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it creates interactive learning environment.
help chemistry teachers to make observable some non observable reactions and process
enable students to manipulate the concepts of chemistry

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Bu öğrenme şekli farklı farzda öğrenme metodları olan öğrenciler için oldukça faydalı bir öğrenme tarzını oluşturmaktadır.Görsel, işitsel, yazarak öğrenen öğrencilerin tümüne hitap eden bir yoldur.

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The acquisition of knowledge or skill through repetitive practice can define as drill and practice.  Drill-and-practice, like memorization, involves repetition of specific skills, such as addition and subtraction, or spelling.  They are based on question and answer interactions and should give the student appropriate feedback.

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blended learning is the way of combination of traditional classroom methods and independent study methods.

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this learning way is much more about using different type of learning methods together. when one method is needed , this method is used in that times. other times different methods will be used to facilitate to the students learning processing

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Drill and practice have different meanings. Drill means doing repetitive example and it forces to students to memorize the process. Yet, the practicing emphasises on review of the things which have been learnt. When we give homework to the students we need to consider this

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method of giving information specifically rather than general.

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How can we understand mathematics such as complex numbers, real and complex infinite series and vectors? At this moment, simulation can help us to teach them easily. Mathematical simulations are based on Easy Java Simulation (EJS) programming tools or Geogebra, Cabri 3D v2.

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They are like memorization, and involve repetition of basic knowledge and fundamental skills such as multiplication

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live view of a physical world by computer-generated perceptual information. Can be use for group counseling sessions to gamify the exercises.

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the intersection of online and classroom learning

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Blended learningis a term increasingly used to describe the wayis being combined with traditional classroom methods and independent study to create a new, hybrid teachingmethodology. It represents a much greater change in basic technique than simply adding computers to classrooms; it represents, in many cases, a fundamental change in the way teachers and students approach the learning experience. It has already produced an offshoot - the flipped classroom - that has quickly become a distinct approach of its own.

No single, reliable definition of blended learning exists, or even a universal agreement on the term itself. Many use terms like hybrid, mixed, or integrativeto describe the same trend. But the trend is significant. In 2000 an estimated 45,000 K-12 students took an online course, but almost a decade later more than 3 million took courses that way, many of them using computers in the schools themselves.

A learning model in three parts

There is a general consensus among education innovators that blended learning has three primary components:

In-person classroom activities facilitated by a trained educator.

Online learning materials, often including pre-recorded lectures given by that same instructor.

Structured independent study time guided by the material in the lectures and skills developed during the classroom experience.

A course created in a blended learning model uses the classroom time for activities that benefit the most from direct interaction. Traditional education (especially at the college level) tends to place an emphasis on delivering material by way of a lecture, while in a blended learning model lectures can be videotaped ahead of time so the student can watch on their own time. The classroom time is more likely to be for structured exercises that emphasize the application of the curriculum to solve problems or work through tasks.

An individual semester of blended learning may emphasize classroom time at the beginning, then gradually increase the amount of work that students do online or during independent study. Many argue that class discussion boards, for example, are far more useful if the participants have met face-to-face first.

The "flipped" classroom, a more recent coinage, refers to classes that are structured almost exclusively around a reversal of expectations for lectures and homework. Students are expected to watch lectures online at home, and do homework while they are in class.

Blended learning redefining teaching roles

In some situations, the move to blended learning has inspired educators to redefine traditional roles. The word " facilitator " has emerged as an alternative to "teacher," bringing with it a slightly different focus. The facilitator places an emphasis on empowering students with the skills and knowledge required to make the most of the online material and independent study time, guiding students toward the most meaningful experience possible. Facilitators focus on four key areas:

Development of online and offline course content.

Facilitation of communication with and among students, including the pedagogy of communicating content online without the contextual clues students would get in person.

Guiding the learning experience of individual students, and customizing material wherever possible to strengthen the learning experience.

Assessment and grading, not unlike the expectations for teachers within the traditional framework.

By putting an emphasis on learning through supervised activities, blended learning has proven to be very adaptable to what some corporations are calling blended training. Trainers can shift their focus from the delivery of knowledge to its application, and companies spend less flying trainers around to oversee all instruction in person.
Image used under Creative Commons by Flickr userSarah M Stewart.

