgeable personnel.

Learning content management system

A learning content management system (LCMS) is software for author content (courses, reusable content objects). An LCMS may be solely dedicated to producing and publishing content that is hosted on an LMS, or it can host the content itself. The Aviation Industry Computer-Based Training Committee (AICC) specification provides support for content that is hosted separately from the LMS.

Computer-aided assessment

Computer-aided assessment (e-assessment) ranges from automated multiple-choice tests to more sophisticated systems. With some systems, feedback can be geared towards a student's specific mistakes, or the computer can navigate the student through a series of questions adapting to what the student appears to have learned or not learned. Formative assessment sifts out the incorrect answers, and these questions are then explained by the teacher. The learner then practices with slight variations of the sifted-out questions. The learning cycle often concludes with summative assessment, using a new set of questions that cover the topics previously taught.

Training management system

A training management system or training resource management system is software designed to optimize instructor-led training management. Similar to an enterprise resource planning (ERP), it is a back office tool that aims at streamlining every aspect of the training process: planning (training plan and budget forecasting), logistics (scheduling and resource management), financials (cost tracking, profitability), reporting, and sales for-profit training providers.

Standards and ecosystem

Learning objects

Content

Content and design architecture issues include pedagogy and learning object re-use. One approach looks at five aspects:

  • Fact – unique data (e.g. symbols for Excel formula, or the parts that make up a learning objective)
  • Concept – a category that includes multiple examples (e.g. Excel formulas, or the various types/theories of instructional design)
  • Process – a flow of events or activities (e.g. how a spreadsheet works, or the five phases in ADDIE)
  • Procedure – step-by-step task (e.g. entering a formula into a spreadsheet or the steps that should be followed within a phase in ADDIE)
  • Strategic principle – a task performed by adapting guidelines (e.g. doing a financial projection in a spreadsheet, or using a framework for designing learning environments)

Artificial intelligence

The academic study and development of artificial intelligence can be dated to at least 1956 when cognitive scientists began to investigate thought and learning processes in humans and machines. The earliest uses of AI in education can be traced to the development of intelligent tutoring systems (ITS) and their application in enhancing educational experiences. They are designed to provide immediate and personalized feedback to students. The incentive to develop ITS comes from educational studies showing that individual tutoring is much more effective than group teaching, in addition to the need for promoting learning on a larger scale. Over the years, a combination of cognitive science and data-driven techniques have enhanced the capabilities of ITS, allowing it to model a wide range of students' characteristics, such as knowledge, emotion, off-task behavior, and wheel spinning. There is ample evidence that ITS are highly effective in helping students learn. ITS can be used to keep students in the zone of proximal development (ZPD): the space wherein students may learn with guidance. Such systems can guide students through tasks slightly above their ability level.

Generative artificial intelligence (GenAI) gained widespread public attention with the introduction of ChatGPT in November 2022. This caused alarm among K-12 and higher education institutions, with a few large school districts quickly banning GenAI, due to concerns about potential academic misconduct. However, as the debate developed, these bans were largely reversed within a few months. To combat academic misconduct, detection tools have been developed, but their accuracy is limited.

There have been various use cases in education, including providing personalized feedback, brainstorming classroom activities, support for students with special needs, streamlining administrative tasks, and simplifying assessment processes. However, GenAI can output incorrect information, also known as hallucination. Its outputs can also be biased, leading to calls for transparency regarding the data used to train GenAI models and their use. Providing professional development for teachers and developing policies and regulations can help mitigate the ethical concerns of GenAI. And while AI systems can provide individualized instruction and adaptive feedback to students, they have the potential to impact students' sense of classroom community.

Settings and sectors

Preschool

Various forms of electronic media can be a feature of preschool life. Although parents report a positive experience, the impact of such use has not been systematically assessed.

Preschool activity

The age when a given child might start using a particular technology, such as a cellphone or computer, might depend on matching a technological resource to the recipient's developmental capabilities, such as the age-anticipated stages labeled by Swiss psychologist, Jean Piaget. Parameters, such as age-appropriateness, coherence with sought-after values, and concurrent entertainment and educational aspects, have been suggested for choosing media.

At the preschool level, technology can be introduced in several ways. At the most basic is the use of computers, tablets, and audio and video resources in classrooms. Additionally, there are many resources available for parents and educators to introduce technology to young children or to use technology to augment lessons and enhance learning. Some options that are age-appropriate are video- or audio-recording of their creations, introducing them to the use of the internet through browsing age-appropriate websites, providing assistive technology to allow disabled children to participate with the rest of their peers, educational apps, electronic books, and educational videos. There are many free and paid educational website and apps that are directly targeting the educational needs of preschool children. These include Starfall, ABC mouse, PBS Kids Video, Teach me, and Montessori crosswords. Educational technology in the form of electronic books [109] offer preschool children the option to store and retrieve several books on one device, thus bringing together the traditional action of reading along with the use of educational technology. Educational technology is also thought to improve hand-eye coordination, language skills, visual attention, and motivation to complete educational tasks, and allows children to experience things they otherwise would not. There are several keys to making the most educational use of introducing technology at the preschool level: technology must be used appropriately, should allow access to learning opportunities, should include the interaction of parents and other adults with the preschool children, and should be developmentally appropriate. Allowing access to learning opportunities especially for allowing disabled children to have access to learning opportunities, giving bilingual children the opportunity to communicate and learn in more than one language, bringing in more information about STEM subjects, and bringing in images of diversity that may be lacking in the child's immediate environment.

Coding is also becoming part of the early learning curriculum and preschool-aged children can benefit from experiences that teach coding skills even in a screen-free way. There are activities and games that teach hands-on coding skills that prepare students for the coding concepts they will encounter and use in the future. Minecraft and Roblox are two popular coding and programming apps being adopted by institutions that offer free or low-cost access.

Primary and secondary

Teacher showing primary school students how to work a program at a primary school in Santa Fe, Mexico City
Students at the World Vision Higher Secondary College

E-learning is increasingly being utilized by students who may not want to go to traditional brick-and-mortar schools due to severe allergies or other medical issues, fear of school violence and school bullying, and students whose parents would like to homeschool but do not feel qualified. Online schools create a haven for students to receive a quality education while almost completely avoiding these common problems. Online charter schools also often are not limited by location, income level, or class size in the way brick and mortar charter schools are.

A student attending online class in Kerala, India, during the COVID-19 pandemic

E-learning also has been rising as a supplement to the traditional classroom. Students with special talents or interests outside of the available curricula use e-learning to advance their skills or exceed grade restrictions.

Virtual education in K-12 schooling often refers to virtual schools, and in higher education to virtual universities. Virtual schools are "cybercharter schools" with innovative administrative models and course delivery technology.

Education technology also seems to be an interesting method of engaging gifted youths that are under-stimulated in their current educational program. This can be achieved with after-school programs or even technologically-integrated curricula. 3D printing integrated courses (3dPIC) can also give youths the stimulation they need in their educational journey. Université de Montréal's Projet SEUR in collaboration with Collège Mont-Royal and La Variable are heavily developing this field.

