Five reasons we think Glass is going to matter:

• Ubiquitous – always on, always there – we left computers behind on our desks when we left home or work, we carried our laptop and notebook computers around, called them portable but didn’t really use them everywhere, then came tablets/smart-phone like devices; we carry them around but we aren’t always looking at them, or using them. Glass changes this in a marked way, rather than your computer being something you carried, turned on and looked at when required, it is cumbersome. Glass overcomes that by being always on, and as something you wear, always there.In this video, it becomes apparent that the wearable nature of the device overcomes many of the physical limitations of a device that must be carried, and turned on/off (or woken/sent to sleep).This video gives a great example of ubiquitous always-on devices integrated with back-end systems is capable of
• Capable of Continuous Capture – With an inbuilt camera for capture photos and video, Glass offers the ability to capture the user’s field of view. This is a pretty unique feature, up to now our devices with capture capabilities have been cumbersome. While smart-phones have changed the game for photos and videos, it doesn’t really offer a first-person view unless one makes an attempt at it. Google Glass changes that; because of its wearable design the Glass camera has a field of view that is close to what the user is actually seeing. While there has been a flood of ‘how-to’ videos around for a while, Glass takes away many of the perceived barriers to first person field of view video (device not on person, or carried on person but put away, having to turn it on/waken it, and then accessing the device menu to access the camera hardware, etc.). While at this time, the video duration Glass can record is limited, an SDK would allow a developer to circumvent that.Google Glass: What It DoesImagine a future where you can choose to record (and broadcast/stream)video on the go related to any type of performance. This will eventually lead to video content generated on the fly, that is indexed and documents job performances ranging from mundane to highly complex that require well-honed skills.
• Truly Location Aware – desktop computers weren’t location aware, and neither were the portable computers, the advent of phones bought approximate location awareness to devices. Embedding GPS, which is far more accurate than network/cell-based triangulation in wearable devices offers a fine location that is far more useful than a coarse approximation, Glass will always know where it is. While one could visualize many use-case scenarios for location in a wearable device, nothing is as persuasive as being provided information just-in-time based on location. If you are in a museum, get information about what you are seeing, or have detailed information presented to you as you navigate a new workplace, or as one attempt to use a particular use of equipment; the possibilities are enormous.Just to get a good idea of what Google Glass is actually about –Check out the bit where the reviewer asks for directions or Oriole Park, shows what location awareness does for the device and applications. The entire video points to the utility of a device such as Glass.
• Ability to Augment Reality – augmented reality so far has been focused around smart phones; simply because AR depends on the ability to determine location and the orientation of a device, something only smart-phones equipped with sensors and GPS allowed till this point in time. Glass becomes perhaps the first bit of wearable kit that comes with sensors and a display (coupled with it always pointing at the user’s field of view). If looked at differently, Glass can be considered as nothing but a device that is meant to augment reality, something smart-phones weren’t designed to do. This ability to augment reality with information or graphics points to a future of applications for learning that are context-sensitive and actually useful.I love this example of a Google Glass application being used to augment a baseball game
• Truly Hands-free – computers and smart-phones need ‘manipulation’ with hands to make them productive, they need input devices that require physical dexterity and hand-eye coordination. Glass doesn’t use any sort of hardware input device, it depends on advanced voice recognition to interpret commands and act accordingly – what this has done is to free up the user’s hands.This video gives insight into how voice works on Google glass (this is a bit geeky)So now, you could actually be using your computing device while using your hands to ‘work’ on something. One of the simplest examples I can think of is where a Glass-like device is being used when you are fixing something wrong with a car. The display overlays what you see with technical information, repair instructions pertinent to your situation, all this while you continue to work with physical tools – performance support like we’ve never known it.

One of the arguments I often hear is that a comprehensive SDK isn’t in place yet, and making something meaningful from a learning perspective won’t happen until there is an reasonably easy-to-use and cheap SDK in place. At this point in time, any development on Glass is restricted to what you can dream within the boundaries of a fairly restrictive API. As an Android device you could possibly used the Android SDK for development, but other than try out some ideas, I don’t see it worthy of sustained development efforts.

To create something meaningful, robust and use-worthy, we need a Glass specific SDK – it seems that just might be happening shortly – Google seems to be readying to launch the Glass Development Kit .

The age of context is upon us, hardware and software are now driving towards providing computing capabilities in the context of use – whether it is work, play or learning. Performance support applications will change form and become context driven. I believe Glass is just the first wave of devices that will provide context, this will fundamentally change how we leverage learning technology in our day-to-day lives.

