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See all the wonderful things Filiz Kara has made

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Eğitim sürecinde öğrenci ve öğretmenin online olarak katıldığı, var olan teknolojiler aracılığıyla sanal ortamda canlı bir şekilde derslerin işlendiği ve isteğe bağlı olarak öğrencinin yüklenilen dersleri tekrar seyredebildiği akılcı ve bir o kadar da pratik bir eğitim şeklidir.

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Synchronous learning is online e-learning while asynchronous learning can be carried out when teacher and students are offline. They both have certain advantages and disadvantages. For more detail information, you can click on the link.

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November 26, 2014

"Both synchronous and asynchronous approaches to eLearning have their advantages and disadvantages. While one approach may be more effective than the other in a given situation, it is not to say it is best for all learning situations."

When a company decides to embark on an eLearning project, there are many decisions to make. One of these decisions is about making the choice between synchronous and asynchronous learning. In synchronous learning, the instructor and the students are present at the same time and place (whether a physical location or virtual). Asynchronous learning is independent learning, and students can proceed at any time and at their own pace, whether the instructor and the other students are present or not. What are the advantages and disadvantages of each of these approaches?

Synchronous learning

Synchronous learning often employs classroom style elements, such as group discussions, team activities, and other elements that an instructor supervises. The learning blocks are completed on a schedule, like a regular classroom, and there is plenty of interaction between the students and the instructor. Synchronous learning can take place either in a classroom setting, in an online chat room, a video conference, or using a web-conferencing application, and there is near constant interaction between those in attendance.

Main advantages of synchronous learning

Instructors interact with multiple students in real-time, making group activities possible

Instructors can explain certain concepts when students are struggling

Students can get immediate answers about any aspect of the learning process

Main disadvantages of synchronous learning:

Both the student and the instructor have to adhere to time schedules

Much of the training depends on the quality of the instructor

Students may not receive individual attention if other students are also in need

The learning pace is set by the instructor, not the students

Asynchronous learning

Asynchronous learning is a stand-alone approach, designed specifically for self-study. Students will gain knowledge from interacting with, reading, watching, and listening to different types of content. In most cases, the students are free to complete the coursework at their own pace, and are free to take tests at any time as well. Asynchronous is a great method for students who are juggling training as well as their normal workload and personal life.

Main advantages

Learners can study at any time and any pace, according to their own needs

Students have the ability to go back to pieces they need to brush up on

There is the opportunity to review outside resources to aid instruction

Main disadvantages

Limited access to an instructor and/or getting answers in real time

Some students may struggle without constant guidance and interaction

Not all instruction is best suited for self-paced learning

Choosing between synchronous and asynchronous learning

While synchronous and asynchronous learning each have their advantages and drawbacks, the best approach for a given design project is based on multiple factors. Here are three:

The students and their learning needs

The type of content

The time availability of your learners

For example:

If you are training a group that consists of individuals who have significant professional experience, who require lots of interaction and "talk time," and who prefer being around and working with their colleagues you may find a synchronous approach to work best, given a comparative familiarity with eLearning.

The nature of the content is vital in determining the style of learning. Should your content be filled with complex ideas and technical terms that require much explaining or situational context, you may find asynchronous is not the right approach.

When content is easy to digest or in reflection of a complex issue, asynchronous could be the right move.

Finally, the availability of your learners as well as their access to the Internet are important in making a decision on your approach. If your audience is juggling training, work, and extra schooling they may not have the time to devote to completing training in one sitting. This is where asynchronous training really shines-freedom to complete at one's own pace.

Both synchronous and asynchronous approaches to eLearning have their advantages and disadvantages. While one approach may be more effective than the other in a given situation, it is not to say it is best for all learning situations.

Think about each approach, and how your content and audience fit, to ensure you reach your learning objectives.

Topics Covered

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Comparing Asynchronous and Synchronous Learning

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November 26, 2014 "Both synchronous and asynchronous approaches to eLearning have their advantages and disadvantages. While one approach may be more effective than the other in a given situation, it is not to say it is best for all learning situations." When a company decides to embark on an eLearning project, there are many decisions to make.

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Using computer technology for delivering education or educational materials to students.

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It is a kind of instructional strategy promoting learning with "repetitive practice".

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Bilgisayar destekli öğretimde yapısal içerik veya aktiviteler öğrenciler tarafından bilgisayar üzerinden gerçekleştirilir. Ancak bilgisayar temelli öğretimde ise bilgisayar tüm eğitimsel ve yapısal öğretim yollarının kontrol aracı olarka kullanılır. Yani, öğrencilerle ilgili verileri kaydetme, anlatım metodunun ya da ders planını oluşturmak gibi bir çok amaçta aktif olarak kullanılır. 

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Bilgisayar destekli öğretimde yapısal içerik veya aktiviteler öğrenciler tarafından bilgisayar üzerinden gerçekleştirilir. Ancak bilgisayar temelli öğretimde ise bilgisayar tüm eğitimsel ve yapısal öğretim yollarının kontrol aracı olarka kullanılır. Yani, öğrencilerle ilgili verileri kaydetme, anlatım metodunun ya da ders planını oluşturmak gibi bir çok amaçta aktif olarak kullanılır. 

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The word podcast was created by combining the words “broadcasting” and “iPod." It is defined as a digital recording of an audio broadcast, available on the internet for other people.
For instance, in educational technology, lecturers can easily broadcast audio content, which students can then listen to at any time and wherever they are.

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Digital stories allow students to take a linear series of events and turn them into a multidimensional experience. It encourages them to communicate, collaborate, and research as well as to infuse media into the process. A plethora of tools and apps exists to create these projects, and all of them enable students to gain a deeper understanding of history as they explore the most effective way to retell it. http://edtechteacher.org/tools/multimedia/digital-storytelling/

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Just like in a real-world classroom, the student in a virtual classroom participates in syncronious instruction. The teacher and students are logged into the online platform at the same time.

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Okuma yazma bilmeyen bireyler için faydasının yanı sıra görme engelli bireylere çok farklı bir yaklaşım sunan ve hayat kurtarıcı dediğimiz teknolojik gelişmelerden biridir.

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The process of using story and digital media for personal expression.
This includes expressive outlets such as blogs, podcasts, and social media.

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Unfortunately two of the original digital storytelling examples in this post have either been taken down or redirected (LifeOfPi and Travis for Volvo Trucks). As such, we're updated this post with more recent examples. Thanks for reading!
At 8 Million Stories we combine digital marketing with digital storytelling. Most people know and understand digital marketing, but far fewer people have heard of digital storytelling.

In the socially connected world, the attention span of an audience becomes shorter as more and more mundane content is thrust in front of their eyes. Digital storytelling allows a brand to evoke emotion, and become more connected with consumers.

If content is at the heart of digital marketing, then we believe audiences are at the heart of digital storytelling. By creating a story that is authentic to your brand and to your audience, and then building content around that story, you will connect your brand to the lives of your consumers.

Here are six handpicked examples of digital storytelling where we believe great writing is combined with great imagery to create authentic content, and a brilliant digital story.

A Pulitzer prize winning project, 'Snow Fall' has practically become a verb in the Midtown Manhattan area, propelling the New York Times into new acclaimed journalism status. In the six days after Snow Fall's launch on December 20th, 2012, it had received more than 3.5 million page views and 2.9 million visitors.

Going beyond the facts and figures, many believe that thanks to the astounding success of Snow Fall, the New York Times can change the definition of journalism as we know it.