Higher education

Students using laptops in higher education

Online college course enrollment has seen a 29% increase in enrollment with nearly one-third of all college students, or an estimated 6.7 million students are currently enrolled in online classes. In 2009, 44% of post-secondary students in the US were taking some or all of their courses online, which was projected to rise to 81% by 2014.

Although a large proportion of for-profit higher education institutions now offer online classes, only about half of private, non-profit schools do so. Private institutions may become more involved with online presentations as the costs decrease. Properly trained staff must also be hired to work with students online. These staff members need to understand the content area, and also be highly trained in the use of the computer and Internet. Online education is rapidly increasing, and online doctoral programs have even developed at leading research universities.

Although massive open online courses (MOOCs) may have limitations that preclude them from fully replacing college education, such programs have significantly expanded. MIT, Stanford and Princeton University offer classes to a global audience, but not for college credit. University-level programs, like edX founded by Massachusetts Institute of Technology and Harvard University, offer a wide range of disciplines at no charge, while others permit students to audit a course at no charge but require a small fee for accreditation. MOOCs have not had a significant impact on higher education and declined after the initial expansion, but are expected to remain in some form. Lately, MOOCs are used by smaller universities to profile themselves with highly specialized courses for special-interest audiences, as for example in a course on technological privacy compliance.

MOOCs have been observed to lose the majority of their initial course participants. In a study performed by Cornell and Stanford universities, student-drop-out rates from MOOCs have been attributed to student anonymity, the solitude of the learning experience, and to the lack of interaction with peers and with teachers. Effective student engagement measures that reduce drop-outs are forum interactions and virtual teacher or teaching assistant presence - measures which induce staff cost that grows with the number of participating students.

Corporate and professional

E-learning is being used by companies to deliver mandatory compliance training and updates for regulatory compliance, soft skills and IT skills training, continuing professional development (CPD), and other valuable workplace skills. Companies with spread out distribution chains use e-learning for delivering information about the latest product developments. Most corporate e-learning is asynchronous and delivered and managed via learning management systems. The big challenge in corporate e-learning is to engage the staff, especially on compliance topics for which periodic staff training is mandated by the law or regulations.

Government and public

Educational technology is used by governmental bodies to train staff and civil service. Government agencies also have an interest in promoting digital technology use, and improving skills amongst the people they serve.

Benefits

Effective technology use deploys multiple evidence-based strategies concurrently (e.g. adaptive content, frequent testing, immediate feedback, etc.), as do effective teachers. Using computers or other forms of technology can give students practice on core content and skills while the teacher can work with others, conduct assessments, or perform other tasks. Through the use of educational technology, education is able to be individualized for each student allowing for better differentiation and allowing students to work for mastery at their own pace. In India, the National Level Common Entrance Examination (NLCEE) utilized educational technology to provide free online coaching and scholarship opportunities. By leveraging digital platforms during the COVID-19 pandemic, NLCEE ensured students, especially those from underprivileged backgrounds, could access quality education and career guidance remotely.

Modern educational technology can improve access to education, including full degree programs. It enables better integration for non-full-time students, particularly in continuing education, and improved interactions between students and instructors. Learning materials can be used for distance learning and are accessible to a wider audience across geographical boundaries. Course materials are easy to access. In 2010, 70.3% of American family households had access to the internet. In 2013, according to Canadian Radio-Television and Telecommunications Commission Canada, 79% of homes have access to the internet. Students can access and engage with numerous online resources at home. Using online resources can help students spend more time on specific aspects of what they may be learning in school but at home. Schools like the Massachusetts Institute of Technology (MIT) have made certain course materials free online.

Students appreciate the convenience of e-learning, but report greater engagement in face-to-face learning environments. Colleges and universities are working towards combating this issue by utilizing WEB 2.0 technologies as well as incorporating more mentorships between students and faculty members.

According to James Kulik, who studies the effectiveness of computers used for instruction, students usually learn more in less time when receiving computer-based instruction, and they like classes more and develop more positive attitudes toward computers in computer-based classes. Students can independently solve problems. There are no intrinsic age-based restrictions on difficulty level, i.e. students can go at their own pace. Students editing their written work on word processors improve the quality of their writing. According to some studies, the students are better at critiquing and editing written work that is exchanged over a computer network with students they know. Studies completed in "computer intensive" settings found increases in student-centric, cooperative, and higher-order learning, writing skills, problem-solving, and using technology. In addition, attitudes toward technology as a learning tool by parents, students, and teachers are also improved.

Employers' acceptance of online education has risen over time. More than 50% of human resource managers SHRM surveyed for an August 2010 report said that if two candidates with the same level of experience were applying for a job, it would not have any kind of effect whether the candidate's obtained degree was acquired through an online or a traditional school. Seventy-nine percent said they had employed a candidate with an online degree in the past 12 months. However, 66% said candidates who get degrees online were not seen as positively as job applicants with traditional degrees.

The use of educational apps generally has a positive effect on learning. Pre- and post-tests have revealed that the use of educational apps on mobile devices reduces the achievement gap between struggling and average students.

Disadvantages

Globally, factors like change management, technology obsolescence, and vendor-developer partnership are major restraints that are hindering the growth of the Educational technology market.

In the US, state and federal government increased funding, as well as private venture capital, has been flowing into the education sector. However, as of 2013, none were looking at technology return on investment (ROI) to connect expenditures on technology with improved student outcomes.

New technologies are frequently accompanied by unrealistic hype and promises regarding their transformative power to change education for the better or to provide better educational opportunities to the masses. Examples include silent film, broadcast radio, and television, none of which have maintained much of a foothold in the daily practices of mainstream formal education. Technology, in and of itself, does not necessarily result in fundamental improvements to educational practice. The focus needs to be on the learner's interaction with technology—not the technology itself. It needs to be recognized as "ecological" rather than "additive" or "subtractive". In this ecological change, one significant change will create total change.

According to Branford et al., "technology does not guarantee effective learning", and inappropriate use of technology can even hinder it. A University of Washington study of infant vocabulary shows that it is slipping due to educational baby DVDs. Published in the Journal of Pediatrics, a 2007 University of Washington study on the vocabulary of babies surveyed over 1,000 parents in Washington and Minnesota. The study found that for every hour that babies 8–16 months of age watched DVDs and videos, they knew 6–8 fewer of 90 common baby words than the babies that did not watch them. Andrew Meltzoff, a surveyor in this study, states that the result makes sense, that if the baby's "alert time" is spent in front of DVDs and TV, instead of with people speaking, the babies are not going to get the same linguistic experience. Dimitri Chistakis, another surveyor reported that the evidence is mounting that baby DVDs are of no value and may be harmful.

Adaptive instructional materials tailor questions to each student's ability and calculate their scores, but this encourages students to work individually rather than socially or collaboratively (Kruse, 2013). Social relationships are important, but high-tech environments may compromise the balance of trust, care, and respect between teacher and student.

