Definitions

Digital Learning Technologies Higher education Definitions
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Definition of Digital Learning Technology

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2017
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What is your level of agreement or disagreement with the following statements? “Digital Learning Technology is…”

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Personalized learning Other Definitions

"Personalized learning refers to the range of educational programs, learning experiences, instructional approaches, and academic support strategies intended to address the specific learning needs, interests, aspirations, or cultural backgrounds of individual students." (Source: http://edglossary.org/)

Today’s learners want a learning experience that fits their personal needs, learning speed, preferred learning style, and, most importantly, their learning pathway – in other words, learning personalized for them. In a personalized learning environment, contents display is adapted to individual learning styles and needs. Content discovery moves from a “course catalogue” style to an adaptive model. In the old model, everyone learns from the same materials at the same pace. In an adaptive model, students are presented with learning activities based on what they know, what they need to know, and what has worked for other students like them.

[...] The adaptive learning mechanism comprises embedded assessments associated with the designed content.

"Technavio’s market research analyst predicts the global adaptive learning software market to grow at an impressive CAGR of close to 31% during the forecast period 2016-2020. The adaptive learning software market in the Americas is the largest among all the geographical segments and is expected to generate revenues of over USD 2 billion by the end of 2020." (Source: http://www.technavio.com/report/global-education-technology-adaptiveLear...)

An organization ready to support and build personalized learning needs to begin collecting learning analytics. Learning analytics, in many ways, is “big data,” applied to education.

"Whereas traditional forms of analytical processing rely on existing management data, such as student demographics, grades, and recruitment figures, more recent approaches to analytics rely on data that has greater variety and arises from traces left as people use IT systems. This is a central concern for learning analytics, where the data arises from normal use of multiple pieces of software designed for accessing learning resources, social interaction, content creation, etc. In many cases, therefore, practical learning analytics requires that data moves from operational to analytical systems and be put to a different use than originally intended. For example, the data structures in a VLE or LMS are likely to have been designed not for analytics, but to realise teaching and learning use cases - e.g. for accessing video content, participation in forums – in a way is technically scalable and maintainable. When statistical processing or data mining is undertaken, for example to support analysis of learner engagement, data has to be re-interpreted. This situation is further amplified by the necessity of combining data from various sources, or maybe to use cloud-computing based data mining engines, to build, test, and apply useful statistical and predictive models." (Source: Learning Analytics Interoperability – The Big Picture In Brief - Adam Cooper, Cetis, University of Bolton, UK)

As personalized learning integrates into the corporate space, learners will be able to collect and report on their own learning accomplishments using the Experience API, also called xAPI or Tin Can. Organizations and learners can use the Experience API to collect data outside of an LMS from any learning experience, completed in any environment, on any device.

 

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Personalized Learning

Resource: 
 
Publication Year: 
2016
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"Personalized learning refers to the range of educational programs, learning experiences, instructional approaches, and academic support strategies intended to address the specific learning needs, interests, aspirations, or cultural backgrounds of individual students." (Source: http://edglossary.org/)

Today’s learners want a learning experience that fits their personal needs, learning speed, preferred learning style, and, most importantly, their learning pathway – in other words, learning personalized for them. In a personalized learning environment, contents display is adapted to individual learning styles and needs. Content discovery moves from a “course catalogue” style to an adaptive model. In the old model, everyone learns from the same materials at the same pace. In an adaptive model, students are presented with learning activities based on what they know, what they need to know, and what has worked for other students like them.

[...] The adaptive learning mechanism comprises embedded assessments associated with the designed content.

"Technavio’s market research analyst predicts the global adaptive learning software market to grow at an impressive CAGR of close to 31% during the forecast period 2016-2020. The adaptive learning software market in the Americas is the largest among all the geographical segments and is expected to generate revenues of over USD 2 billion by the end of 2020." (Source: http://www.technavio.com/report/global-education-technology-adaptiveLearning-software-market)

An organization ready to support and build personalized learning needs to begin collecting learning analytics. Learning analytics, in many ways, is “big data,” applied to education.