Not to be outdone by the New York Times, the Washington Post decided to have a go at digital storytelling themselves, by creating a highly visual story around celebrity baseball player Bryce Harper.

Combining video, data, graphics, and digital illustrations, the Washington Post have come up trumps with a great story around one of America's top three sports.

A moving documentary that turns into a multimedia showcase where you can use your webcam, mouse, laptop keys to move around an interactive map around Canmore, Banff National Park, Canada.

This interactive documentary isn't just about simple narrative. It's a story about technology, and how it shapes our interaction with the online world.

Another gem from the National Film Board of Canada, Welcome to Pine Point is a digital storytelling example of how we explore and remember the past. It's a multi-award winning story that combines film, writing and photographs. It was meant to be published as a book, but everyone agrees that the story is more powerful is digital form.

Get your headphones plugged in, and get ready to be taken on a boom and bust journey Of McDowell County, West Virginia. The sights, sounds and visual effects come at you thick and fast in this digital storytelling timeline example. Very slick, very interactive, and one of our favourites.

One of our favourite interactive content examples. Created back in 2013, Inequality.is highlights the disparity of economic wealth between the top 10% of the remaining 90% of Americans. Have a play yourself, and you might be surprised by the results. Factual, fun and playful, this is a great example of showcasing important political statements and facts that can resonate with all audiences.

You can also download a copy of our presentation, that highlights each of the six digital story examples listed above.
Let us know if you see any exceptional digital stories, and we'll add them to the comments section below.

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6 Great Examples of Digital Storytelling

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Unfortunately two of the original digital storytelling examples in this post have either been taken down or redirected (LifeOfPi and Travis for Volvo Trucks). As such, we're updated this post with more recent examples. Thanks for reading! At 8 Million Stories we combine digital marketing with digital storytelling.

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Computer based instruction vs Computer supported instruction

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Computer based instruction vs Computer supported instruction

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Computer based instruction uses computer technology. Computer based instruction combines technology and education.
Tutorials, simulations,  instructional games,drills and practices are used in CBI. In computer supported instruction, learning takes place through computer technology. Technology is used as a tool in computer supported instruction.

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Group of students working in computer lab.

Computer-based instruction, or CBI, is a teaching approach that integrates computer software programs with other teaching materials in the classroom. Other terms used for CBI are: computer-based training, computer-assisted instruction and computer-assisted learning. There are many ways CBI can be used in the classrooms or as standalone learning tools. Teachers use CBI for drills and practices, tutorials, simulations, and instructional games.

Drills and Practices

Drills and practices are used to help students master specific skills, and also help increase or refresh a body of knowledge. Drill and practice software, often in the form of games for younger students, is routinely used to improve math and language skills. For the programs to be effective, they must provide feedback or reinforcement to encourage and motivate students. These programs are basic and practical and can be applied in primary, secondary and college classrooms.

Tutorials

Tutorial software can help introduce content, provide practice and assess the learner's progress. Tutorials can be used in all types of school curriculum and by businesses for worker training. They can also be used as remedial or enrichment lessons. Since tutorials interact with the students on a one-on-one basis, instructors gain the extra time they need to do other things while the computer does the coaching.

Simulations

Simulation programs provide representations of real-life situations, phenomena or processes. Simulations are designed to be realistic, which helps students apply their knowledge in a realistic format. They are often used to teach subject areas such as science because they can imitate objects and explain processes as small as atomic interactions. Simulations help students solve problems and improve their thinking skills. Simulations can be implemented in primary, secondary and college classrooms. They are routinely used in law enforcement training.

Instructional Games

Instructional games provide students with the rules of play and an end goal. They can be used in primary, secondary, special education and college classrooms. They appeal to the students' senses through puzzles or arcade-style action and can motivate them through competition, fantasy and personal challenges. Instructional games provide and engaging substitute for worksheets or verbal drills.

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What Is Computer-Based Instruction in Education? | Synonym

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Group of students working in computer lab. Computer-based instruction, or CBI, is a teaching approach that integrates computer software programs with other teaching materials in the classroom. Other terms used for CBI are: computer-based training, computer-assisted instruction and computer-assisted learning. There are many ways CBI can be used in the classrooms or as standalone learning tools.

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self-paced instructional program that provides step by step information in presenting a concept or learning unit. tutorial can be done interdependent of time and teacher can be able to stop for breaks and to repeat section as needed.

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it is personalizing education for students. It requires the physical presence of both teacher and student, with some element of student control over time, place, path, or pace.

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Adaptive learning systems focus on providing personalized/individualized content for the learners

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The effective implementation and use of adaptive learning systems requires a broad, more inclusive conversation among institutions, vendors, and other stakeholders to realize the benefits of next-generation personalized learning.

Virtually every academic area that studies various anthropology disciplines has been largely based on the same research method: assemble groups of individuals into an experiment scenario, determine their average response to the conditions, and use the same averaging to extrapolate a general conclusion for all people. Our current didactic, transactional digital learning solutions are designed around the concept of sameness. It's like the lull before a storm: calm and uneventful.

A haboob -an intense dust storm carried on an atmospheric gravity current-can serve as an analogy of why this strategy doesn't work. Haboobs often arrive without warning, and the dust and debris they carry are blinding, making it impossible to see where you're going until after the sediment has settled. People's reactions to the giant wall of dust vary, from fear and a desire to travel as far in the opposite direction of the storm as possible, to a scientific or adventure-seeking fascination and a desire to chase after it.

Similarly, the swirl of talk about the innovations surrounding adaptive learning systems arrived unexpectedly (before many of us were fully aware of the developments) and involved high-level, abstract discussions, making it difficult to see where we are now or where we want to go. And reactions in the higher education community have been varied: some have ignored the new technologies, whereas others have quickly (too quickly?) embraced these learning systems. To clear the air, we need a larger, more inclusive conversation among institutions, vendors, and other stakeholders in order to provide more granular thinking around specific minimally viable requirements for the application of adaptive learning systems.

Calm Before the Storm: What Problems Do Adaptive Systems Hope to Solve?

Intelligent adaptive learning systems are quickly emerging but are still in experimental stages. The intended design of these data-adaptive solutions seeks to enable differentiated instruction at a personalized level of learning. New approaches to diagnostic and formative assessment design making use of adaptive intelligence are becoming more common. Adaptive learning systems are designed to dynamically adjust to the level or type of course content based on an individual student's abilities or skill attainment, in ways that accelerate a learner's performance with both automated and instructor interventions. These adaptive systems achieve this by helping to address learning challenges such as varying student learning ability, diverse student backgrounds, and resource limitations. The intent of these machine learning systems is to use proficiency and determine what a student really knows and to accurately and logically move students through a sequential learning path to prescribed learning outcomes and skill mastery. These specific features will transform first-generation digital learning systems. The advantages include, but are not limited to, the following:

Automated processes of student assessment and predictive analysis result in significant faculty time efficiencies.

Adaptive systems have the potential to solve the primary and perennial problem in public education: the overwhelming challenge of teachers or faculty being responsible for accomplishing learning mastery among a demographically diverse set of students.

Adaptive systems address the fundamentally different levels of prior knowledge, as well as course content progression based on students' skill and outcomes mastery measurement, decreasing faculty load in teaching and remediation to teaching and facilitating.

Executed effectively, the cost efficiencies in automated feedback and remediation without formal instructor intervention have shown major improvements in student learning.

Regulating course content degree of difficulty results in better course engagement and progression.