Massively open online courses (MOOCs), although quite popular in discussions of technology and education in developed countries (more so in the US), are not a major concern in most developing or low-income countries. One of the stated goals of MOOCs is to provide less fortunate populations (i.e., in developing countries) an opportunity to experience courses with US-style content and structure. However, research shows only 3% of the registrants are from low-income countries, and although many courses have thousands of registered students only 5–10% of them complete the course. This can be attributed to lack of staff support, course difficulty, and low levels of engagement with peers. MOOCs also implies that certain curriculum and teaching methods are superior, and this could eventually wash over (or possibly washing out) local educational institutions, cultural norms, and educational traditions.

With the Internet and social media, using educational apps makes students highly susceptible to distraction and sidetracking. Even though proper use has been shown to increase student performance, being distracted would be detrimental. Another disadvantage is an increased potential for cheating.

A disadvantage of e-learning is that it can cause depression, according to a study made during the 2021 COVID-19 quarantines.

Over-stimulation

Electronic devices such as cell phones and computers facilitate rapid access to a stream of sources, each of which may receive cursory attention. Michel Rich, an associate professor at Harvard Medical School and executive director of the center on Media and Child Health in Boston, said of the digital generation, "Their brains are rewarded not for staying on task, but for jumping to the next thing. The worry is we're raising a generation of kids in front of screens whose brains are going to be wired differently." Students have always faced distractions; computers and cell phones are a particular challenge because the stream of data can interfere with focusing and learning. Although these technologies affect adults too, young people may be more influenced by it as their developing brains can easily become habituated to switching tasks and become unaccustomed to sustaining attention. Too much information, coming too rapidly, can overwhelm thinking.

Technology is "rapidly and profoundly altering our brains." High exposure levels stimulate brain cell alteration and release neurotransmitters, which causes the strengthening of some neural pathways and the weakening of others. This leads to heightened stress levels on the brain that, at first, boost energy levels, but, over time, actually augment memory, impair cognition, lead to depression, and alter the neural circuitry of the hippocampus, amygdala and prefrontal cortex. These are the brain regions that control mood and thought. If unchecked, the underlying structure of the brain could be altered. Overstimulation due to technology may begin too young. When children are exposed before the age of seven, important developmental tasks may be delayed, and bad learning habits might develop, which "deprives children of the exploration and play that they need to develop." Media psychology is an emerging specialty field that embraces electronic devices and the sensory behaviors occurring from the use of educational technology in learning.

Sociocultural criticism

According to Lai, "the learning environment is a complex system where the interplay and interactions of many things impact the outcome of learning." When technology is brought into an educational setting, the pedagogical setting changes in that technology-driven teaching can change the entire meaning of an activity without adequate research validation. If technology monopolizes an activity, students can begin to develop the sense that "life would scarcely be thinkable without technology."

Leo Marx considered the word "technology" itself as problematic, susceptible to reification and "phantom objectivity", which conceals its fundamental nature as something that is only valuable insofar as it benefits the human condition. Technology ultimately comes down to affecting the relations between people, but this notion is obfuscated when technology is treated as an abstract notion devoid of good and evil. Langdon Winner makes a similar point by arguing that the underdevelopment of the philosophy of technology leaves us with an overly simplistic reduction in our discourse to the supposedly dichotomous notions of the "making" versus the "uses" of new technologies and that a narrow focus on "use" leads us to believe that all technologies are neutral in moral standing.

Winner viewed technology as a "form of life" that not only aids human activity, but that also represents a powerful force in reshaping that activity and its meaning.

By far, the greatest latitude of choice exists the very first time a particular instrument, system, or technique is introduced. Because choices tend to become strongly fixed in material equipment, economic investment, and social habit, the original flexibility vanishes for all practical purposes once the initial commitments are made. In that sense, technological innovations are similar to legislative acts or political findings that establish a framework for public order that will endure over many generations. (p. 29)

When adopting new technologies, there may be one best chance to "get it right". Seymour Papert (p. 32) points out a good example of a (bad) choice that has become strongly fixed in social habit and material equipment: our "choice" to use the QWERTY keyboard.

Neil Postman endorsed the notion that technology impacts human cultures, including the culture of classrooms, and that this is a consideration even more important than considering the efficiency of new technology as a tool for teaching. Regarding the computer's impact on education, Postman writes (p. 19):

What we need to consider about the computer has nothing to do with its efficiency as a teaching tool. We need to know in what ways it is altering our conception of learning, and how in conjunction with television, it undermines the old idea of school.

There is an assumption that technology is inherently interesting so it must be helpful in education; based on research by Daniel Willingham, that is not always the case. He argues that it does not necessarily matter what the technological medium is, but whether or not the content is engaging and utilizes the medium in a beneficial way.

Digital divide

The concept of the digital divide is a gap between those who have access to digital technologies and those who do not. Access may be associated with age, gender, socio-economic status, education, income, ethnicity, and geography.

Data protection

According to a report by the Electronic Frontier Foundation, large amounts of personal data on children are collected by electronic devices that are distributed in schools in the United States. Often, far more information than necessary is collected, uploaded, and stored indefinitely. Aside from name and date of birth, this information can include the child's browsing history, search terms, location data, contact lists, as well as behavioral information. Parents are not informed or, if informed, have little choice. According to the report, this constant surveillance resulting from educational technology can "warp children's privacy expectations, lead them to self-censor, and limit their creativity". In a 2018 public service announcement, the FBI warned that widespread collection of student information by educational technologies, including web browsing history, academic progress, medical information, and biometrics, created the potential for privacy and safety threats if such data was compromised or exploited.

The transition from in-person learning to distance education in higher education due to the COVID-19 pandemic has led to enhanced extraction of student data enabled by complex data infrastructures. These infrastructures collect information such as learning management system logins, library metrics, impact measurements, teacher evaluation frameworks, assessment systems, learning analytic traces, longitudinal graduate outcomes, attendance records, social media activity, and so on. The copious amounts of information collected are quantified for the marketization of higher education, employing this data as a means to demonstrate and compare student performance across institutions to attract prospective students, mirroring the capitalistic notion of ensuring efficient market functioning and constant improvement through measurement. This desire of data has fueled the exploitation of higher education by platform companies and data service providers who are outsourced by institutions for their services. The monetization of student data in order to integrate corporate models of marketization further pushes higher education, widely regarded as a public good, into a privatized commercial sector.

Teacher training

Since technology is not the end goal of education, but rather a means by which it can be accomplished, educators must have a good grasp of the technology and its advantages and disadvantages. Teacher training aims for the effective integration of classroom technology.

Teacher training in Naura

The evolving nature of technology may unsettle teachers, who may experience themselves as perpetual novices. Finding quality materials to support classroom objectives is often difficult. Random professional development days are inadequate.