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What Is Blended Learning? | LMS by Mindflash

# data[].attributes.attachment_link.description
Blended learningis a term increasingly used to describe the wayis being combined with traditional classroom methods and independent study to create a new, hybrid teachingmethodology. It represents a much greater change in basic technique than simply adding computers to classrooms; it represents, in many cases, a fundamental change in the way teachers and students approach the learning experience.

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It helps students to be more creative and productive

# data[].attributes.attachment_link.readable_text
Blended learningis a term increasingly used to describe the wayis being combined with traditional classroom methods and independent study to create a new, hybrid teachingmethodology. It represents a much greater change in basic technique than simply adding computers to classrooms; it represents, in many cases, a fundamental change in the way teachers and students approach the learning experience. It has already produced an offshoot - the flipped classroom - that has quickly become a distinct approach of its own.

No single, reliable definition of blended learning exists, or even a universal agreement on the term itself. Many use terms like hybrid, mixed, or integrativeto describe the same trend. But the trend is significant. In 2000 an estimated 45,000 K-12 students took an online course, but almost a decade later more than 3 million took courses that way, many of them using computers in the schools themselves.

A learning model in three parts

There is a general consensus among education innovators that blended learning has three primary components:

In-person classroom activities facilitated by a trained educator.

Online learning materials, often including pre-recorded lectures given by that same instructor.

Structured independent study time guided by the material in the lectures and skills developed during the classroom experience.

A course created in a blended learning model uses the classroom time for activities that benefit the most from direct interaction. Traditional education (especially at the college level) tends to place an emphasis on delivering material by way of a lecture, while in a blended learning model lectures can be videotaped ahead of time so the student can watch on their own time. The classroom time is more likely to be for structured exercises that emphasize the application of the curriculum to solve problems or work through tasks.

An individual semester of blended learning may emphasize classroom time at the beginning, then gradually increase the amount of work that students do online or during independent study. Many argue that class discussion boards, for example, are far more useful if the participants have met face-to-face first.

The "flipped" classroom, a more recent coinage, refers to classes that are structured almost exclusively around a reversal of expectations for lectures and homework. Students are expected to watch lectures online at home, and do homework while they are in class.

Blended learning redefining teaching roles

In some situations, the move to blended learning has inspired educators to redefine traditional roles. The word " facilitator " has emerged as an alternative to "teacher," bringing with it a slightly different focus. The facilitator places an emphasis on empowering students with the skills and knowledge required to make the most of the online material and independent study time, guiding students toward the most meaningful experience possible. Facilitators focus on four key areas:

Development of online and offline course content.

Facilitation of communication with and among students, including the pedagogy of communicating content online without the contextual clues students would get in person.

Guiding the learning experience of individual students, and customizing material wherever possible to strengthen the learning experience.

Assessment and grading, not unlike the expectations for teachers within the traditional framework.

By putting an emphasis on learning through supervised activities, blended learning has proven to be very adaptable to what some corporations are calling blended training. Trainers can shift their focus from the delivery of knowledge to its application, and companies spend less flying trainers around to oversee all instruction in person.
Image used under Creative Commons by Flickr userSarah M Stewart.

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What Is Blended Learning? | LMS by Mindflash

# data[].attributes.attachment_link.description
Blended learningis a term increasingly used to describe the wayis being combined with traditional classroom methods and independent study to create a new, hybrid teachingmethodology. It represents a much greater change in basic technique than simply adding computers to classrooms; it represents, in many cases, a fundamental change in the way teachers and students approach the learning experience.

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MOOC's List is a directory massive course  publisher which is  online, world-wide and free up to 30 days.
MOOC's List
does not produce any lectures but provides access to courses through platforms like Moodle or OpenEdX.
There are many Mooc providers in the sea you can catch one :)
We can use Moocs with students that do not have a teacher in their schools also we can use it to support that teacher's instruction.

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It is the name of a technic in which teachers  integrate  technology  into the class when traditional ways are not sufficient for effective acquisition.

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The basic definition of simulation is that it is the imitative representation of the functioning of one system or process by means of the functioning of another. In education, instructional simulation is used for making predictions about the social or economic world by students. Simulation helps students to engage 'deep learning'. You can find more information about instructional simulation from the link http://trlink.org/JVbM2 .

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virtual reality can be used in language teaching by creating scene that enables to use target language in a specific place.
"Immerseme" is a kind of project that offers places and usage of appropriate words/sentences at that situation. and it has many languages to be studied.

other examples are "Language VR" and "VR Learn English" apps

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pelin semercioğlu
it is an improved technology material that provides realistic experience.