"Whereas traditional forms of analytical processing rely on existing management data, such as student demographics, grades, and recruitment figures, more recent approaches to analytics rely on data that has greater variety and arises from traces left as people use IT systems. This is a central concern for learning analytics, where the data arises from normal use of multiple pieces of software designed for accessing learning resources, social interaction, content creation, etc. In many cases, therefore, practical learning analytics requires that data moves from operational to analytical systems and be put to a different use than originally intended. For example, the data structures in a VLE or LMS are likely to have been designed not for analytics, but to realise teaching and learning use cases - e.g. for accessing video content, participation in forums – in a way is technically scalable and maintainable. When statistical processing or data mining is undertaken, for example to support analysis of learner engagement, data has to be re-interpreted. This situation is further amplified by the necessity of combining data from various sources, or maybe to use cloud-computing based data mining engines, to build, test, and apply useful statistical and predictive models." (Source: Learning Analytics Interoperability – The Big Picture In Brief - Adam Cooper, Cetis, University of Bolton, UK)

As personalized learning integrates into the corporate space, learners will be able to collect and report on their own learning accomplishments using the Experience API, also called xAPI or Tin Can. Organizations and learners can use the Experience API to collect data outside of an LMS from any learning experience, completed in any environment, on any device.

 

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Microlearning Other Definitions

Microlearning is often referred to as bite-sized learning. It is a short learning nugget (three to five minutes in length, or shorter) designed to meet a specific learning outcome. While it can be used for formal training, it is majorly used in informal learning (with a focus on performance gain).

Microlearning nuggets are designed for and delivered in rich media formats. Their brevity and accessibility on multiple devices (including smartphones, tablets, desktops, and laptops) makes them an ideal fit for just-in-time training. Corporations can use microlearning for formal training as well as for learning on the job. Microlearning is ideal for distracted or busy corporate learners, as it gives them the opportunity to build their knowledge base when it’s most convenient for them.

Example: Google University

Effectively, Google operates an “invisible” corporate university, delivering personalized, just-in-time information to employees based on their job function and performance. Instead of giving new employees a training manual, Google provides bite-sized tutorial information just before it is needed. For example, managers are given guidance on how to complete performance reviews shortly before it’s time to do so.

Although the term Performance Support has been used for a number of years now, the majority of organizations have yet to introduce a learning-at-the-point-of-need strategy. We expect more businesses will start to move away from the traditional firehose method of delivering learning and, instead, separate training content into two groups: Information that employees need to know in their heads versus information they simply need at their fingertips to do their jobs better. This will change the way businesses deliver learning and pave the way for performance support methods to become mainstream.

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Microlearning

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Microlearning is often referred to as bite-sized learning. It is a short learning nugget (three to five minutes in length, or shorter) designed to meet a specific learning outcome. While it can be used for formal training, it is majorly used in informal learning (with a focus on performance gain).

Microlearning nuggets are designed for and delivered in rich media formats. Their brevity and accessibility on multiple devices (including smartphones, tablets, desktops, and laptops) makes them an ideal fit for just-in-time training. Corporations can use microlearning for formal training as well as for learning on the job. Microlearning is ideal for distracted or busy corporate learners, as it gives them the opportunity to build their knowledge base when it’s most convenient for them.

Example: Google University

Effectively, Google operates an “invisible” corporate university, delivering personalized, just-in-time information to employees based on their job function and performance. Instead of giving new employees a training manual, Google provides bite-sized tutorial information just before it is needed. For example, managers are given guidance on how to complete performance reviews shortly before it’s time to do so.

Although the term Performance Support has been used for a number of years now, the majority of organizations have yet to introduce a learning-at-the-point-of-need strategy. We expect more businesses will start to move away from the traditional firehose method of delivering learning and, instead, separate training content into two groups: Information that employees need to know in their heads versus information they simply need at their fingertips to do their jobs better. This will change the way businesses deliver learning and pave the way for performance support methods to become mainstream.

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Competency-based learning Higher education Definitions

Competency-based education combines an intentional and transparent approach to curricular design with an academic model in which the time it takes to demonstrate competencies varies and the expectations about learning are held constant.  Students acquire and demonstrate their knowledge and skills by engaging in learning exercises, activities and experiences that align with clearly defined programmatic outcomes. Students receive proactive guidance and support from faculty and staff.  Learners earn credentials by demonstrating mastery through multiple forms of assessment, often at a personalized pace.

Definition of Competency-Based Education

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Competency-based education combines an intentional and transparent approach to curricular design with an academic model in which the time it takes to demonstrate competencies varies and the expectations about learning are held constant.  Students acquire and demonstrate their knowledge and skills by engaging in learning exercises, activities and experiences that align with clearly defined programmatic outcomes. Students receive proactive guidance and support from faculty and staff.  Learners earn credentials by demonstrating mastery through multiple forms of assessment, often at a personalized pace.

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