According to Jenkins, "Rather than dealing with each technology in isolation, we would do better to take an ecological approach, thinking about the interrelationship among different communication technologies, the cultural communities that grow up around them, and the activities they support." Jenkins also suggested that the traditional school curriculum guided teachers to train students to be autonomous problem solvers. However, today's workers are increasingly asked to work in teams, drawing on different sets of expertise, and collaborating to solve problems. Learning styles and the methods of collecting information have evolved, and "students often feel locked out of the worlds described in their textbooks through the depersonalized and abstract prose used to describe them". These twenty-first-century skills can be attained through the incorporation and engagement with technology. Changes in instruction and use of technology can also promote a higher level of learning among students with different types of intelligence.

Assessment

There are two distinct issues of assessment: the assessment of educational technology and assessment with technology.

Assessments of educational technology have included the Follow Through project.

Educational assessment with technology may be either formative assessment or summative assessment. Instructors use both types of assessments to understand student progress and learning in the classroom. Technology has helped teachers create better assessments to help understand where students who are having trouble with the material are having issues.

Formative assessment is more difficult, as the perfect form is ongoing and allows the students to show their learning in different ways depending on their learning styles. Technology has helped some teachers make their formative assessments better, particularly through the use of a classroom response system (CRS). A CRS is a tool in which the students each have a handheld device that partners up with the teacher's computer. The instructor then asks multiple choice or true or false questions and the students answer on their devices. Depending on the software used, the answers may then be shown on a graph so students and the teacher can see the percentage of students who gave each answer and the teacher can focus on what went wrong.

Learning analytics (LA) is applied to examine the relationship between self-regulated learning (SRL) processes and academic success. By integrating learning management systems with psychological data, like self-efficacy and cognitive strategies, researchers have found correlations between learning practices and success. Studies have shown that students who take part in focused, moderate study sessions and make regular annotations or reflective posts are likely to display greater confidence and higher achievement than those who study for extended sessions without any organized structure. These findings have led to the development of dashboards that use learning analytics, allowing learners to visualize their learning and to reflect metacognitively. This integration of learning analytics and self-regulation theory also helps assess learners.

Classroom response systems have a history going back to the late 1960s and early 1970s, when analogue electronics were used in their implementations. There were a few commercial products available, but they were costly and some universities preferred to build their own. The first such system appears to have been put into place at Stanford University, but it suffered from difficulties in use. Another early system was one designed and built by Raphael M. Littauer, a professor of physics at Cornell University, and used for large lecture courses. It was more successful than most of the other early systems, in part because the designer of the system was also the instructor using it. A subsequent classroom response technologies involved H-ITT with infrared devices.

Summative assessments are more common in classrooms and are usually set up to be more easily graded, as they take the form of tests or projects with specific grading schemes. One huge benefit of tech-based testing is the option to give students immediate feedback on their answers. When students get these responses, they are able to know how they are doing in the class which can help push them to improve or give them confidence that they are doing well. Technology also allows for different kinds of summative assessment, such as digital presentations, videos, or anything else the teacher/students may come up with, which allows different learners to show what they learned more effectively. Teachers can also use technology to post graded assessments online for students to have a better idea of what a good project is.

Electronic assessment uses information technology. It encompasses several potential applications, which may be teacher or student-oriented, including educational assessment throughout the continuum of learning, such as computerized classification testing, computerized adaptive testing, student testing, and grading an exam. E-Marking is an examiner-led activity closely related to other e-assessment activities such as e-testing, or e-learning which are student-led. E-marking allows markers to mark a scanned script or online response on a computer screen rather than on paper.

There are no restrictions on the types of tests that can use e-marking, with e-marking applications designed to accommodate multiple choice, written, and even video submissions for performance examinations. E-marking software is used by individual educational institutions and can also be rolled out to the participating schools of awarding exam organizations. E-marking has been used to mark many well-known high stakes examinations, which in the United Kingdom include A levels and GCSE exams, and in the US includes the SAT test for college admissions. Ofqual reports that e-marking is the main type of marking used for general qualifications in the United Kingdom.

In 2014, the Scottish Qualifications Authority (SQA) announced that most of the National 5 question papers would be e-marked.

In June 2015, the Odisha state government in India announced that it planned to use e-marking for all Plus II papers from 2016.

Analytics

The importance of self-assessment through tools made available on educational technology platforms has been growing. Self-assessment in education technology relies on students analyzing their strengths, weaknesses, and areas where improvement is possible to set realistic goals in learning, improve their educational performances and track their progress. One of the unique tools for self-assessment made possible by education technology is Analytics. Analytics is data gathered on the student's activities on the learning platform, drawn into meaningful patterns that lead to a valid conclusion, usually through the medium of data visualization such as graphs. Learning analytics is the field that focuses on analyzing and reporting data about students' activities in order to facilitate learning.

Expenditure

The five key sectors of the e-learning industry are consulting, content, technologies, services, and support. Worldwide, e-learning was estimated in 2000 to be over $48 billion according to conservative estimates. Commercial growth has been brisk. In 2014, the worldwide commercial market activity was estimated at $6 billion venture capital over the past five years, with self-paced learning generating $35.6 billion in 2011. North American e-learning generated $23.3 billion in revenue in 2013, with a 9% growth rate in cloud-based authoring tools and learning platforms.

Semantic Web

From Wikipedia, the free encyclopedia
A tag cloud (a typical Web 3.0 phenomenon in itself) presenting Web 3.0 themes

The Semantic Web, sometimes known as Web 3.0, is an extension of the World Wide Web through standards set by the World Wide Web Consortium (W3C). The goal of the Semantic Web is to make Internet data machine-readable.

To enable the encoding of semantics with the data, technologies such as Resource Description Framework (RDF) and Web Ontology Language (OWL) are used. These technologies are used to formally represent metadata. For example, ontology can describe concepts, relationships between entities, and categories of things. These embedded semantics offer significant advantages such as reasoning over data and operating with heterogeneous data sources. These standards promote common data formats and exchange protocols on the Web, fundamentally the RDF. According to the W3C, "The Semantic Web provides a common framework that allows data to be shared and reused across application, enterprise, and community boundaries." The Semantic Web is therefore regarded as an integrator across different content and information applications and systems.

History

The term was coined by Tim Berners-Lee for a web of data (or data web) that can be processed by machines—that is, one in which much of the meaning is machine-readable. While its critics have questioned its feasibility, proponents argue that applications in library and information science, industry, biology and human sciences research have already proven the validity of the original concept.

The idea of adding semantics to the Web predates the term itself. Berners-Lee discussed the need for semantics in the Web at the first International World Wide Web Conference in 1994. In 1998, he published a design document titled "Semantic Web Road map", outlining the architecture for a web of machine-processable data. The first patent for the creation of a semantic web was filed by Amit Sheth et al. on 30 October 2001.

Berners-Lee originally expressed his vision of the Semantic Web in 1999 as follows:

I have a dream for the Web [in which computers] become capable of analyzing all the data on the Web – the content, links, and transactions between people and computers. A "Semantic Web", which makes this possible, has yet to emerge, but when it does, the day-to-day mechanisms of trade, bureaucracy and our daily lives will be handled by machines talking to machines. The "intelligent agents" people have touted for ages will finally materialize.