Students own their learning journey: real-time response to their ongoing coursework provides detailed feedback for self-mediation.

Adaptive systems encourage student ownership of their learningthrough automated feedback cycles prompting them to take action and advance independently of the class instructor.

Adaptive systems conform to individual students' varied lifestyles versus students having to conform to the system.

Faculty receive data with insights into individual students' needs. Adaptive systems can provide a level of automation allowing faculty to better allocate time to students who need remediation.

The technology actively uses research about how people learn. Correctly developed, adaptive systems reveal how individual students learn.

Adaptive systems empower faculty. Rich data analysis of student progression enables faculty to continually improve course design.

Traditional assessment methods inform both faculty and students too late in the learning cycle. Using timely and comprehensive data-driven feedback, adaptive systems can inform in real time.

Adaptive systems have the potential to shift education in the service of students by providing a student-centric design. The student-centered capabilities these systems aspire to provide are vastly unlike the current models for traditional classroom support-models deeply rooted in student administration. The underlying data systems and transaction timing (e.g., gradebook, end-of-course exams) are radically different from the real-time, learner-specific data that forms the foundation of student-centered learning.

No Two Dust Clouds Are Alike: Adaptive Learning Shapes and Sizes

There are many forms of adaptive learning systems. While classifications in other industries are narrower in defining their specific applications and use cases, there are no specific guidelines, taxonomies, or common vernacular for how various types of adaptive capabilities are described in the edtech industry. These new data-driven systems are expansive in categorical descriptions and desperately need more specifics to describe and evaluate appropriateness for any given instructional design and teaching and learning purpose. Key tenants of these first-generation adaptive systems include the following:

Automation: The ability to create automated processes that reduce more manual teaching processes in assessment, evaluation, remediation, and competency attainment

Sequencing: The ability to create a sequenced progression of skills and competencies contained in a finite learning path in a term or non-term unit of time

Assessment: The ability to employ combinations of benchmark, diagnostic, and formative assessments on a more immediate and continuous evaluation

Real-time data collection: The ability to collect, calculate, and evaluate data, from an array of sources, with some assumed inference method in real or near-real time

Self-organizing: The ability to self-organize information and data results from inferences to form ongoing and persistent feedback in the teaching and learning cycle

With these commonalities in mind, we have identified four types and variants of adaptive learning systems: machine-learning-based adaptive systems; advanced algorithm adaptive systems; rules-based adaptive systems; and decision-tree adaptive systems.

Machine-Learning-Based Adaptive Systems

Machine-learning-based adaptive platforms are the most sophisticated scientific method in which to establish a truly adaptive state. Machine learning (ML) is synonymous with pattern recognition, statistical modeling, predictive analytics, statistical regularities, and other forms of advanced adaptive capabilities. ML-based systems use programmed algorithms to create the adaptive science core and make real-time predictions about a learner's subject matter mastery. ML-based adaptive platforms use learning algorithms, known also as "learners," to create other algorithms, which in turn create adaptive sequences and predictive analytics that can continuously collect data and use it to move a student through a guided learning path. These ML learners continually harvest data in real time, determining students' proficiency in mastering objective-specific content modules. The data is then used to automatically adjust the overall sequence of skills or the type of content a student experiences. Learning algorithms create analytical models that are designed to produce reliable, repeatable decisions, and results reveal undiscovered insights in student mastery through ongoing evaluation of historical relationships and trends in the data.

What's unique about ML-based adaptive systems is their ability to detect how an individual learns and approaches a learning task as part of these intelligent systems, as well as provide accurate and timely feedback and improve student performance. Since ML-based systems are highly computational, analyzing billions of data bits in real time, system scalability can be a question from two perspectives: how these systems are efficiently coded; and the provisioning architecture used to process, load, and balance enormous amounts of data. At minimum, ML systems feature the following:

Continuous and dynamic improvement: The instructional method improves over time.

Learner profiles: A baseline profile is used to classify learners' characteristics, such as demographic information, learning style attributes, academic strengths and weaknesses, and learning preferences.

Personal path and pacing: Learners can automate the process of self-instructional pacing for any particular individualized learning path or time sequence that is predetermined.

Individualized feedback and remediation: The system has the capacity to infer an individual student's knowledge level and accurately provides general feedback and remediation activity based on collective performance knowledge.

Content-agnostic: The system assumes relative knowledge of student mastery based on a variety of learning mediums such as text, video, audio, and other content.

Advanced Algorithm Adaptive Systems

Advanced algorithm (AA) adaptive systems provide 1:1 computer-to-student interaction, making it scalable based on the type of content (usually mathematics and sciences). Content modules are prescribed to specific, individual learner profiles based on prior proven mastery of knowledge and applied knowledge activity. In AA systems, learning paths, feedback, and content are evaluated in real time by constantly analyzing data from the individual student and comparing it with data from other students exposed to the same or similar content. AA adaptive systems record and manage a large amount of data, tied to a learner profile, and record clickstreams, time intervals, assessment attempts, and other transactional behavioral data. In AA systems, learning paths are determined in real time, and feedback is provided instantly in response to the data analytics running in the background. Instructional methods and content are altered where certain predetermined learning paths are ineffective, and alternative content, feedback, or remediation is provided.

Rules-Based Adaptive Systems

Rules-based (RB) adaptive systems work on a preconceived set of rules and do not precisely adapt to an individual learner, using ML-like scientific methods. RB is not designed around an algorithmic approach; rather, the particular learning path in these systems is predetermined by rule sets that can change for individual learners, and feedback is provided once a learning unit is concluded. In a self-paced learner use case, the student's learning path is usually sequential and linear, with little-to-no personalized adaptation other than a predetermined set of conditions. Students may take a differentiated path through assessment of prior knowledge and can progress individually at their own pace. RB systems don't use learner profile information and learning characteristics. Students go through a predetermined learning path with predetermined assignment sequences but can, depending on persistent diagnostic assessments, advance or regress individually. Ongoing feedback is provided, and remediation is prescribed based on the predetermined set of rules. RB systems use a smaller, more contrived set of content, matching diagnostic and formative assessment rules with the next sequences of content. The instructional method is not improved as learners progress through a path unless the content sequence is manually altered.

Decision Tree Adaptive Systems

Decision tree (DT) adaptive systems are a relatively simple classification designed around the concept of a "tree" of prescribed and limited repositories of content, assessments, and answer banks. Usually DT systems have limited assessment item types that are binary in their form and function. DT is differentiated from RB in that the rules and associated learning materials are relatively static and do not change. DT systems don't take into consideration a specific learner profile but use a series of static "if this, then that" (IFTT) branching sequences. DT systems, depending on their design sophistication, can take the form of intelligent recommending systems but are not knowledge-based in that there is no inherent collective and historical knowledge capture. However, these systems can infer a learning path from case-based reasoning using basic IFTT logic that supports users in the learning process by the selection of an individual learning path interacting with an inventory of didactic learning content. DT systems use an established set of rules from a pre-prescribed set of content modules and pre-prescribed sets of assessments and answer banks. Using intervals of data and feedback, learner workflows are created and individualized work streams assigned throughout a set pace.

Seeding the Storm: Adaptive Learning Systems Requirements

There is increasing market ambiguity and growing concern about the "black box" of personalized learning. What is the real anatomy of the next promising personalized learning environment? As more market capital is being used to fuel and accelerate the development of adaptive learning systems by the private sector, higher education research must set the foundation for the key design requirements necessary for these next-generation systems to reach their full potential and meet their rhetorical promises. Adaptive learning systems require, at the core, an architecture that integrates key functions of modular content, assessment, and competency mapping that work together to support a personalized learning ecosystem.