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There is a convergence happening, one that is widespread but only just beginning to surface on the radar of learning technologists – it’s the joining of huge numbers of smartphones and televisions. Before the internet was widespread, content was segregated into silos based on the type of device used to access in – there was the print content, radio and of course television – essentially you read on paper, listened to the radio and watched TV. The delineation was quite clear. The internet coupled with mobile connected personal computers (smart phones) changes that; actually destroying the fundamental barriers between media.

Today, I use Google Chrome to take my reading, listening and viewing from platform to platform, or to be more precise from screen to screen and this transition happens seamlessly. I might start out on the tablet in the morning, watch related videos on TV as I have breakfast, have content read out to me as I ride to work and then be able to access that very content on my computer in the office, and this is just the beginning of our lives being dominated by multiple screens.

This future seamless melding of multiple screens will be another massive developer playground. Which also means, just like the smart-phone space, we will see the development of distinct ecosystems. Google and Apple are already formidable presences in this market, and you never know what technology is lying around the corner seeking to disrupt established players.

In my opinion the first wave of implementations for multi-screen will enable publishers to develop rich content applications across devices within a single ecosystem. For example, a single application that can control content, data and presented on a HDTV, iPad, iPod or other displays within that eco-system. Such an app might also allow for synchronization between devices and across screens, which is a natural technological evolution as all these are screens are in essence connected computers. This has significant impacts on how we perceive content delivery for learning. Consider that we are only just beginning to connect all the screens that surround us, how would a learning designer leverage this in the design of courseware or tools for performance support?

Just a decade a ago, we couldn’t have imagined the impacts the smart-phone revolution is having on our daily lives and in the way we learn. Perhaps TV in ten years will be very different from what we know today as a one-way video channel. The APIs and tools for creating application for TV are emerging, how can we as learning designers use them?

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Ever browsed through an Ikea catalog? Then you’d know there is an aesthetic value in the glossy paper, fabulous product photography and innovative use of color and layouts. It’d be a shame to throw that out the window and replace with a digital version. Hit this link to check out videos of what Ikea did rather than completely replace the 211 million print copies of their catalog, they chose to add digital elements to using QR-like codes. It’s quite impressive, because of the way they used AR technology to enhance rather than replace print: One to let users get an ‘x-ray view’ to look inside furniture. Second, to let users interact with 3D models of (this sort of thing has great applications in engineering and technology courseware) and third, links to videos of products in action.

One of our clients actually took it a step futher – they are now printing QR codes on all their product labels that are directly tied to ‘just-in-time’ use resources – videos that show the product being used, Dos and Don’ts, and the ability to subscribe to advanced courseware linked to the product. Rather than pushing or forcing training on individuals, provide information at the point of need, where learners value it because it helps them do the task at hand. (more about this later).

It is the right time to explore AR to supplement learning, perhaps make it an essential part of a blended learning program. The ability to bridge the physical and virtual worlds can make a huge difference in how we view performance support. Over the course of the last few years, we’ve experimented quite often with augmented reality (AR) technology, either in the form of AR apps for mobile phones, or leveraging AR browsers, etc. We published several posts around this then, talking about the tools you can use and how Layar content can be developed, and more. Some of these articles are dated, but that doesn’t detract from their value.

Leverage augmented reality, there are many worthwhile applications. Because we are on the subject of AR – check out some indie film-makers view on the future of AR – ‘Sight’ That AR technology will be common place in a few years is accepted, will learning be one of its key application?

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Lastly, most desktops will see (perhaps) a gradual migration to Windows 8; the roadmap states “Adobe is currently working closely with Microsoft to finalize details around supported configurations for Flash Player and Adobe AIR on Windows 8.” That sounds quite strange, given that the Metro Touch version of IE will not support the Flash plugin.  Given its memory intensive nature it isn’t surprising that Tablet OS developers aren’t keen on having it;  iOS made that choice a long time ago.

I’d hazard a guess Android will be abandoning Flash mobile too.

So where are we going with Flash? And will it remain the authoring/development environment of choice for eLearning developers/designers? It’s a tough question to answer. I want to be able to develop once and run on both desktop, mobile and ‘in-between’ devices. Without a stable and feature-rich HTML5 development environment that matches the depth of Flash, eLearning developers are faced with a dual development approach – develop with full media rich functionality  (easily) using Flash for desktop delivery, and a more technically intensive HTML5 approach for devices. Who knows, in the future maybe workplace learning will ONLY be driven by mobile devices, with desktop based elearning turning into an archaic remnant of a bygone age.