The 2001 Scientific American article by Berners-Lee, Hendler, and Lassila described an expected evolution of the existing Web to a Semantic Web. In 2006, Berners-Lee and colleagues stated that: "This simple idea…remains largely unrealized". In 2013, more than four million Web domains (out of roughly 250 million total) contained Semantic Web markup.

Example

In the following example, the text "Paul Schuster was born in Dresden" on a website will be annotated, connecting a person with their place of birth. The following HTML fragment shows how a small graph is being described, in RDFa-syntax using a schema.org vocabulary and a Wikidata ID:

<div vocab="https://schema.org/" typeof="Person">
  <span property="name">Paul Schuster</span> was born in
  <span property="birthPlace" typeof="Place" href="https://www.wikidata.org/entity/Q1731">
    <span property="name">Dresden</span>.
  </span>
</div>
Graph resulting from the RDFa example

The example defines the following five triples (shown in Turtle syntax). Each triple represents one edge in the resulting graph: the first element of the triple (the subject) is the name of the node where the edge starts, the second element (the predicate) the type of the edge, and the last and third element (the object) either the name of the node where the edge ends or a literal value (e.g. a text, a number, etc.).

 _:a <https://www.w3.org/1999/02/22-rdf-syntax-ns#type> <https://schema.org/Person> .
 _:a <https://schema.org/name> "Paul Schuster" .
 _:a <https://schema.org/birthPlace> <https://www.wikidata.org/entity/Q1731> .
 <https://www.wikidata.org/entity/Q1731> <https://schema.org/itemtype> <https://schema.org/Place> .
 <https://www.wikidata.org/entity/Q1731> <https://schema.org/name> "Dresden" .

The triples result in the graph shown in the given figure.

Graph resulting from the RDFa example, enriched with further data from the Web

One of the advantages of using Uniform Resource Identifiers (URIs) is that they can be dereferenced using the HTTP protocol. According to the so-called Linked Open Data principles, such a dereferenced URI should result in a document that offers further data about the given URI. In this example, all URIs, both for edges and nodes (e.g. http://schema.org/Person, http://schema.org/birthPlace, http://www.wikidata.org/entity/Q1731) can be dereferenced and will result in further RDF graphs, describing the URI, e.g. that Dresden is a city in Germany, or that a person, in the sense of that URI, can be fictional.

The second graph shows the previous example, but now enriched with a few of the triples from the documents that result from dereferencing https://schema.org/Person (green edge) and https://www.wikidata.org/entity/Q1731 (blue edges).

Additionally to the edges given in the involved documents explicitly, edges can be automatically inferred: the triple

 _:a <https://www.w3.org/1999/02/22-rdf-syntax-ns#type> <http://schema.org/Person> .

from the original RDFa fragment and the triple

 <https://schema.org/Person> <http://www.w3.org/2002/07/owl#equivalentClass> <http://xmlns.com/foaf/0.1/Person> .

from the document at https://schema.org/Person (green edge in the figure) allow to infer the following triple, given OWL semantics (red dashed line in the second Figure):

 _:a <https://www.w3.org/1999/02/22-rdf-syntax-ns#type> <http://xmlns.com/foaf/0.1/Person> .

Background

The concept of the semantic network model was formed in the early 1960s by researchers such as the cognitive scientist Allan M. Collins, linguist Ross Quillian and psychologist Elizabeth F. Loftus as a form to represent semantically structured knowledge. When applied in the context of the modern internet, it extends the network of hyperlinked human-readable web pages by inserting machine-readable metadata about pages and how they are related to each other. This enables automated agents to access the Web more intelligently and perform more tasks on behalf of users. The term "Semantic Web" was coined by Tim Berners-Lee, the inventor of the World Wide Web and director of the World Wide Web Consortium ("W3C"), which oversees the development of proposed Semantic Web standards. He defines the Semantic Web as "a web of data that can be processed directly and indirectly by machines".

Many of the technologies proposed by the W3C already existed before they were positioned under the W3C umbrella. These are used in various contexts, particularly those dealing with information that encompasses a limited and defined domain, and where sharing data is a common necessity, such as scientific research or data exchange among businesses. In addition, other technologies with similar goals have emerged, such as microformats.

Limitations of HTML

Many files on a typical computer can be loosely divided into either human-readable documents, or machine-readable data. Examples of human-readable document files are mail messages, reports, and brochures. Examples of machine-readable data files are calendars, address books, playlists, and spreadsheets, which are presented to a user using an application program that lets the files be viewed, searched, and combined.

Currently, the World Wide Web is based mainly on documents written in Hypertext Markup Language (HTML), a markup convention that is used for coding a body of text interspersed with multimedia objects such as images and interactive forms. Metadata tags provide a method by which computers can categorize the content of web pages. In the examples below, the field names "keywords", "description" and "author" are assigned values such as "computing", and "cheap widgets for sale" and "John Doe".

<meta name="keywords" content="computing, computer studies, computer" />
<meta name="description" content="Cheap widgets for sale" />
<meta name="author" content="John Doe" />

Because of this metadata tagging and categorization, other computer systems that want to access and share this data can easily identify the relevant values.

With HTML and a tool to render it (perhaps web browser software, perhaps another user agent), one can create and present a page that lists items for sale. The HTML of this catalog page can make simple, document-level assertions such as "this document's title is 'Widget Superstore'", but there is no capability within the HTML itself to assert unambiguously that, for example, item number X586172 is an Acme Gizmo with a retail price of €199, or that it is a consumer product. Rather, HTML can only say that the span of text "X586172" is something that should be positioned near "Acme Gizmo" and "€199", etc. There is no way to say "this is a catalog" or even to establish that "Acme Gizmo" is a kind of title or that "€199" is a price. There is also no way to express that these pieces of information are bound together in describing a discrete item, distinct from other items perhaps listed on the page.

Semantic HTML refers to the traditional HTML practice of markup following intention, rather than specifying layout details directly. For example, the use of <em> denoting "emphasis" rather than <i>, which specifies italics. Layout details are left up to the browser, in combination with Cascading Style Sheets. But this practice falls short of specifying the semantics of objects such as items for sale or prices.

Microformats extend HTML syntax to create machine-readable semantic markup about objects including people, organizations, events and products. Similar initiatives include RDFa, Microdata and Schema.org.

Semantic Web solutions

The Semantic Web takes the solution further. It involves publishing in languages specifically designed for data: Resource Description Framework (RDF), Web Ontology Language (OWL), and Extensible Markup Language (XML). HTML describes documents and the links between them. RDF, OWL, and XML, by contrast, can describe arbitrary things such as people, meetings, or airplane parts.

These technologies are combined in order to provide descriptions that supplement or replace the content of Web documents. Thus, content may manifest itself as descriptive data stored in Web-accessible databases, or as markup within documents (particularly, in Extensible HTML (XHTML) interspersed with XML, or, more often, purely in XML, with layout or rendering cues stored separately). The machine-readable descriptions enable content managers to add meaning to the content, i.e., to describe the structure of the knowledge we have about that content. In this way, a machine can process knowledge itself, instead of text, using processes similar to human deductive reasoning and inference, thereby obtaining more meaningful results and helping computers to perform automated information gathering and research.