At minimum, adaptive systems are composed of methods that organize modular content to be learned, multiple systems of assessment that track and evaluate students' abilities, and techniques of matching the presentation of content to individual learners in dynamic and personalized ways. Based on close observation of the work in which we are currently engaged in adaptive learning systems experimentation, we have constructed the following framework for a minimally viable product design-or what should be inside the black box. In the outline of core requirements, the three integrated and interdependent areas of content, assessment, and competency frameworks are essential in creating true adaptive states.

Content

Content Scaffolding: Content modules are designed to index critical concepts, which present a multistep problem as a series of smaller problems used to help students synthesize concepts when those concepts are still new to them. Scaffolding is especially relevant in adaptive systems where students need to apply their knowledge to new scenarios learned. Scaffolding methods are designed to statistically provide different follow-up questions to various students, depending on their level of proficiency. Content scaffolding allows students to apply the original learned concept to a variety of analogous situations.

Social Interaction: Social connectedness within an adaptive course is a key element in the multiple measures of both cognition and engagement. In the adaptive field, content-driven group collaboration extends the functionality of student mastery measurement. Personalized learning can and should be incorporated to facilitate collaboration between learning teams and forums, as well as resource-sharing capabilities. While no current systems have integrated synchronous capabilities, adaptive systems can be a key foundation to link personalization of customized content to students' profiles and interests, automated grouping of users with parallel interests, and even peer-ranking indexes.

Content Interoperability: Adaptive systems need to take into consideration content management and interoperability requirements. Interoperable content models need to have built-in methods by which specific topics or content domains are structured with aligned learning outcomes and learning task definitions. Adaptive systems need to be capable of configuring predetermined content sequencing as well as variable sequencing tied to ongoing measurements of skill mastery. They need to have interoperable content management systems in order to identify content appropriateness based on what the system knows about what the student knows in an ongoing and dynamic way.

Metadata: Equally important is the built-in capability of developing key identifiers linking content to other content where there are concept interrelationships and adjacencies. Simply defined, metadata is data that describes information about other data-a method for advanced tagging of content with underlying data about each module of content (e.g., subject area identifiers, age and level, learning outcome). All adaptive systems, no matter their level of sophistication, must have a defined, advanced metadata schema in order to achieve a personalized state.

Assessment

Normed- vs. Criterion-Referenced Assessments: Normed-reference (NR) assessments are designed to compare individual students' achievement to that of a "norm group," or a representative sample of peers in the same or similar cohort group. NR assessments do not compare the students' achievements tied to a particular set of standards or defined outcomes for what students actually should know and be able to do; they only compare students to other students in the same norm group. In NR, learners do not address questions that refer to a particular skill and demonstrated knowledge. NR tests make it mathematically impossible for all the children to be above average. Conversely, criterion-referenced (CR) assessmentsare designed to show how students achieve as directly referenced to a set of standards or defined outcomes. CR assessments are specifically designed to determine whether students have mastered the knowledge and skills contained in the defined set of outcomes. CR tests begin with the outcome in mind: the knowledge and skills students are expected to learn and be able to apply. Most adaptive systems in the market today use only an NR assessment process, which is the key component in determining adaptive content placement. It is merely a mathematical computation and requires a significant number of student participants in order to be statistically accurate.

Predictive Psychometric Design: Truly designed adaptive tests have the ability to achieve accurate placement of a student in an individualized learning path. Predictive capabilities are derived from adaptive assessment design whereby each test question can be altered by the response to the previous question, and the test rigor is variable. Adaptive assessment must use a stable scale, which is the only way to accurately show a student's growth over time, regardless of grade-level performance. There must be built-in predictive values using mechanisms that automate the selection of questions based on the student's responses and reports that relate scores to the student's instructional needs.

Diagnostic Classification Modeling: Diagnostic classification models determine learner mastery or nonmastery of a set of attributes or skills. These assessment models are used to diagnose cognition, particular skill competency, or subcompetency of a defined outcome. Diagnostic models are particularly important in adaptive systems because they are used to align teaching, learning, and assessment-and to give timely diagnostic feedback by knowing students' weaknesses and strengths to guide teaching and learning in the adaptive processes.

Zone of Proximal Development: By definition, zone of proximal development (ZPD) is the difference between what learners can do without help and what they can do with help. ZPD is used to serve content within learners' "zones" to establish an appropriate adaptive baseline. ZPD is particularly useful to measure what students are able to master within a social setting. Using ZPD methods enables the sequential movement of skill mastery on an individual student-to-student basis. It also allows adaptive systems to measure and provide next-step guidance and feedback. ZPD is an important element in content scaffolding, where a learning aid can be applied when necessary within a personalized learning experience.

Self-Assessment:In any adaptive system, it is important to measure learners' interpretation of what they know versus what they actually know as measured by the adaptivity. Self-assessment capability provides adaptive systems with benchmarks on how students monitor and evaluate the quality of their learning behavior when learning and identifying strategies that improve their understanding and skills in automated ways. It compares students' judgment about their own work versus what the adaptive systems know about the sequential work that has been completed at any given learning interval.

Competency Frameworks

Skill Standards Libraries: Competencies are defined units of knowledge, skills, and abilities used to evaluate skill mastery and the demonstrated application of those skills. Core competencies serve as standards of practice for program mastery in K-16 education, as well as for workforce competency definitions. Modular adaptive content and assessment need to be built on the foundation of these broad skill standards libraries and act as a correlative "reference point" or outcome based on a framework across a number of subject and skill area domains. Each competency and skill definition focuses on the performance of the end product or goal state of instruction and reflects mastery expectations external to the instructional program. These skill standards libraries judge competence through the use of a standard that is not dependent on the performance of other learners, and standards libraries inform learners, as well as other stakeholders, about what is expected of them. Competency and skill definitions must be comprehensive in ways that confirm mastery in a learner's ability to source, process, manage, communicate, and apply knowledge across diverse contexts seen as critical for successful K-16 programs and workplace success.

Parent/Child Relationships and Competencies/Sub-Competencies: In any given competency framework, identified skills and competencies are further defined by a subset of competencies and skills, or "sub-competencies." They are specific facets of an individual, aggregate competency. Sub-competencies are a broad array of constituent "nested" elements that make up a complete competency structure linked together into parent-child relationships. Learners progress through mastery of a series of sub-competencies accretive to an overall competency that is a key indicator of future success.

Prerequisite Knowledge and Prior Knowledge Qualifiers: Assessment of prior learning capabilities sets a baseline of established student knowledge, skills, and competences against a predetermined standard. Prior learning assessments are learner-centered; they validate learning and give credit for what learners already know, placing learners at a starting point for the next viable competency to be learned in order to move forward and build on existing knowledge. Assessing broad ranges of prior knowledge before entering an adaptive course has a direct impact on student engagement as it shortcuts the course-pacing process. Adaptive systems are engineered to account for new knowledge becoming prior knowledge to be used as a building block for further knowledge and competency attainment.

Ultimately, adaptive learning systems will need to develop and embrace some degree of standards, taxonomy, and minimally viable product definitions in order to create order out of the current chaos. Not creating and adopting a more rigid framework will leave the market open to continued ambiguity. Figure 1 shows a conceptual model of minimal viable product (MVP) of truly adaptive system design.

