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Ed-Tech Trends of 2017 in Higher Education (so far)

Ed-Tech Trends of 2017 in Higher Education (so far)

August 22, 2017 | By: Dr. Dovi Weiss, Chief Scientist, Time To Know

The NMC Horizon Report 2017 - Higher Education Edition has been published recently. The Horizon report is a collaborative effort between the NMC and the EDUCAUSE Learning Initiative. It describes annual findings from the NMC Horizon Project, an ongoing research project designed to identify and describe emerging technologies likely to have an impact on learning, teaching, and creative inquiry in higher education.

The report comprises three parts:  Six key trends, six significant challenges, and six important developments in educational technology.

 

In this post I will describe the six important developments in educational technology for higher education. The next two posts will describe the six key trends and six significant challenges that are most influential on higher education.

Each of the six developments in educational technology detailed in this post were selected by the Horizon’s expert panel. The technology developments, which the members of the expert panel agreed are very likely to drive technology planning and decision-making over the next five years, are sorted into three time-related categories — near-term developments that are expected to achieve widespread adoption in one year or less; midterm developments that will take two to three years and far-term developments, which are forecasts to enter the mainstream of education within four to five years.

 

Time-to-Adoption Horizon: One Year or Less

 

Adaptive Learning Technologies: Adaptive learning refers to the technologies monitoring student progress, using data to modify instruction at any time. Adaptive learning technologies, dynamically adjust to the level or type of course content based on an individual’s abilities or skill attainment, in ways that accelerate a learner’s performance with both automated and instructor interventions.

 

Mobile Learning: The pervasiveness of mobile devices is changing the way students interact with content and their surroundings. Mobile learning, or m-learning, enables learners to access materials anywhere, often across multiple devices. For more on mobile learning, see our other blog titled "Your Complete Guide to Distance Learning."

 

 

Time-to-Adoption Horizon: Two to Three Years

 

The Internet of Things: IoT consists of objects endowed with computing power through processors or imbedded sensors that are capable of transmitting information across networks. These connections allow remote management, status monitoring, tracking, and alerts. Connected devices are generating data on student learning and campus activity, informing the direction of content delivery and institutional planning. As more smart devices arrive on campuses, institutions are examining implications for privacy and security.

Next-Generation LMS: LMS have long been adopted by colleges and universities worldwide to manage and administer online and blended courses. It is commonplace for students to access syllabi and readings, submit assignments, check grades, and contact peers and instructors through their institution’s LMS. Next-generation LMS, also called next-generation digital learning environments (NGDLE), refers to the development of more flexible spaces that support personalization, meet universal design standards, and play a larger role in formative learning assessment.

 

Find out more about next-generation LMS and more at timetoknow.com!

digital learning

 

Time-to-Adoption Horizon: Four to Five Years

 

Artificial Intelligence: Artificial intelligence (AI) is intelligence exhibited by machines. There are three key developments in AI that might influence learning:

  • Knowledge Engineering allows computers to simulate human perception, learning, and decision making is based on access to categories, properties, and relationships between various information sets.
  • Machine Learning is a subset of AI, providing computers the ability to learn without being explicitly programmed
  • Neural Networks model the biological function of human brains to interpret and react to specific inputs such as words and tone of voice

AI has the potential to enhance online learning, adaptive learning software, and research processes in ways that more intuitively respond to and engage with students.

 

machine learning, deeper learning, mobile learning, higher education

 

Natural User Interfaces: Natural user interfaces (NUIs) accept input in the form of taps, swipes, and other ways of touching; hand and arm motions; body movement; and increasingly, natural language. These NUIs enable users to engage in virtual activities with movements like what they would use in the real world, manipulating content intuitively. Developments in NUIs are enabling greater access to education for those with disabilities. Experiments with NUIs have the potential to unearth new forms of learning and communication in education.

 

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