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blending learning combines digital learning tools and media with traditional education methods.

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virtual reality in chemistry education can be used to make non observable particles like molecules, elements observable and three dimensional

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It can help students to understand terms especially in science teaching

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AR provides users with computer generated-composite images on their view of the real world. It could be used in second language teaching to consolidate the recently covered vocabulary items.

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Berin Baran                     Virtual Reality applications plays a significant role on the chemistry education.It helps to increase the understanding level of students,especially on the laborotary applications.Some schools may not have laboratory or the stuff,chemicals needed to have in chemistry labarotory.In this case,we can use free-virtual laboratory applications.

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Berin Baran                     Virtual Reality applications plays a significant role on the chemistry education.It helps to increase the understanding level of students,especially on the laborotary applications.Some schools may not have laboratory or the stuff,chemicals needed to have in chemistry labarotory.In this case,we can use free-virtual laboratory applications.

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Virtual Reality (VR) is the use of computer technology to create a simulated environment.  By simulating as many senses as possible, such as vision, hearing, touch, even smell, the computer is transformed into a gatekeeper to this artificial world.

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It is a kind of transferring knowledge. It might define as a part of learning process.
*interactive
*specific

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pelin semercioğlu
a technology method that combines the real world and the simulation

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Storytelling by using digital tools. It gives students the opportunity to share information about themselves and can express their emotions.

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: a paper, book, film, or computer program that provides practical information about a specific subject

: a class conducted by a tutor for one student or a small number of students

: of, relating to, or involving a tutor or a tutorial

Examples of tutorial in a Sentence

Noun An online The professor was offering a We offer tutorial gives basic instructions for using the software. tutorial in her office a week before the exam. Adjective tutorial instruction for students preparing for the examination.

These examples are programmatically compiled from various online sources to illustrate current usage of the word 'tutorial.' Any opinions expressed in the examples do not represent those of Merriam-Webster or its editors. Send us feedback about these examples.

Word History

Noun

1923, in the meaning defined at sense 2

Adjective

1822, in the meaning defined above

The first known use of tutorial was in 1822

Dictionary Entries Near tutorial

Cite this Entry

"Tutorial." Merriam-Webster.com Dictionary, Merriam-Webster, https://www.merriam-webster.com/dictionary/tutorial. Accessed 3 Nov. 2023.

More from Merriam-Webster on tutorial

- Updated example sentences

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Merriam-Webster unabridged

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a paper, book, film, or computer program that provides practical information about a specific subject; a class conducted by a tutor for one student or a small number of students... See the full definition

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Educational Uses of Digital Storytelling

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" One of the reasons that digital stories fit so well with today's students is that they incorporate the skills, tools, and practices that resonate with contemporary learners."

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This simply means a school district or other environment (such as a lab) that has one computer or other device for every student, thus making the device-to-person ratio 1:1. Many schools in the K-12 arena are currently attempting to better integrate technology into their curriculum by “going 1:1.”

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telling story by using media , visula image sounds . cooperating them and using them together and creating a a person, a situation or event

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Teorik ve pratik tersleri birbirinden ayırmak imkansız. Hali hazırdaki sistemde teori kısmı her ne kadar başarıyla anlatılsa da pratik kısmını yapmakta eğitim, araç gereç şartlarından dolayı başarı tam olarak elde edilemiyor. Uygulamalı öğrenme ise bu konuda oldukça verimli bir yol sunuyor. bu şekilde uygulama ile öğrenmeyi içeriyor. yaparak öğrenme kapsamına giriyor.

“Bana anlatırsan, unuturum. Nasıl yapıldığını gösterirsen, hatırlarım. Bırak kendim yapayım, işte o zaman öğrenirim.” (Konfüçyüs, Çinli Filozof)

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AR may have more advantages than VR in terms of language learning  in regard to use the real world. it may be an advantage because the learning outcomes will be related to the user's life. Aurasma is an example of it

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it is an educational method which uses computers as interactive teaching devices, and to orchestrate the allocation of human and mediated resources according to the unique needs of each learner.
- as a teacher you can know your students' understanding.

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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

# data[].attributes.attachment_link.description
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.

# data[].attributes.body
Flipped classroom is an instructional strategy and a type of blended learning that reverses the traditional learning environment by delivering instructional content, often online, outside of the classroom.

# data[].attributes.body
We can use simulations in education to develop students' abilities and understanding levels

# data[].attributes.body
A tutorial is a document, software or other media designed for purpose of the helping the students learn new skills using step by step process.