Figure 1. Adaptive Logic Model
Source: The Action Lab,EdPlus, Arizona State University

The Initial Gust of Dust: The Adaptive Learning Systems Market

The realization that K-16 education is still at formative stages in the development of fully adaptive systems, as well as the current design of even the most sophisticated systems, leaves gaping holes in the key design requirements that will enable these systems to reach market scale. If these holes are left unaddressed, early adopters who drive market decisions will be largely unsuccessful in developing personalized learning programs for the mainstream market. Failure to execute at scale and with the promised level of achievement will be particularly disruptive to the potential of personalized learning, especially in institutions seeking to transition from a static, one-size-fits-all LMS "transmission model" of education. Gaps we see include, but are not limited to, the following:

One-off applications: There are currently few adaptive systems with even rudimentary content-authoring tools. Most of these systems are one-off build platforms controlled by the vendor, who provides authoring and adaptive course development services. This removes faculty from the teaching and learning process, relegating them to a sideline approval role.

Complex design: Adaptive systems are not designed to adapt to the skill level of the teachers and faculty.

Current behavioral measurement: Adaptive systems lack the measurement and discourse analysis of synchronous communities of online learners engaged in group activity and assignments.

Integration: Current systems lack integration to disparate applications that can act as valuable inputs to adaptive measurement.

Voids in instructional design: Current systems lack the flexibility to achieve multiple methods of instructional design to reach a desired result in digital learning.

Learner profiling: Most adaptive systems do not have robust learner profiling capabilities, especially for a broad base of learner profiles and demographics.

Synchronous learning: No adaptive systems currently in the market have peer-to-peer learning tools. This misses a valuable and critical knowledge and engagement measurement that factors into the matrix of adaptation. Current systems consider only static content modules and not peer involvement, group knowledge, and/or any other web source of information.

Content management: Content modules and associated metadata are valuable assets for reuse and reconstitution of adaptive course variations. Most adaptive systems don't have capabilities designed for reusable content in adaptive environments and are not well designed for adding, modifying, and re-creating content.

Algorithm design: Current adaptive systems are designed for and work best in finite subject areas, such as mathematics. Algorithms and cognitive models should be broad enough to teach the sciences, language arts, humanities, and language acquisition.

Opaque cognitive modeling: These environments utilize cognitive modeling processes to provide feedback to the student while assessing the student's abilities and adapting the curriculum based on the student's past performance. These processes are unclear, largely unproven, and not research-based. Vendors are often not forthcoming about the science they use to develop and prove cognitive models.

Ten Questions to Ask an Adaptive Learning Systems Vendor

Various companies have taken a first creative shot at developing and designing adaptive systems for what they believe the market requires-not necessarily what the market may need. As a result, it's worth asking any adaptive systems vendor the following ten questions.
Does your adaptive learning system have ...

1. The ability to adapt and conform to a nontraditional learner lifestyle, where the pace is variable and the learner has on-demand requirements?

2. Statistically accurate cognitive models representing authenticity of skill and competency mastery? If so, how does the system achieve that adaptive state?

3. Accurate content path placement that includes an assessment of learned knowledge that can be applied?

4. The ability to correctly perform adaptive sequencing to precisely and continuously collect real-time data on a student's performance and use it to automatically change a student's learning experience?

5. The ability to accurately determine remediation and corrective action using adaptive assessments in both normed- and criterion-referenced assessment design where applicable?

6. The correct algorithm design for weighing different multiple-measurement factors? Can you describe how the adaptive logic works in your system?

7. Adaptive assessments in the design? Or are assessments derived from traditional formative assessment item banks?

8. Synchronous capabilities as a critical measurement of student engagement behavior, as well as content knowledge mastery?

9. The necessary provisioning architecture to scale thousands of concurrent users when considering the complex calculations needed to accommodate adaptive learning?

10. The ability to develop comprehensive competency frameworks that index learning standards and outcomes?

Surviving the Storm: Barriers to Adoption

It's one challenge to develop adaptive technology capabilities, but it's another challenge to use and actually apply data from adaptive learning systems to teaching. Key stakeholders need to develop a thorough understanding of the intersections among next-generation digital platforms, adaptive pedagogy, and rapid advances in the learning sciences. The most consequential barrier to the adoption of adaptive learning systems is that scientific, multidimensional data methods to effectively facilitate individualized learning and true personalization have only recently begun to emerge. The learning science is not well understood, leaving academic outcome benefits up for conjecture.

Research is expensive, and in the development of private-sector companies, early-stage funding is used to develop market prototypes and version 1.0 commercial-release software, not research. The successful design of these complex adaptive learning systems requires years of research and development and market testing. For K-16 institutions, there is a high cost involved in adopting these systems due to the arduous process of converting existing course content into the adaptive, modular designs required to match the needs and interests of individual learners.

Another challenge is teacher/faculty proficiency in successful adoption and use of adaptive systems implementation. Individualized student learning paths, at variable pace, are advantageous because they allow teaching and remediation to apply to those students with the most need. However, faculty are accustomed to all students in the same online course progressing at the same pace. Altering an instructional strategy to these new capabilities requires significant periods of trial and error.

Next-generation adaptive systems are data-intensive. They need rich amounts of data-about user actions, behavior information, and content interaction-to perform an adequate job of mapping an optimal learning path for students. For personalized learning to realize its full potential in using comprehensive, multidimensional data, systems require access to a wide variety of additional applications in the institutional enterprise data repositories, as well as in third-party web services applications. Most adaptive systems focus solely on data derived from functionality inside the proprietary vendor software application.

"Smart" content repositories require a sufficient amount of metadata generated by some combination of both computers and humans. Metadata, however, also presents challenges and questions: the amount required; the source of the metadata; and the person/department responsible for creating and maintaining rich metadata libraries. If metadata tagging is crowdsourced and open to any participant, it introduces a significant margin for error. In addition, tags need to be maintained and tested for accuracy, corrected, and updated. If the metadata foundation is weak, the entire intent of personalization is compromised.

Lastly, the financial resources required for the initial design or implementation of adaptive learning models and the integration into existing online programs may create a significant barrier to adoption. Since adaptive learning systems are an emerging next-generation technology, few institutions have the financial resources required for investing in the technology infrastructure, software, and licensing fees. Institutions will also need to provide faculty training to effectively design and implement a personalized approach to online instruction.

After the Dust Settles

Many education leaders believe that personalized learning has unprecedented potential to reshape the landscape of how we teach and learn online. Yet higher education faces a shortage of agreed-upon scalable and repeatable methods against which to measure learning and assign value to the efficacy of these next-generation technologies. Adaptive learning systems promise to advance these efforts, but they will have little impact without a basis to measure and assess student learning. Together, we need to define a coherent set of expectations, competencies, standards, and frameworks.

Institutions have been slow to adopt personalized learning mainly due to the lack of awareness of what it is, why it is important, and how to successfully incorporate it into course and program design. With an unprecedented opportunity to redefine and transform digital education, what will it take to move this promising innovation forward? In an environment where there is high potential for the private sector to dictate the rules of engagement, those of us in higher education should consider a number of immediate actions that may impact the successful transition of our digital teaching and learning.