An example of a tag that would be used in a non-semantic web page:

<item>blog</item>

Encoding similar information in a semantic web page might look like this:

<item rdf:about="https://example.org/semantic-web/">Semantic Web</item>

Tim Berners-Lee calls the resulting network of Linked Data the Giant Global Graph, in contrast to the HTML-based World Wide Web. Berners-Lee posits that if the past was document sharing, the future is data sharing. His answer to the question of "how" provides three points of instruction. One, a URL should point to the data. Two, anyone accessing the URL should get data back. Three, relationships in the data should point to additional URLs with data.

Tags and identifiers

Tags, including hierarchical categories and tags that are collaboratively added and maintained (e.g. with folksonomies) can be considered part of, of potential use to or a step towards the semantic Web vision.

Unique identifiers, including hierarchical categories and collaboratively added ones, analysis tools and metadata, including tags, can be used to create forms of semantic webs – webs that are to a certain degree semantic. In particular, such has been used for structuring scientific research i.a. by research topics and scientific fields by the projects OpenAlex,[22][23][24] Wikidata and Scholia which are under development and provide APIs, Web-pages, feeds and graphs for various semantic queries.

Web 3.0

Tim Berners-Lee has described the Semantic Web as a component of Web 3.0.

People keep asking what Web 3.0 is. I think maybe when you've got an overlay of scalable vector graphics – everything rippling and folding and looking misty – on Web 2.0 and access to a semantic Web integrated across a huge space of data, you'll have access to an unbelievable data resource …

— Tim Berners-Lee, 2006

"Semantic Web" is sometimes used as a synonym for "Web 3.0", though the definition of each term varies.

Beyond Web 3.0

The next generation of the Web is often termed Web 4.0, but its definition is not clear. According to some sources, it is a Web that involves artificial intelligence, the internet of things, pervasive computing, ubiquitous computing and the Web of Things among other concepts. According to the European Union, Web 4.0 is "the expected fourth generation of the World Wide Web. Using advanced artificial and ambient intelligence, the internet of things, trusted blockchain transactions, virtual worlds and XR capabilities, digital and real objects and environments are fully integrated and communicate with each other, enabling truly intuitive, immersive experiences, seamlessly blending the physical and digital worlds".

Challenges

Some of the challenges for the Semantic Web include vastness, vagueness, uncertainty, inconsistency, and deceit. Automated reasoning systems will have to deal with all of these issues in order to deliver on the promise of the Semantic Web.

  • Vastness: The World Wide Web contains many billions of pages. The SNOMED CT medical terminology ontology alone contains 370,000 class names, and existing technology has not yet been able to eliminate all semantically duplicated terms. Any automated reasoning system will have to deal with truly huge inputs.
  • Vagueness: These are imprecise concepts like "young" or "tall". This arises from the vagueness of user queries, of concepts represented by content providers, of matching query terms to provider terms and of trying to combine different knowledge bases with overlapping but subtly different concepts. Fuzzy logic is the most common technique for dealing with vagueness.
  • Uncertainty: These are precise concepts with uncertain values. For example, a patient might present a set of symptoms that correspond to a number of different distinct diagnoses each with a different probability. Probabilistic reasoning techniques are generally employed to address uncertainty.
  • Inconsistency: These are logical contradictions that will inevitably arise during the development of large ontologies, and when ontologies from separate sources are combined. Deductive reasoning fails catastrophically when faced with inconsistency, because "anything follows from a contradiction". Defeasible reasoning and paraconsistent reasoning are two techniques that can be employed to deal with inconsistency.
  • Deceit: This is when the producer of the information is intentionally misleading the consumer of the information. Cryptography techniques are currently utilized to alleviate this threat. By providing a means to determine the information's integrity, including that which relates to the identity of the entity that produced or published the information, however credibility issues still have to be addressed in cases of potential deceit.

This list of challenges is illustrative rather than exhaustive, and it focuses on the challenges to the "unifying logic" and "proof" layers of the Semantic Web. The World Wide Web Consortium (W3C) Incubator Group for Uncertainty Reasoning for the World Wide Web (URW3-XG) final report lumps these problems together under the single heading of "uncertainty". Many of the techniques mentioned here will require extensions to the Web Ontology Language (OWL) for example to annotate conditional probabilities. This is an area of active research.

Standards

Standardization for Semantic Web in the context of Web 3.0 is under the care of W3C.

Components

The term "Semantic Web" is often used more specifically to refer to the formats and technologies that enable it. The collection, structuring and recovery of linked data are enabled by technologies that provide a formal description of concepts, terms, and relationships within a given knowledge domain. These technologies are specified as W3C standards and include:

The Semantic Web Stack illustrates the architecture of the Semantic Web. The functions and relationships of the components can be summarized as follows:

  • XML provides an elemental syntax for content structure within documents, yet associates no semantics with the meaning of the content contained within. XML is not at present a necessary component of Semantic Web technologies in most cases, as alternative syntaxes exist, such as Turtle. Turtle is a de facto standard, but has not been through a formal standardization process.
  • XML Schema is a language for providing and restricting the structure and content of elements contained within XML documents.
  • RDF is a simple language for expressing data models, which refer to objects ("web resources") and their relationships. An RDF-based model can be represented in a variety of syntaxes, e.g., RDF/XML, N3, Turtle, and RDFa. RDF is a fundamental standard of the Semantic Web.
  • RDF Schema extends RDF and is a vocabulary for describing properties and classes of RDF-based resources, with semantics for generalized-hierarchies of such properties and classes.
  • OWL adds more vocabulary for describing properties and classes: among others, relations between classes (e.g. disjointness), cardinality (e.g. "exactly one"), equality, richer typing of properties, characteristics of properties (e.g. symmetry), and enumerated classes.
  • SPARQL is a protocol and query language for semantic web data sources.
  • RIF is the W3C Rule Interchange Format. It is an XML language for expressing Web rules that computers can execute. RIF provides multiple versions, called dialects. It includes a RIF Basic Logic Dialect (RIF-BLD) and RIF Production Rules Dialect (RIF PRD).