# data[].attributes.body
After new information is presented to the students, repetitive drills and practice is needed. Learning can be achieved only with sending new info to the long memory by drills and practice.

# data[].attributes.body
Blended learning is a formal education program in which students learn at online learning, with some element of student control over time, place, path, and/or pace. It involves digital learning tools, media, etc. to improve traditional learning methods.

# data[].attributes.body
VR is a computer-generated scenario that simulates a realistic experience. In second language teaching, immerseme.co is a useful VR platform that helps learners access abroad experience without leaving their office/home/lab.

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pelin semercioğlu
it's a useful method that improves the students' understanding and making sense of the context.

# data[].attributes.body
creating virtual atmosphere to teach information to students by taking advantages of technology.

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Berin BaranAugmented Reality makes the chemistry education more interactive and alive.When it comes to use AR in the classroom environment,the students need to have their technological device,too.It helps students to associate their chemistry knowledge with their mobile phones etc.The students may have fun and it makes the lesson permanent in students’ mind.

# data[].attributes.subject
Berin BaranAugmented Reality makes the chemistry education more interactive and alive.When it comes to use AR in the classroom environment,the students need to have their technological device,too.It helps students to associate their chemistry knowledge with their mobile phones etc.The students may have fun and it makes the lesson permanent in students’ mind.

# data[].attributes.headline
Berin BaranAugmented Reality makes the chemistry education more interactive and alive.When it comes to use AR in the classroom environment,the students need to have their technological device,too.It helps students to associate their chemistry knowledge with their mobile phones etc.The students may have fun and it makes the lesson permanent in students’ mind.

# data[].attributes.subject
Berin BaranAugmented Reality makes the chemistry education more interactive and alive.When it comes to use AR in the classroom environment,the students need to have their technological device,too.It helps students to associate their chemistry knowledge with their mobile phones etc.The students may have fun and it makes the lesson permanent in students’ mind.

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BYOD (bring your own device)

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BYOD (bring your own device) is where you allow your students and staff to use personal mobile devices on your school wireless network. I'm sure you've heard of it, because it's spreading through schools faster than juicy gossip in a tiny town. Now, there's a lot to consider when determining if implementing BYOD in schools is the right move. So, here's a list of 20 pros and cons to hopefully help make the decision a little easier on you.

Pros of BYOD

1.) Students are already familiar and comfortable using their own technology so they can focus on actually learning with them than learning how to use the device.

2.) Students' personal mobile devices tend to be more cutting-edge, so schools can more easily stay up-to-date with technology..

3.) Students are more likely to have remembered their beloved mobile devices than textbooks or notes.

4.) It's a cost-effective way to save schools money on technology.

5.) With BYOD students are more likely to continue learning outside of schools hours.

6.) When students use their own devices, they take care of their own 'training.'

7.) BYOD provides opportunity for teaching respectful/appropriate use, which will be important in properly preparing them for the future.

8.) Students will be more organized with all their notes and assignments all in one place.

9.) Gives the students limitless access to information and resources.

10.) BYOD allows student & teacher to swap roles.

11.) Teachers can use certain apps to be more connected with students and parents than ever before.

12.) Students love technology so BYOD engages students and creates enthusiasm and excitement about learning.
Also Read | "1:1 or BYOD? Which is Better for Your School? [Infographic]"
13.) If students bring their own devices to school, schools can concentrate funding on the students who need it, maybe providing an iPad leasing program.

14.) BYOD allows more opportunities for more personalized learning where students can excel at their own pace.

15.) Students take control of ensuring that their device is working, instilling a sense of responsibility.

16.) BYOD offers a way of delivering ebooks.

17.) There are loads of cool and exciting educational apps to get students excited about learning.

18.) The majority of students and adults already own the devices necessary for BYOD.

19.) Students can use the device they have chosen to complete their tasks so they are more likely to do them.

20.) BYOD can be used as a privilege to encourage students to stay on task.

Cons of BYOD

1.) All these mobile devices can overload your wireless network.

2.) It possibly opens up the opportunity for cheating.

3.) Technology may become a status symbol for students causing the divide between the haves and have not's to be even greater.

4.) Parents may not be happy with the idea of paying for mobile devices for use in school.

5.) Students may be more easily distracted while working on their own mobile devices.

6.) Students might forget to charge their devices.

7.) Students bringing in their own devices to school may increase the possibility of theft.