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The argument then was that chess is about finite number of possible moves. The use of intensive mathematics, brutal processing logic and speed make chess a well defined challenge – computers were appropriately designed for such a challenge. However, natural language is very different. Modeling natural language mathematically is very challenging, and at the time (of deep blue vs. Kasparov), even natural language processing researchers admitted we were many years yet before computers would understand queries and respond to them in human language.  I’ve banged on about intelligent personal learning agents based on semantic technologies in the past, and Watson – a ‘natural language processing’ ‘pattern recognizing’ ‘world aware’ engine – is a huge step towards making that happen.

Ray Kurzweil calls the Watson Jeopardy match-up a “milestone” in the progression of machines to achieving human intelligence , which will be reached within 20 years, he believes. While I don’t agree with a lot of things Kurzweil says because they don’t necessarily seem scientific, I’d agree about machine intelligence rapidly evolving and overtaking human cognitive abilities. In my opinion, computers with software/hardware that support true natural language processing abilities coupled with augmented reality interfaces can change the learning landscape; or to be more precise, the way performance support is perceived and used in the workplace. At this time, performance support tends to be viewed to happen in discrete episodes. Ex., I refer to a quick programming guide when configuring a DVR; or use a calculator while doing math. But in a world with wearable interfaces and ubiquitous computing, it’s easy to imagine a situation where data about the workplace environment is constantly available and updated in real-time; this changes how we view performance support itself.

Couple this sort of wearable, connected interface with an intelligent software agent that can make sense of its environment, has constant access to large amounts of information about the environment, and understands and can respond in natural language is the digital assistant that I dream of. I’d like it to be able to do five things initially:

1. Answer questions – Simply answer questions based on its best judgment and confidence using the internet’s data space and sets; and respond in human language with a concise but wide-ranging answer.
2. Do research – find patterns, trends and be able to make effective recommendations for tasks in the workplace based on pertinent internet data sets.
3. Communicate, negotiate schedules – be able to communicate with other individuals ‘personal computing agents’ to exchange information, and schedule meetings/calls and the like.
4. Assist Decision making based on large sets of existing data – there are situations in which humans need to make quick decisions without the benefit of prior experience; in such situations, the agent would be able to determine the best possible decision based on past experience (internet data) and take corrective action. While this sounds a bit scary, being able to capitalize on the experience of others in situations similar to ours is very useful for learning.
5. Digital memory/stream – the agent will constantly monitor all life activities and keep a record of decision, situations encountered and the environmental variables at the time. This stream of data will constantly be referenced and will serve for further decision making on part of the agent. Additionally, when stripped of identifiable personal data, these streams can help support other intelligent agents in making decisions.

Put together, these five would probably amount to a super duper performance support system. Available at all times, usable in all contexts, with situational awareness, access to vast quantities of information, and human language cognition abilities to interpret that data – it will make an awesome learning tool.

I will probably be able to tell my agent in the future “tell me how this machine works” or “I want to do ‘this’ with the machine” and the agent would be able to create a concise summary from internet data about that particular machine, that is human  understandable and I could use to act immediately. Extend this type of questioning and response from the agent into a hundred workplace situations and you’ll find it applies equally well.

Advances like the one IBM’s Watson demonstrates and increasingly miniaturized wearable interfaces, and ubiquitous computing will change learning, sooner than we imagine. I for one, can’t wait.

P.S. You can read a succinct note about Watson here

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“People will swarm more often and work solo less. They’ll work with others with whom they have few links, and teams will include people outside the control of the organization.”

“In addition, simulation, visualization and unification technologies, working across yottabytes of data per second, will demand an emphasis on new perceptual skills.”

–       Tom Austin, Vice President and Gartner Fellow

Gartner points out that the world of work will probably witness ten major changes in the next ten years. Interesting in that it will change how learning happens in the workplace as well. The eLearning industry will need to account for the coming change and have a strategy in place to deal with the changes.

1. “De-routinization” of work. The core value that people add is not in the processes that can be automated, but in non-routine processes, uniquely human, analytical or interactive contributions that result in words such as discovery, innovation, teaming, leading, selling and learning.