Current state of standardization

Well-established standards:

Not yet fully realized:

Applications

The intent is to enhance the usability and usefulness of the Web and its interconnected resources by creating semantic web services, such as:

  • Servers that expose existing data systems using the RDF and SPARQL standards. Many converters to RDF exist from different applications. Relational databases are an important source. The semantic web server attaches to the existing system without affecting its operation.
  • Documents "marked up" with semantic information (an extension of the HTML <meta> tags used in today's Web pages to supply information for Web search engines using web crawlers). This could be machine-understandable information about the human-understandable content of the document (such as the creator, title, description, etc.) or it could be purely metadata representing a set of facts (such as resources and services elsewhere on the site). Note that anything that can be identified with a Uniform Resource Identifier (URI) can be described, so the semantic web can reason about animals, people, places, ideas, etc. There are four semantic annotation formats that can be used in HTML documents; Microformat, RDFa, Microdata and JSON-LD. Semantic markup is often generated automatically, rather than manually.
Arguments as distinct semantic units with specified relations and version control on Kialo
  • Common metadata vocabularies (ontologies) and maps between vocabularies that allow document creators to know how to mark up their documents so that agents can use the information in the supplied metadata (so that Author in the sense of 'the Author of the page' will not be confused with Author in the sense of a book that is the subject of a book review).
  • Automated agents to perform tasks for users of the semantic web using this data.
  • Semantic translation. An alternative or complementary approach are improvements to contextual and semantic understanding of texts – these could be aided via Semantic Web methods so that only increasingly small numbers of mistranslations need to be corrected in manual or semi-automated post-editing.
  • Web-based services (often with agents of their own) to supply information specifically to agents, for example, a Trust service that an agent could ask if some online store has a history of poor service or spamming.
  • Semantic Web ideas are implemented in collaborative structured argument mapping sites where their relations are organized semantically, arguments can be mirrored (linked) to multiple places, reused (copied), rated, and changed as semantic distinct units. Ideas for such, or a more widely adopted "World Wide Argument Web", go back to at least 2007 and have been implemented to some degree in Argüman and Kialo. Further steps towards semantic web services may include enabling "Querying", argument search engines, and "summarizing the contentious and agreed-upon points of a discussion".

Such services could be useful to public search engines, or could be used for knowledge management within an organization. Business applications include:

  • Facilitating the integration of information from mixed sources
  • Dissolving ambiguities in corporate terminology
  • Improving information retrieval thereby reducing information overload and increasing the refinement and precision of the data retrieved
  • Identifying relevant information with respect to a given domain
  • Providing decision making support

In a corporation, there is a closed group of users and the management is able to enforce company guidelines like the adoption of specific ontologies and use of semantic annotation. Compared to the public Semantic Web there are lesser requirements on scalability and the information circulating within a company can be more trusted in general; privacy is less of an issue outside of handling of customer data.

Skeptical reactions

Practical feasibility

Critics question the basic feasibility of a complete or even partial fulfillment of the Semantic Web, pointing out both difficulties in setting it up and a lack of general-purpose usefulness that prevents the required effort from being invested. In a 2003 paper, Marshall and Shipman point out the cognitive overhead inherent in formalizing knowledge, compared to the authoring of traditional web hypertext:

While learning the basics of HTML is relatively straightforward, learning a knowledge representation language or tool requires the author to learn about the representation's methods of abstraction and their effect on reasoning. For example, understanding the class-instance relationship, or the superclass-subclass relationship, is more than understanding that one concept is a "type of" another concept. [...] These abstractions are taught to computer scientists generally and knowledge engineers specifically but do not match the similar natural language meaning of being a "type of" something. Effective use of such a formal representation requires the author to become a skilled knowledge engineer in addition to any other skills required by the domain. [...] Once one has learned a formal representation language, it is still often much more effort to express ideas in that representation than in a less formal representation [...]. Indeed, this is a form of programming based on the declaration of semantic data and requires an understanding of how reasoning algorithms will interpret the authored structures.

According to Marshall and Shipman, the tacit and changing nature of much knowledge adds to the knowledge engineering problem, and limits the Semantic Web's applicability to specific domains. A further issue that they point out are domain- or organization-specific ways to express knowledge, which must be solved through community agreement rather than only technical means. As it turns out, specialized communities and organizations for intra-company projects have tended to adopt semantic web technologies greater than peripheral and less-specialized communities. The practical constraints toward adoption have appeared less challenging where domain and scope is more limited than that of the general public and the World-Wide Web.

Finally, Marshall and Shipman see pragmatic problems in the idea of (Knowledge Navigator-style) intelligent agents working in the largely manually curated Semantic Web:

In situations in which user needs are known and distributed information resources are well described, this approach can be highly effective; in situations that are not foreseen and that bring together an unanticipated array of information resources, the Google approach is more robust. Furthermore, the Semantic Web relies on inference chains that are more brittle; a missing element of the chain results in a failure to perform the desired action, while the human can supply missing pieces in a more Google-like approach. [...] cost-benefit tradeoffs can work in favor of specially-created Semantic Web metadata directed at weaving together sensible well-structured domain-specific information resources; close attention to user/customer needs will drive these federations if they are to be successful.

Cory Doctorow's critique ("metacrap") is from the perspective of human behavior and personal preferences. For example, people may include spurious metadata into Web pages in an attempt to mislead Semantic Web engines that naively assume the metadata's veracity. This phenomenon was well known with metatags that fooled the Altavista ranking algorithm into elevating the ranking of certain Web pages: the Google indexing engine specifically looks for such attempts at manipulation. Peter Gärdenfors and Timo Honkela point out that logic-based semantic web technologies cover only a fraction of the relevant phenomena related to semantics.

Censorship and privacy

Enthusiasm about the semantic web could be tempered by concerns regarding censorship and privacy. For instance, text-analyzing techniques can now be easily bypassed by using other words, metaphors for instance, or by using images in place of words. An advanced implementation of the semantic web would make it much easier for governments to control the viewing and creation of online information, as this information would be much easier for an automated content-blocking machine to understand. In addition, the issue has also been raised that, with the use of FOAF files and geolocation meta-data, there would be very little anonymity associated with the authorship of articles on things such as a personal blog. Some of these concerns were addressed in the "Policy Aware Web" project and is an active research and development topic.

Doubling output formats

Another criticism of the semantic web is that it would be much more time-consuming to create and publish content because there would need to be two formats for one piece of data: one for human viewing and one for machines. However, many web applications in development are addressing this issue by creating a machine-readable format upon the publishing of data or the request of a machine for such data. The development of microformats has been one reaction to this kind of criticism. Another argument in defense of the feasibility of semantic web is the likely falling price of human intelligence tasks in digital labor markets, such as Amazon's Mechanical Turk.

Specifications such as eRDF and RDFa allow arbitrary RDF data to be embedded in HTML pages. The GRDDL (Gleaning Resource Descriptions from Dialects of Language) mechanism allows existing material (including microformats) to be automatically interpreted as RDF, so publishers only need to use a single format, such as HTML.

Research activities on corporate applications

The first research group explicitly focusing on the Corporate Semantic Web was the ACACIA team at INRIA-Sophia-Antipolis, founded in 2002. Results of their work include the RDF(S) based Corese search engine, and the application of semantic web technology in the realm of distributed artificial intelligence for knowledge management (e.g. ontologies and multi-agent systems for corporate semantic Web) and E-learning.

Since 2008, the Corporate Semantic Web research group, located at the Free University of Berlin, focuses on building blocks: Corporate Semantic Search, Corporate Semantic Collaboration, and Corporate Ontology Engineering.

Ontology engineering research includes the question of how to involve non-expert users in creating ontologies and semantically annotated content and for extracting explicit knowledge from the interaction of users within enterprises.