Perhaps the most effective way to create public- and private-sector unity would be to form a higher education, next-generation innovation council supported by institutional, federal, and/or private-sector funding. Similar to the University Innovation Alliance, the council could be designed to pool research in learning science in order to devise best practices or a common framework for the underlying data science necessary to establish accurate states of personalized and adaptive precision. A major goal for an innovation council would be to develop clear criteria for an "approved model" designation, which would be essential to ensure designations of quality and usability. Specific criteria would designate a vendor's commitment to certain performance measures, which the innovation council would monitor and report. Through a mix of private- and public-sector funding, institutions could engage in research and development activities whereby evaluations and experiences could be broadly disseminated. The resulting work might be used as an "ISO 9000" best practices approach for minimally viable requirements. The innovation council would rigorously review adaptive learning systems vendors for specific quality and performance benchmarks and would encourage private-sector providers to invest in developing new models and rigorously evaluating their results. The innovation council could also develop and conduct national workshops for administrative and academic IT preparedness of adaptive technology products and services.

In time the innovation council might incubate and eventually evolve into a central nonprofit organization similar to Internet2 or the IMS Global Learning Consortium, in order to take on a more central leadership role on certain functions supporting innovation. This type of independent organization could also leverage public funding by applying for, receiving, and distributing philanthropic funds for public-private partnerships to promote innovation and personalized learning. Independent nonprofits can also serve as advocates for innovation with state policymakers and K-16 institutions by working to spur demand for new models. To address funding shortfalls for the successful adoption of adaptive technology innovation, the innovation council could also work with federal and state funding opportunities for seed adoption grants.

Scaling access and the effective implementation and use of these groundbreaking, next-generation adaptive learning technologies will require broad strategies to address both demand and supply market dynamics. Higher education has a poor historical track record of seeking out, learning, and applying models outside the education sector. We must observe, analyze, evaluate, and replicate applicable models for personalized learning from the private sector, especially since the areas of business intelligence, financial services, healthcare, and government agencies had a head start of more than a decade. Doing so will give us the metaphorical dust mask needed not only to survive but to thrive in the face of the next big storm.

Lou Pugliese is Senior Innovation Fellow and Managing Director at the Arizona State University (ASU) Action Lab.

© 2016 Lou Pugliese

EDUCAUSE Review, October 17, 2016

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adaptive learning systems: surviving the storm

# data[].attributes.attachment_link.title
Adaptive Learning Systems: Surviving the Storm

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The effective implementation and use of adaptive learning systems requires a broad, more inclusive conversation among institutions, vendors, and other stakeholders to realize the benefits of next-generation personalized learning.

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Adapting the content according to the users' needs.

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karma eğitim olarak da bilinir. okullarda yapılan normal eğitimi online eğitim materyalleriyle zenginleşmesi olarak kullanılır. örn: çocuklar online ortamda bilgiyi kavrar ve okula gelince bilgi üzerine sentez ve analiz yaparak, öğrendikleri bilgiyi kullanıp kendi öğrenilmiş bilgilerini bir üst seviyeye cıkarırlar.

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distance learning is a way of learning remotely without being in regular face to face contact with a teacher in classroom. Distance learning gives students most opportunities of studying for a degree from an international university whilst staying at home.

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Karma eğitim olarak da   nitelendirilen öğretimin üç temel boyutu vardır. bunlardan ilki yüz yüze ve online eğitimin bilrşetirilmesidir(akıllı tahtalar veya moodle sistemi). ikinci olarak teknolojilerin bilrşetirilmesidir(internet telefonu veya e-mail). son olarak metodların birleştirilmesidir(constructivist theory).Bu üç yaklaşımın temelinde de eğitim teknolojileri vardır

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Karma eğitim olarak da   nitelendirilen öğretimin üç temel boyutu vardır. bunlardan ilki yüz yüze ve online eğitimin bilrşetirilmesidir(akıllı tahtalar veya moodle sistemi). ikinci olarak teknolojilerin bilrşetirilmesidir(internet telefonu veya e-mail). son olarak metodların birleştirilmesidir(constructivist theory).Bu üç yaklaşımın temelinde de eğitim teknolojileri vardır

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Background

'Blended learning' (BL) is a 'buzz' word in language teaching. However, it has been in use for almost 20 years and its meaning 'has been constantly changing during this period' (Sharpe, Benfield, Roberts, and Francis 2006: 18).

It was first used in the corporate world to refer to a course designed to allow workers to both continue in the workplace and study. Rather than taking time out on a residential seminar, training was delivered via (for example) self-study manuals, videos, and the web. BL was in part adopted as a cost-saving measure (see Driscoll 2002).

Definitions

In the world of education, three definitions of BL are especially relevant.

Definition 1: a combination of face-to-face and online teaching

'The integrated combination of traditional learning with web based on-line approaches' (Oliver and Trigwell 2005: 17). This is, arguably, the classic definition of the term. 'Traditional learning' here is classroom teaching or 'face-to-face' language lessons. The delivery of the online part of the course is usually through learning technologies, typically involving a Virtual Learning Environment (VLE) such as 'Blackboard' or 'Moodle' and comprising the use of synchronous and asynchronous electronic tools, such as, respectively, 'chat' and 'bulletin boards'.

Definition 2: a combination of technologies

'The combination of media and tools employed in an e-learning environment' (ibid.: 17). This definition could describe a purely distance learning course, where no face-to-face lessons occur. Communication between the learner and e-tutor may take place through any number of technologies, such as email and internet telephone.

Definition 3: a combination of methodologies

'The combination of a number of pedagogic approaches, irrespective of the learning technology used' (ibid.: 17). A course that combines 'transmission' and 'constructivist' approaches would fit into this category, such as one involving elements of a present-practice-produce methodology as well as task-based learning.

The term continues to develop. A further possible conceptualization of BL is as 'a combination of real world plus in-world', where a teacher delivers a face-to-face lesson and then arranges to meet his or her student for a follow-up class in a virtual world such as 'Second Life' (Claypole 2010: 36).

Furthermore, computer-assisted language learning in general has been described as 'context specific' (Levy and Stockwell 2006: 234), and a number of local uses of the term 'BL' also exist within various educational settings, both national and institutional. For example, as some governments switch from coursebooks to CD Rom or web-based material, the term has been applied to the blending (or combination) of print and digital materials.

There are also a number of 'dimensions' associated with definitions of BL, such as 'breadth' and 'connotation'.

Breadth

Claypole (2003: 169) has argued that BL is nothing new and is indeed the logical development of prior tendencies involving the mixing of methods of teaching. A similar 'broad' interpretation of the term states that BL is a combination of the face-to-face part of a course and an ' appropriate use of technology' ( Sharma and Barrett 2007: 7, my italics). On the other hand, a 'narrow' definition-for example 'blending face-to-face plus web-based learning'-excludes the use of CD Rom and other technologies and therefore could be seen as excessively restrictive.

Connotation

The term BL is used both positively and disparagingly. Thus, a '1 + 1 is more than 2' argument assumes a positive connotation, i.e. combining the best of the teacher with the best of the technology will deliver improved learning outcomes. On the other hand, a negative connotation can be assumed where there may be no thought-through pedagogical relation between parts of the blend, so that the course may appear to lack coherence.

The practice of BL

One of the main factors involved in running a BL course is a consideration of the 'appropriateness' of each medium of course delivery and the related matching of the delivery type to the learning activity. Thus, the face-to-face part of the course might develop student fluency through in-class discussion, while the electronic bulletin board component might develop learners' critical thinking skills. However, in practice, of course, students may favour one of the delivery modes (face-to-face or online) to the detriment of the other.