2. Work swarms. Swarming is a work style characterized by a flurry of collective activity by anyone and everyone conceivably available and able to add value. Gartner identifies two phenomena within the collective activity; Teaming (instead of solo performances) will be valued and rewarded more and occur more frequently and a new form of teaming, which Gartner calls swarming, to distinguish it from more historical teaming models, is emerging.

3. Weak links. In swarms, if individuals know each other at all, it may be just barely, via weak links. Weak links are the cues people can pick up from people who know the people they have to work with. They are indirect indicators and rely, in part, on the confidence others have in their knowledge of people. Navigating one’s own personal, professional and social networks helps people develop and exploit both strong and weak links and that, in turn, will be crucial to surviving and exploiting swarms for business benefit.

4. Working with the collective. There are informal groups of people, outside the direct control of the organization, who can impact the success or failure of the organization. These informal groups are bound together by a common interest, a fad or a historical accident, as described by Gartner as “the collective.” Smart business executives discern how to live in a business ecosystem they cannot control; one they can only influence. The influence process requires understanding the collectives that potentially influence their organization, as well as the key people in those external groups.

5. Work sketch-ups. Most non-routine processes will also be highly informal. It is very important that organizations try to capture the criteria used in making decisions but, at least for now, Gartner does not expect most non-routine processes to follow meaningful standard patterns.

6. Spontaneous work. This property is also implied in Gartner’s description of work swarms. Spontaneity implies more than reactive activity, for example, to the emergence of new patterns. It also contains proactive work such as seeking out new opportunities and creating new designs and models.

7. Simulation and experimentation. Active engagement with simulated environments (virtual environments), which are similar to technologies depicted in the film Minority Report, will come to replace drilling into cells in spreadsheets. This suggests the use of n-dimensional virtual representations of all different sorts of data. The contents of the simulated environment will be assembled by agent technologies that determine what materials go together based on watching people work with this content.

8. Pattern sensitivity. Gartner expects to see a significant growth in the number of organizations that create groups specifically charged with detecting divergent emerging patterns, evaluating those patterns, developing various scenarios for how the disruption might play out.

9. Hyperconnected. Hyperconnectedness is a property of most organizations, existing within networks of networks, unable to completely control any of them. Hyperconnectedness will lead to a push for more work to occur in both formal and informal relationships across enterprise boundaries, and that has implications for how people work and how IT supports or augments that work.

10. My place. The workplace is becoming more and more virtual, with meetings occurring across time zones and organizations and with participants who barely know each other, working on swarms attacking rapidly emerging problems. But the employee will still have a “place” where they work. Many will have neither a company-provided physical office nor a desk, and their work will increasingly happen 24 hours a day, seven days a week. In this work environment, the lines between personal, professional, social and family matters, along with organization subjects, will disappear.

The Gartner report “Watchlist: Continuing Changes in the Nature of Work, 2010-2020.” is available on Gartner’s website at http://www.gartner.com/resId=1331623. (Purchase required)

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Educating the Mobile Generation

As a teacher confirms in this video “the understanding the students have when working with mobile devices is much deeper”. And Prof Elliot Soloway says that “mobile technologies are going to make a bigger change to our lives than the PC and Internet together”. There’s no doubt the impact on corporate training too would be equally huge.

Designing Schools for 21st Century Learning

Randall Fielding talks about designing schools that stimulate learning. He believes (and research shows) stimulus rich environments improve learning. He makes a very interesting comment: “There’s no research out there that says you need to be 18 years old to take charge of your own learning”. Some interesting thoughts on how children learn.

Check out the other videos too at the www.NewLearningInstitute.org

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Having mentioned Web 3.0 often in the past, I continue my research into it. Last evening, this particular slide share presentation about Web 3.0 and beyond popped into my inbox. Steve Wheeler at the University of Plymouth put it together.

Interesting to see that in the world of Web 3.0 , eLearning will actually transform from a content delivery and tracking mechanism. The rapid development of ontologies and taxonomies, the emergence of artificial intelligence allowing digital agents and assistance, and powerful mobile computing will probably mean the death of the LMS as we know it.

On the other hand, the proportion of content consumers to creators, which is currently heavily skewed towards consumption will even out with increasing number of creators. While it may seem unlikely at this time, I can envisage a future where ‘courseware’ as we know it won’t really exist. Instead, intelligent agents will gather and aggregate information from repositories and present those appropriately at the time of need.

It was also nice to see that some of the technologies that we’ve been experimenting with – like Augmented Reality and Virtuality – feature in the presentation. Take a look, great infographics and clear presentation.

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