8.) Students may not be able to afford these devices.

9.) What if a student brings a virus from home onto the school wireless network?
Also Read | "How to Plan a BYOD Security Policy for Schools"
10.) It can be more challenging for an IT department to manage the bandwidth required to ensure all devices are connected and running smoothly.

11.) Students forget to bring their mobile devices.

12.) Applications may not be universal across all platforms.

13.) Your wireless network infrastructure may need to be addressed before implementation.

14.) Just like some of the parents, some of the students may not to mix in school work on their personal mobile devices.

15.) Some teacher may resist teaching with technology.

Okay, I know I said I would give you 20 but I honestly can't think of any more cons at the moment.

So we have 20 pros and 15 cons...looks like the pros for BYOD in schools are beating out the cons here. Even with all 15 of these cons, I still think a lot of the pros hold a lot more value and therefor out weight most of the cons of BYOD anyways. Plus, many of these cons can be avoided or fixed with certain policies, apps, the correct network infrastructure, and mobile device management.

If you have any questions about BYOD implementation and whether or not allowing it is the right move for your school, contact us here. We have helped schools all over the Unites States implement BYOD on their school wireless networks and are always more than happy to answer any of your questions. Good luck!

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20 Pros and Cons of implementing BYOD in schools

# data[].attributes.attachment_link.description
BYOD (bring your own device) is where you allow your students and staff to use personal mobile devices on your school wireless network. I'm sure you've heard of it, because it's spreading through schools faster than juicy gossip in a tiny town.

# data[].attributes.body
Flipped classroom is an instructional strategy and a type of blended learning that reverses the traditional learning environment by delivering instructional content, often online, outside of the classroom. It moves activities, including those that may have traditionally been considered homework, into theclassroom.
- it enables to students make more active in the classroom.

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Sometimes it can be as effective as face-to-face contact and online learning is a product of communication and internet technologies.

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A learning way without attending a physical class.

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üç boyutlu görseller kullanılarak yapılan öğrenme metodu. Bu metod matematikte ve fizikte üç boyutlu cisimleri içeren konularda kullanılabilir. Öğrencinin algılamasını daha da kolaylaştırır . Hatta bu şekilde verilen soruları üç boyutu ve gerçekliği kullanarak daha doğru çözme ihtimali ve şansı çocuklara verilmiş olur.

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Computer is a key element of the process in computer based instruction. Also, instructor can define as organizer of information.
Computer applications such as pictures, video, etc. can define as an advantage of this learning style.
On the other hand, in a computer supported instruction, it may help us to connect with other classes and schools. It means that it might be beneficial for the use of collaboration of groups, which is not possible as face to face.

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A course in which materials and instruction are delivered over the Internet to users around the world.

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In 1:1 Technology Programs, standardized devices for all teachers and their students are used. It's an expensive way for school administrators because the devices should be kept updated in few years. Research show that 1:1 technology initiatives improve the students engagements, their attendance,i academic success and etc.

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Learning occurs at everywhere, every time. So distance learning can be practical if students have no chance to learn at classroom or if students miss lessons. In online learning, students can learn lots of things thanks to educational technological tools creating by their teachers.

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this system specifies the needs of user and organize needed material and suggest improvement in a dynamic and interactive content.

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Once I had a chance to experience Synchronous E-Learning through Adobe Connect. It allows students to ask questions, write comments, start a discussion during an ongoing online lecture.

Asynchronous E-Learning is location independent e-learning that students can attend online activities or lab studies.

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I prefer computer supported instruction to computer based instruction because one answer connot be the answer to all problems. Sometimes, we may need use direct instruction or sometimes we may need student based instruction. Computer based instruction has some disadvantages, it forces us to give same lesson in each class. Also, we need to use a flexible instruction method to be effective as much as we can. Finally, educational technology does not mean using always technology in the class, it aims to give effective education.

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specialized learning period. there is direct communication and coloboration between teacher and students. this learning path can be used by technology or directly communication. but the main thing in this learning path is its specialized version for students in the education setting/ my comment, this way of learning construct the fundemantel understading about information and for the whole information about world.

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Tutorial means leading the students step by step on a assigned task while the tutor gives instruction at the same time.

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Drill and Practice is structured review of learned concepts. Examples of drill and practice are worksheets, flashcards, computer-based systems and colorful and animated graphics.

