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.
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.
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 NEO 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.
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.”
Deep Learning vs. Deep Learning
Last time I discussed machine learning vs. artificial intelligence; a phrase that has cropped up as an addition to those discussions and topics is “deep learning”. Confusingly, this is also a phrase bandied about in education circles, and unfortunately for us EdTech proponents, they mean quite different things.
Deep learning (Tech): Where machine learning is a part of the broader umbrella of artificial intelligence, deep learning is in turn a function or derivative of machine learning. Unsurprisingly, Google is at the commercial forefront of deep learning, with a team they call “Google Brain”. The clue is in the name: deep learning leverages the large neural networks now possible with increased computing power, to develop sophisticated algorithms that take machine learning to a more productive and notionally more organic level.
Deep learning machines have the ability to learn and discover their own features, as well as train themselves— a function of what most scientists working in the field refer to as hierarchical feature learning. “Deep” here refers to how “deep” the algorithms can go in terms of mapping and labeling “higher” features and concepts from “lower” ones.
Best Online Definition – “Deep Learning” book by Ian Goodfellow and Yoshua Bengio and Aaron Courville: “The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones. If we draw a graph showing how these concepts are built on top of each other, the graph is deep, with many layers. For this reason, we call this approach to AI deep learning.”
Deep(er) Learning (Ed): The Deep(er) Learning concept is a reaction to the pressure and consequences of standardized testing. As Ron Berger, chief academic officer at Expeditionary Learning says, “The push in [education] has been so deeply around accountability based on high-stakes assessments that educators have become more and more fearful that the kind of going deeper has not been celebrated and prioritized.”According to Berger and other educational reform advocates, deeper learning yields a number of academic skill-sets that enable students to better learn from, adapt to, resolve and thrive in a changing or challenging environment.
Many pedagogues decry the buzzword because it describes what all dedicated teachers have, and always will, aim for as an educational goal — and is nothing new. In that sense the phrase is perhaps more useful when describing a method, rather than an outcome — Deeper Instruction, as some have suggested — we’ll explore this fascinating concept in future posts.
Best Online Definition – Hewlett Foundation: “The six Deeper Learning competencies: master core academic content, think critically and solve complex problems, communicate effectively, work collaboratively, learn how to learn, develop academic mindsets.”
Differentiated Learning vs Personalized Learning vs. Individualized Learning
Oh Boy! Not only are these three phrases relative behemoths of the current education lexicon, at a distance they mean almost the same thing: hence the many instances they tend to be interchanged, misused and hence misunderstood, in an educational context. Lay readers may feel we are splitting hairs here, however because the phrases do in fact denote different teaching methods, all be they nuanced, teachers need to know the difference.
Differentiated Learning: This way of learning (and teaching) acknowledges that everyone learns differently. Naturally there are broad, as well as more narrow, categories into which students can be cast with regard to how they learn, but the ambition of differentiated learning is to, as much as possible, manipulate classroom inputs, pace, assessment rubrics, approaches, groupings, resources and environments (per student) to achieve the best results. Importantly, differentiated learning does not denote any changes in curriculum. The entire class is learning the same thing, yet students reach those outcomes in different ways.
Best Online Definition – Carol Ann Tomlinson: “Ensuring that what a student learns, how he or she learns it, and how the student demonstrates what he or she has learned is a match for that student’s readiness level, interests, and preferred mode of learning.”
Individualized Learning: Stay with me here: Individualized learning is a tool within the Differentiated learning toolbox. However, definitions are not agreed upon. Some hold that individualized learning refers exclusively to the pace of instruction, while others frame differentiated instruction as a teacher’s ability or license to teach groups within a class differently, individualized instruction defines teaching 1-on-1. On balance I have found more references to the definition pertaining to pace.
Best Online Definition – Dale BasyeM – “Instruction calibrated to meet the unique pace of various students is known as individualized learning. If differentiation is the ‘how’ then individualization is the ‘when’.”
Personalized Learning: Probably the most commonly used term, it is most often used as an umbrella phrase that encompasses instruction that is individualized and differentiated. In some source material however personalized learning is distinguished from the other two, by allowing students to have far greater say not only in how and when they study — but indeed what. Personalized learning in this context is then perhaps the most flexible of all three styles, and describes a truly modern teacher-student relationship that is highly collaborative.
Best Online Definition – Glossary of Educational Reform: “The term personalized learning, or personalization, refers to a diverse variety of educational programs, learning experiences, instructional approaches, and academic-support strategies that are intended to address the distinct learning needs, interests, aspirations, or cultural backgrounds of individual students.”
We’ve examined some truly interesting concepts today, and I am now curious as to how technology is enabling and empowering them. Look out for future blogs where we will further explore these innovative instruction methods through the lens of technology.