Putting BL into practice can involve a variety of approaches, such as the following:

▪ Teachers can issue learners with a password to their class VLE or 'wiki' (an editable website that can be used for collaborative activities), allowing 24/7 access to digital materials that support classroom work: a 'dual track' approach.

▪ A teacher could set homework assignments based on a CD Rom that accompanies the coursebook. Students use technology to do specific tasks between the face-to-face classes, to prepare or consolidate: an 'integrated' approach.

How the use of the term BL might develop in the future is not clear. Westbrook (2008: 13) has argued that it may be becoming diluted because the large number of definitions it has attracted means that almost any approach can be defined as BL. The term may therefore become redundant. However, BL is likely to remain an important concept in language teaching since its overall focus is concerned with the search for 'best practice', i.e. the attempt to identify the optimum mix of course delivery in order to provide the most effective language learning experience.

References

'Blended learning: new resources for teaching business English'

,

IATEFL Brighton Conference Selections

,

,

Controversies in ELT. What You Always Wanted to Know about Teaching English but Were Afraid to Ask

,

,

Blended Learning: Let's Get Beyond the Hype

,

,

CALL Dimensions: Options and Issues in Computer-Assisted Language Learning

,

'Can "Blended Learning" be redeemed?'

,

,

, vol.

(pg.

-

)

,

Blended Learning: Using Technology in and Beyond the Language Classroom

,

,

The Undergraduate Experience of Blended E-learning: A Review of UK Literature and Practice

,

'The beginning of the end for blended learning?'

,

,

, vol.

(pg.

-

)

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Blended learning | ELT Journal | Oxford Academic

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© The Author 2010. Published by Oxford University Press; all rights reserved.'Blended learning' (BL) is a 'buzz' word in language teaching. However, it has been in use for almost 20 years and its meaning 'has been constantly changing during this period' (Sharpe, Benfield, Roberts, and Francis 2006: 18)....It was first used in the corporate world to refer to a course designed to allow workers to both continue in the workplace and study.

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Computer-based or online learning that adapts the content according to the performance of the students

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It refers to massive open online courses. Learners can get information in classroom-like settings regardless of their age, education level and location. Ex. Coursera is a good example of these type of courses.

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Coursera | Online Courses From Top Universities. Join for Free

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2000+ courses from schools like Stanford and Yale - no application required. Build career skills in data science, computer science, business, and more.

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In flipped Classroom, students take their instruction at their home from an online souce and do their practices and discussions in a classroom setting. Thus, both the teacher and the students do not waste their time for something they can do without a teacher's guidance and they use their time for education more effectively.

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A policy that lets individuals bring their own personalized devices to the workplace or classroom for educational purposes.

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it refers to the use of video games to support teaching and learning.  It is about using ‘elements’ derived from video-game design, which are then deployed in a variety of contexts, rather than about using individual video games.

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Blended learning is the integration of computer-based teaching methods into teacher-based teaching methods.

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Techniques and tools that help learners and educators assess learning

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Hepsi gibi klasik tanımlardan öte kendi bakış açımdan tanımlamak istiyorum. Benim için virtual reality; "case analiz" dediğimiz kavramın sanallaştırılmış halidir. 3 boyutlu oluşu ve bunu online communicationa entegre edebiliyor olmamız en büyük avantajlarından olsa da, beklenilen kadar yaygınlaşması durumunda çokça zararı olacağını düşündüğüm teknolojidir. Bilgisayar üzerinden savaş oyunu oynayan bir çocuğun dahi psikolojisi bu denli etkilenirken, gerçek anlamda birini boğazlama hissiyle oynayacak o çocukların nasıl gelişeceğini/etkileneceğini düşünmüyor muyuz? Düşünmek mi istemiyoruz?

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As opposed to virtual reality in which there is a computer-generated artificial environment, augmented reality integrates digital data with the user's surroundings. This activity takes place in real time.
Augmented reality is being used in applications and on mobile devices, tables, etc. to blend digital components into the real world.
Augmented reality has more to do with the real events such as holograms, 3D emails, etc.

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Augmented Reality (AR) is a growing field of technology where real life is modified and enhanced by computer-generated sights and sounds.

The most common use of AR can be seen through mobile apps. Point your device's camera at something that the app recognizes, and it will generate a 3D animation or video superimposed over whatever is on your camera's screen. The effect makes the computer-generated item appear like it's really there.

App developers are building AR apps for fun, educational, and commercial uses.

20 Of The Best Augmented Reality Apps For The Classroom

Description: JigSpace allows you to learn about all sorts of things, from car engines to the Roman wars. Jigspace features up-close 3D diagrams, disassembling and reassembling 3D objects, and 3D models of historical battles, along with other quality features.

Price: Free - $49.99 subscription for added benefits.

2. Complete Anatomy 2021

Description: Complete Anatomy 2021 is a modern way to learn about the human body. With this app, you can take up-close 3D looks anywhere in the body: the heart, nerves, muscles, etc.

Price: $34.99 - $99.99 annual subscription.

Developer Description: Adobe Aero is an immersive AR app that students can use for learning or play. This app allows students to 'spawn' anything right in front of them and get a live look at the object before them.

Price: Free

Description: Animal Safari 3D is a way for students to learn about animals visually. Animal Safari features the ability to see 1:1 scale animals in 3D, hear live animal noises, move and rotate any animal you want, feed, and watch your animal eat. Students can also pull up any animal's information card and learn about its habitat, diet, etc.

Price: Free

Description: solAR is an AR solar system app that allows students to have fun learning about astronomy. Some of the features included are; a 3D visual representation of all of the planets, full-scale planets placed right before you giving you the ability to grasp the proper sizes of the planets, and informational cards to allow students to learn more about individual planets.

Price: Free

Description: AR Pianist is an interactive, modern way for students to learn how to play the piano, with features in the app; such as providing them with a virtual piano if they don't have one, famous virtual pianists that can play songs right in front of them, the ability to slow down songs, learn songs with interactive sheet music, live feedback, and more.

Price: Free

Description: ARvid is an interactive AR app that allows students to interact with animals, buildings and place full-scale objects right before them to create their very own artistic scenes, all while in 4K. ARvid also has all kinds of features, including 4K textures, vehicles, sci-fi, and more.

Price: Free

8. 3D Brush

Description: With 3D Brush students can add live effects to scenes right in front of them. Students can also use your finger to draw and write photorealistic stuff in the scene in front of you. Students can also take pictures of their creation to keep.

Price: Free

Description: Sky Guide makes stargazing for students simple. Just hold your device overhead, and the app will automatically find stars, constellations, planets, satellites, and more for you with ease. Some additional features included with the app allow you to get notifications from the International Space Station and see stars invisible to the human eye with an X-ray filter.

Price: $2.99

Description: Math Ninja AR makes learning math fun for younger children by incorporating physical movements such as; stretching, bending, squatting, crawling, reaching, and peering to find the answer through the many levels inside the app to help stimulate a child's memory and help them retain information.

Price: Free

11. Particular Augmented Reality

Description: This app allows students to create virtual, 3D art; it has all kinds of face filters, virtual UFOs, and more. It's a easy way to help younger students learn about art in a fun-related environment. It's also an easy-to-use app for aspiring graphic, comic, and 3D artists to learn about and create art.

Price: Free

Description: With Big Bang AR, you can watch the universe form right in front of you. This app is a fun way for students to learn about the history of the universe. Another feature allows you to create a supernova, explore the nebula, and other fun stuff.