Future of applications

Tim O'Reilly, who coined the term Web 2.0, proposed a long-term vision of the Semantic Web as a web of data, where sophisticated applications are navigating and manipulating it. The data web transforms the World Wide Web from a distributed file system into a distributed database.

Web3

From Wikipedia, the free encyclopedia

Web3 is an idea for a new iteration of the World Wide Web which incorporates concepts such as decentralization, blockchain technologies, tokenomics, and privacy-enhancing technologies. The term is sometimes confused with the Semantic Web, as both have sometimes been referred to as "Web 3.0". Some technologists and journalists have contrasted it with Web 2.0, in which they say user-generated content is controlled by a small group of companies referred to as Big Tech. The term "web 3.0" as applied to blockchain technology was coined in 2014 by Ethereum co-founder Gavin Wood, and the idea gained interest in 2021 from cryptocurrency enthusiasts, large technology companies, and venture capital firms.

Academic surveys describe Web3 as combining smart contract platforms, peer-to-peer networks, and cryptographic mechanisms, while noting trade-offs involving scalability, interoperability, and governance.

Critics have expressed concerns over the centralization of wealth to a small group of investors and individuals, or a loss of privacy due to more expansive data collection. Billionaires like Elon Musk and Jack Dorsey have argued that web3 only serves as a buzzword or marketing term.

Background

Web 1.0 and Web 2.0 refer to eras in the history of the World Wide Web as it evolved through various technologies and formats. Web 1.0 refers roughly to the period from 1991 to 2004, where most sites consisted of static pages, and the vast majority of users were consumers, not producers of content. Web 2.0 is based around the idea of "the web as platform" and centers on user-created content uploaded to forums, social media and networking services, blogs, and wikis, among other services. Web 2.0 is generally considered to have begun around 2004 and continues to the current day.

Terminology and concept

Web3 is distinct from Tim Berners-Lee's 1999 concept of a Semantic Web, which was also sometimes referred to as Web 3.0. While the Semantic Web envisioned a web of linked data, web3 in the blockchain context refers to a decentralized internet built upon distributed ledger technologies. Ethereum co-founder Gavin Wood first popularized the use of the term Web 3.0 in a blockchain context in 2014. Wood used the term to refer to a "decentralized online ecosystem based on blockchain." "Web3" and "Web 3.0" have been used interchangeably by later writers, leading to confusion between the two concepts. In popular and industry usage, "Web 3.0" is sometimes used interchangeably with "web3" to refer to blockchain-based proposals rather than the Semantic Web.

The definition of the term has been described by Bloomberg journalist Olga Kharif as "hazy", but it revolves around the idea of decentralization and often incorporate blockchain technologies, such as various cryptocurrencies and non-fungible tokens (NFTs). Kharif has described web3 as an idea that "would build financial assets, in the form of tokens, into the inner workings of almost anything you do online". Web3 has been proposed as a possible response to the over-centralization of the web in a few Big Tech companies. Web3 could potentially improve data security, scalability, and privacy. According Kharif in December 2021, skeptics say the idea "is a long way from proving its use beyond niche applications, many of them tools aimed at crypto traders". DuPont et al (2024) describe web3 as an umbrella term for NFTs, cryptocurrency, and blockchain which appeals to users who are "not necessarily plugged into the cypher-punk or crypto-anarchist ideals of Bitcoin". Rohn (2025) describes the term as synonymous with "blockchain economy".

In 2019, The Conversation quoted legal scholars discussing concerns over the difficulty of regulating a decentralized web, which they reported might make it more difficult to prevent cybercrime, online harassment, hate speech, and the dissemination of child sexual abuse material. The concept of Web3 has also been described as a part of a cryptocurrency bubble, and as an extension of harmful and short-lived blockchain-based trends such as NFTs. The use of cryptocurrencies, NFTs, and blockchains in general also introduce potential harmful environmental effects. Others have expressed beliefs that web3 and the associated technologies are a pyramid scheme.

A policy brief published by the Bennett Institute for Public Policy at the University of Cambridge in March 2022 defined web3 as "the putative next generation of the web's technical, legal, and payments infrastructure—including blockchain, smart contracts and cryptocurrencies."

Web3 proposals often include decentralized autonomous organizations (DAOs) and decentralized finance (DeFi).

Decentralization

Ethics professor Kevin Werbach said in 2021 that "many so-called 'Web 3.0' solutions are not as decentralized as they seem, while others have yet to show they are scalable, secure and accessible enough for the mass market", adding that this "may change, but it's not a given that all these limitations will be overcome".

In early 2022, Moxie Marlinspike, creator of Signal, wrote about web3 as not being as decentralized as it appears to be, mainly due to consolidation in the cryptocurrency field, including in blockchain application programming interfaces which are currently mainly controlled by the companies Alchemy and Infura; cryptocurrency exchanges which are mainly dominated by Binance, Coinbase, MetaMask, and OpenSea; and the stablecoin market which is currently dominated by Tether. Marlinspike also remarked that the new web resembles the old web.

Reception

In 2021, interest in the web3 concept began to increase. Particular interest spiked toward the end of 2021, largely due to interest from cryptocurrency enthusiasts and investments from high-profile technologists and companies. Executives from venture capital firm Andreessen Horowitz traveled to Washington, DC, in October 2021 to lobby for the idea as a potential solution to questions about regulation of the web, with which policymakers have been grappling. The New York Times and Fortune reported in December 2021 that $27 billion had been invested in blockchain projects due to interest in the Web3 concept.

In late 2021, Reddit, Discord, and other Web 2.0 companies experimented with incorporating web3 technologies into their platforms. On November 8, 2021, Discord CEO Jason Citron tweeted a screenshot suggesting the platform might be integrating cryptocurrency wallets into their platform. Two days later, and after heavy user backlash, Discord announced they had no plans to integrate such technologies and that it was an internal-only concept that had been developed in a company-wide hackathon.

In November 2021, James Grimmelmann of Cornell University referred to web3 as vaporware, calling it "a promised future internet that fixes all the things people don't like about the current internet, even when it's contradictory." Grimmelmann also argued that moving the internet toward a blockchain-focused infrastructure would centralize and cause more data collection compared to the current internet. In December 2021, software engineer Stephen Diehl described Web3 as a "vapid marketing campaign that attempts to reframe the public's negative associations of crypto assets into a false narrative about disruption of legacy tech company hegemony." Liam Proven, writing for The Register, concluded that web3 is "a myth, a fairy story. It's what parents tell their kids about at night if they want them to grow up to become economists". That same month, Jack Dorsey, co-founder and former CEO of Twitter, dismissed web3 as a "venture capitalists' plaything". Dorsey opined that web3 will not democratize the internet, but it will shift power from players like Facebook to venture capital funds like Andreessen Horowitz. Also in December 2021, SpaceX and Tesla CEO Elon Musk expressed skepticism about web3 in a tweet, saying that web3 "seems more marketing buzzword than reality right now."

Prime number

From Wikipedia, the free encyclopedia https://en.wikipedia.org/wiki/Prime_number Composite numbers can...