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Drill & Practice Alissa, Jessie, Carmen, Jenn, & Laura

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Berin BaranSimulation using in chemistry education helps students to increase their level of understanding on molecular level of knowledge.As we all know there are 3 basic method to memorise knowledge;these are macro,submicro and symbolic levels.In order to make an efficient lesson,especially in chemistry education,weneed to use some simulations,animations etc. to show students what happens between the molecules.

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Berin BaranSimulation using in chemistry education helps students to increase their level of understanding on molecular level of knowledge.As we all know there are 3 basic method to memorise knowledge;these are macro,submicro and symbolic levels.In order to make an efficient lesson,especially in chemistry education,weneed to use some simulations,animations etc. to show students what happens between the molecules.

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we can use simulation in math class in order to provide critical thinking and students' understanding clearly. for example, we use simulation in comparing data graphs.

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In Asynchronous e-learning students complete the tasks that are assigned according to their own time schedule.
In synchronous e-learning students have to finish their work according to the due-time.
Time issue could be very threatening for young students. When they were not able to finish their work to the due time. They can feel very pessimistic about their future.

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this technique is based on memorization and repetition of specific skills mostly used by behaviorists. the three basic steps achieved by this activity: assigning a task, performing a task, and providing feedback.

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augmented reality as a tool can be used to teach and learn chemistry in different concept like molecular shape, state of matter, reaction rates with mobile phones of users

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Drills and practices could be components of Focus Teaching in Second language teaching. It helps students play with the target structure so that they can come up with novel utterances. Kahoot and Socrative can be examples of online drills and practices we can make use of.

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Berin BaranThere are some schools,univerties that use the distance learning in the world.When it comes to chemistry education,we can say that sometimes studying/working in the labaratory conditions may be considered to be time consuming issue.Instead of working in actual chemistry lab, we can follow the instructions and procedure of the experiments,and we can watch the video of experiment or apply the simulation of the topic/experiment.

# data[].attributes.subject
Berin BaranThere are some schools,univerties that use the distance learning in the world.When it comes to chemistry education,we can say that sometimes studying/working in the labaratory conditions may be considered to be time consuming issue.Instead of working in actual chemistry lab, we can follow the instructions and procedure of the experiments,and we can watch the video of experiment or apply the simulation of the topic/experiment.

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Virtual Reality improve learning in Math via using some educational technological tools such as VRmath, Construct3D. Thanks to these tools, students can understand some complex situations easily. They can concretize abstract mathematics.

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Bu teknikle öğrencilerin anlatılan konunun görselleştirilmesi ve görsel bağlamda konunun daha iyi kavranması hedeflenebilir. Bu çizimler, görsel betimlemeler yardımcı olur.

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Virtual reality means that the computer generated stimulation of images or our environments that can be interacted by a person with using electronic equipment. in math education, we can use virtual reality in geometry to help students to see 3D geometry.

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Virtual

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Özellikle 1990'larda bilgisayar temelli eğitim programlarının ortaya çıkmasıyla öncelikle Amerika'da uygulanmaya başlanan bu programlar daha sonra tüm dünyaya yayılmıştır. Ülkemizde ise YÖK denkliğini sağlayan birçok kurumda uzaktan eğitim programları mevcuttur.

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'Dijital hikaye anlatımı' olarak dilimize çevrilen bu yeni terim, klasik hikaye anlatımını multi-medya uygulamalarını kullanarak daha etkileşime açık ve derinleşme imkanı olan bir hale getirmeyi kapsamaktadır. Hikayeyi, çekilen görüntüler, ses efektleri ve animasyonlar gibi uygulamalarla daha da pekiştiren bir yöntemdir.

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Digital storytelling apps and websites are really useful especially in foreign language teachinglearning. They can provide students with so many story book options that help students' understanding go well and quizzes that help measure- evaluate what students have learned from the assigned story.

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Pause Animations Play Animations

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v4.15.0.1 (Niles) b166118

All logos, designs, and brand names for Renaissance's products and services are trademarks of Renaissance Learning, Inc., and its subsidiaries, registered, common law, or pending registration in the United States.

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myON reader personalizes reading for students by recommending books based on their interests, reading level, and ratings of books they’ve read . myON reader tracks book usage and reading growth over time and can project a student’s future reading score based on their current reading activities within the system.

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Advantages
*It might create an advantage for the use of time.
*It can be helpful for dangerous  situations.
*It might not be useful for everything (in education), but it is very effective. (interesting, attractive, unforgettable...)
*Mostly student centered