Price: Free

Description: With Assemblr, students learn traditional academic content through virtual and augmented reality. Teachers can also create multiple online classrooms and record and present virtual 3D presentations.

Price: Free - $50 monthly subscription

Description: CamToPlan can be helpful to engineering or architectural students since it can virtually measure anything in front of them. This app can be beneficial for interior design, landscaping, and more.

Price: Free - $17.99

Description: Hyperspace AR allows students to scan their space and then create the art sculpture or decoration of their choice. Though the art within the app may be limited, it can help students learn to be more creative.

Price: Free

Description: Wonderscope is an interactive way to help younger students find enjoyment in reading. Wonderscope is constantly adding new books for your student or kid to discover and enjoy.

Price: Free

Description: Tracing Projector helps students learn to draw through a virtual tracing mechanism. It has all kinds of photos for students to trace, from the Mona Lisa to their own faces-you can even edit your photos once you're done drawing them.

Price: Free

Description: Halo AR allows you to take a picture of any flat object and overlay any photo, video, or 3D model on top of it. Halo AR also makes it easy to share the halos you create with students.

Price: Free

19. For All Mankind: Time Capsule

Description: This app allows students to use augmented reality to explore digitally artifacts from the early 80s.In that way, it is a mix of science and history-this could also be useful for homeschooled students.

Price: Free

Description: Google Lens is a useful way to help identify all sorts of plant life, learn about landmarks, translate words, or identify animals. All you have to do is hold your device up to what you want to learn about, and it'll tell you.

Price: Free

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12 Augmented Reality Apps For The Classroom

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Augmented Reality (AR) is a growing field of technology where real life is modified and enhanced by computer-generated sights and sounds. The most common use of AR can be seen through mobile apps. Point your device's camera at something that the app recognizes, and it will generate a 3D animation or video superimposed over whatever is on your camera's screen.

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Özellikle fizik derslerinde etkili olan bir uygulama, soyut bilgileri öğrenmeyi kolaylaştırır.

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A reverse way of traditional teaching in which the students are given the intruction online, outside the classroom and in class they carry out activities, engage in concepts, etc.

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simulation is the imitation of the operation of a real-world process or system over time.

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(Massive Open Online Courses)

Online courses that are aimed to reach up to masses for educational purposes

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Telling a story by using digital tools and multimedia devices.

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English podcasts for listening exercises

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English podcasts for listening exercises

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Podcast:BBC Podcsts is a rich source for both English teachers and learners of English for listening practices. http://www.bbc.co.uk/podcasts

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It is digital imagery and data that is gained from the real world and gives people more information about the environment. It is beyond virtual reality which is like simulate reality.Smartphones and tablets, with their screens are examples of augmented reality.

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Using game elements in non-game contexts. It can be online games or real life games. Considering that digital native students are quite familiar with online games and we can use elements from these games while teaching. Ex. Ranking top five students, giving stars, giving a limited time for a task etc.

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A period of intensive tuition given by a tutor to an individual student or to a small group of students. The goal is to make knowledge more permanent.

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online learning is a way of studying for an internationally recognized qualification without needing to attend classes on campus.

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A realistic and immersive simulation of a three-dimensional environment, created using interactive software and hardware, and experienced or controlled by movement of the body.There are a wide variety of applications for virtual reality which include:Sport,Medicine,The Arts,Entertainment.

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Students receive the instructional content online. They make practices and projects in the classroom. Classroom activities are the crucial point.

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A tutorial is a more specific and interactive method of transferring knowledge used in the learning process. Tutorials can be in private, at schools or at conferences. Besides learners can access to tutorials on the internet via e-learning platforms.

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Traditional/Direct instruction replaced by  web-based learning. It is also known hybrid or mixed mode courses.Instruction is delivered online way. For example, ın a blended-learning course, students might attend a class taught by a teacher in a traditional classroom setting, while also independently completing online components of the course outside of the classroom as online.

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Drill and practice is an educational method which focuses on repetitive tasks. For instance; the memorization or the repetition of words. The aim is learning without error.

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It is a kind of learning in which there is no "face-to- face" communication between teacher and student. Distance learning is especially useful in less-developed countries where building a school cost a lot. Thanks to distance learning, a teacher can reach even the most isolated areas via media tools such as radio, television, telephone etc. and education becomes accessible to more students.

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Computer based insruction vs Computer supported instruction

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Computer based insruction vs Computer supported instruction

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Distance learning / Virtual Classroom/ Online learning /

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Distance learning / Virtual Classroom/ Online learning /

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1:1 (one-to-one) laptop/tablet ... ,  BYOD

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1:1 (one-to-one) laptop/tablet ... ,  BYOD

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The spin-off podcast from the incredibly successful 'How Stuff Works' website, this brilliant series entertains and instructs students with straightforward explanations of concepts. From philanthropy to history to why the leaves change colour, students can explore any question they wish with these brilliant podcasts.

Ideally suited to older students, these fantastic podcasts compare and contrast the academic theories of educational thinkers from Harvard law professors to comedians, debating topical subjects across a wide and diverse range of different themes.

This brilliant series of educational podcasts takes 100 objects from the British museum and shapes a single episode around each one, using it as a historical landmark from which to relate the history of the world. From economic to financial development to the growth and fall of civilizations, these fascinating insights into the artifacts of the famous British museum are a fabulous resource for history students or any young people interested in learning more about the world and its development.

Bringing you short stories from the world of science, Nova explores fascinating topics, from discovering different types of packaging to the secrets of Stonehenge. Conveniently sorted into subject categories including 'body and brain', 'evolution', 'nature' and 'planet earth', teachers of all subjects are sure to find useful resources here to engage and entertain their students.

Perfect for students of English and English literature, these wonderful podcasts introduce students to a growing catalogue of some of the greatest works of English poetry.

Ever wondered how easy it could be to learn English using the Internet? This brilliant series of podcasts is the answer. Each taking on a different topic, they provide the online equivalent of conversation classes, with specific ideas and issues explored in each new episode and the option for students to listen as many times as they need to in order to understand and note down the relevant new vocabulary.

This great podcast series containing well over 200 episodes traces the heavens for students to explore and learn. With separate episodes on scientists, elements and space phenomena, this is a fantastic resource for scientists and budding astronomers alike to widen their horizons.

This fantastic podcast brings science students the past, present and future of chemistry! With a new episode fortnightly, the series gives students the opportunity to gain historical perspective on current scientific issues.

Another one for older students, this fascinating podcast explores areas of math that might not be included on the curriculum, exploring the strange, fun or otherwise unexplored corners of mathematics!

This fantastic series of podcasts engages students by demonstrating the relevance of science in real-life situations. From exploring the chemistry behind the Thanksgiving turkey to investigating the science of photography, this is science in everyday life brought alive for students of all ages.

Image courtesy of Flickr, ralphbijker

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The spin-off podcast from the incredibly successful 'How Stuff Works' website, this brilliant series entertains and instructs students with straightforward explanations of concepts. From philanthropy to history to why the leaves change colour, students can explore any question they wish with these brilliant podcasts.

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Asynchronous E-Learning Vs. Synchronous E-Learning | LMS by Mindflash

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On the other hand, Asynchronous learning can be carried out even while the student is offline. Asynchronous e-learning involves coursework delivered via web, email and message boards that are then posted on online forums. In such cases, students ideally complete the course at their own pace, by using the internet merely as a support tool rather than volunteering exclusively for an e-learning software or online interactive classes.

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