Find your portal

Edtech definitions every teacher should know: Part 1

A while ago we received a comment on a post about Augmented Reality. I had erred by not at any point in the blog referring to Augmented Reality, but only AR. Our reader asked us simply to clarify what AR meant. It occured to me that not only are the wide variety of acronyms found in the world of edtech confusing (I did a post on organizations in that regard), but indeed there is quite a bit of overlap — as well as misuse — of terms that can make navigating the literature (in particular online literature) on the subject of edtech confusing and unhelpful.

Edtech definitions every teacher should know

So, with apologies to our regular readers, who no doubt have mastered the edtech shorthand and specific phrases, let’s develop a quick terminology with the help of key and quality online sources. I have also situated phrases that are often incorrectly interchanged against one another for what I hope is a useful comparison. Where possible I will define general tech terms in an educational context.

AR, VR, MR — the realities

Augmented Reality (AR): This is a digital overlay onto reality. So when viewing “reality” through the camera or mobile screen, digital tools augment or add to what you see. Pokémon GO is an excellent example, where players see the real world, but also see Pokémon characters in that world, ready to be collected. In an educational context this technique is most often used, currently, in AR sensitive textbooks, where for instance a technical drawing may be in 2D to the naked eye, when viewed through an app enabled mobile phone camera, the drawing comes to life in 3D.

Read more: Can Pokemon Go be part of the classroom?

Best Online Definition - WikiPedia: “Augmented reality (AR) is an interactive experience of a real-world environment where the objects that reside in the real-world are ‘augmented’ by computer-generated perceptual information, sometimes across multiple sensory modalities, including visual, auditory, haptic, somatosensory, and olfactory.”

Virtual Reality (VR): Where AR is an overlay of digital graphic and other sensory layers, Virtual Reality is the full immersion into an interactive digital world, that stimulates 100% of the visual and auditory senses. The best educational example of this, I think, Google Cardboard where students simply download a VR educational app, slip their smartphones into a set of cardboard goggles, and are taken on a fully immersive journey to places such as Mars, the Great Wall of China and the Louvre.

Read more: 5 useful VR apps for the modern classroom

Best Online Definition - The Virtual Reality Society: “A three-dimensional, computer generated environment which can be explored and interacted with by a person. That person becomes part of this virtual world or is immersed within this environment and whilst there, is able to manipulate objects or perform a series of actions.”

Mixed Reality (MR). This, I admit, was a new one for me. Sometimes referred to as Hybrid Reality, the term refers in essence to the next iteration of Augmented Reality. However the digital artifacts interact and engage with the “real” world far more seamlessly. Take the example of the 3D rendering in my description of AR above: with Mixed Reality not only would that rendering be entirely 3D, but would also be navigable and manipulable by the viewer, who could conceivably place the rendering within an actual, physical environment to see how it would respond to what is actually there. As this is relatively new concept the educational applications are few and far between, keep an eye on the K-20 Blog for more news on MR in the future.

Best Online Definition - WikiPedia: “Mixed reality (MR), sometimes referred to as hybrid reality, is the merging of real and virtual worlds to produce new environments and visualizations where physical and digital objects co-exist and interact in real time.”

Machine Learning vs Artificial Intelligence

A really common error that I see online quite often is people using these two terms interchangeably. As both concepts are extremely important to understand: both in terms of understanding the World’s technological trajectory of the next 20/30 years as well as understanding the impact these powerful tools have on our lives. Please visit Aeon in this regard, which (as a brief aside) is simply one the very best free online resources for insightful comment from top-drawer academics, writers and thinkers.

So, what is the actual difference between the two terms?

Artificial Intelligence (AI) is a product of the much older computational concept of “logical machines”, and Machine Learning (ML) is in turn a byproduct of AI.

In the 1950s scientists were working to create logical machines — machines that could record information — and make new calculations based on information previously recorded. In fact as early as 1957 Frank Rosenblatt had designed the first neural computer network which mimicked the thought processes of the human brain (Find an interesting potted history of AI at Forbes Magazine).

As microprocessors decreased in size, allowing for greater computational power, these machines became increasingly able to make faster, more complex calculations, yet still not what one would call “intelligent”. Currently the ambition of AI is to create machines that can problem solve in the same way a human would, using and collating a range of disparate information and applying both clean and fuzzy logic to resolving problems. Examples include automated stock trading software and automated cars.

Read more: 4 Ways AI will be a great teaching assistant

Machine Learning (ML) - while undoubtedly the offspring of AI, ML has, in my opinion, an altogether more creative output. Where previously computers required inputs and instructions from a program or programer, ML means machines are programmed how to learn.

As information storage boomed in the Internet age, huge sets of data have suddenly become easily stored and accessible. By teaching computers how to “read” the data, to “understand” a certain analytical goal and how to switch functions and algorithms to achieve that goal, computers are in a sense “let loose” on the data often times observing patterns and making connections humans could quite simply never do (due to the quantity of data involved).

Additionally, once all the available data has been consumed and analyzed by the computer it has already “learned” what to look for, and in some cases is able to rewrite their own algorithms to better process the information — without any human input.

Best Online Definition (AI) - Encyclopedia Britannica: “Artificial intelligence (AI), the ability of a digital computer or computer-controlled robot to perform tasks commonly associated with intelligent beings. The term is frequently applied to the project of developing systems endowed with the intellectual processes characteristic of humans, such as the ability to reason, discover meaning, generalize, or learn from past experience.

Best Online Definition (ML) - Techopedia: “Machine learning is an artificial intelligence (AI) discipline geared toward the technological development of human knowledge. Machine learning allows computers to handle new situations via analysis, self-training, observation and experience.”

Stay tuned for Part 2!

Stay tuned for our next part in this definitions series where we will explore the practical meanings of terms such as: Data Mining, Big Data, Meta Data, API, Badging, Blending and more.