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99 Solutions Named Best Of Elearning! 2016

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99 Solutions Named Best Of Elearning! 2016

The 2016 “Best of Elearning!” awards honor best-in-class solutions across the learning and… Read more...

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The Silo Organizational Structure: An office where no one communicates, everyone hordes their… Read more...

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Three Disruptive Macro Trends Shaping Enterprise Technology

Artificial Intelligence, Machine Learning, Intelligence Systems. These applications are… Read more...

2017 HR Technology Trends

We are now embracing an era when both enterprise and personal technology options are improving… Read more...

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Imagine if a technology came along that allowed your students to transport themselves to any time… Read more...

99 Solutions Named Best Of Elearning! 2016

The 2016 “Best of Elearning!” awards honor best-in-class solutions across the learning and… Read more...

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The 2017 E-learning User Study was conducted by Elearning! Media Group via an online survey of learning professionals to reveal the current trends and practices in e-learning. These findings were tabulated from 363 responses across corporate, government, education, and non-profit organizations. The study was conducted industry wide, including Elearning! Magazine subscribers. E-learning encompasses enterprise-wide learning and workplace technologies.

LEARNING DEPLOYMENT BY LOCATION TYPE

Drivers for Learning Investments

Employee engagement and improved collaboration are the top business objectives for learning investments. Personalized  learning moves up to #3 in priority.

Training PrioritiesCompliance regains the lead in training priority for 2017.

ELM March First Look 1

LEARNING SOLUTIONS USED & PURCHASES

Learning teams use a variety of solutions and are actively sourcing new solutions. The fastest growing solutions based upon purchase intention are:

ELM March First Look 2

Published in Trends

Ed Stengel joined Ingenuiti, as client services manager. Ingenuiti delivers e-learning and translations services to global enterprises. Stengel departed Brookwood Media Arts, after 11 years of service as director of e-learning services.

 

Published in People on the Move

The top consumer technology transformations were revealed by Dr. Shawn Dubravac, Research Director, Consumer Technology Association. These trends will impact enterprise technology over the coming months and years.

1. The Next Computer Interface is Voice

The word error rate is now at human parity, meaning the graphic user interface will disappear ushering in an era of faceless computing. Voice will be the command function for digital devices including robotics, A.I., etc.

2. Increasing Intelligent Systems  will Connect Diverse Objects

Software is now found in hardware out of the box, and hard- ware is eating software. Alexa is found in refrigerators and automobiles. While google’s newest smartphone is embedded with AR/VR software out of the box.  “This creates a ‘physical manifestation of data’ in our lives,” says Dubravac.

3. Transportation Transformation

The self-driving car was the catalyst for intelligent systems. With connected systems reporting to other cars, the vehicles can respond and react without human intervention. This is a model of many “robotic” type activities that may complement or replace human interactions.

4. A.I.’s Infusion into Real Life

Blending data from diverse devices is improving signal, functionality, and recommendations for users to follow. Hub de- vices will be used for vocal computing. A.I. will boost informa- tion processing geometrically. For example, Google cars have already driven more miles than a human can in 75 years. The speed, experiences, data collection and sharing has increased geometrically.

5. Digitizing the Consumer Experience

From wearables to smart home, online and mobile characterizes consumers tastes. Drone purchases reached 1.1 million units in 2016; VR 700,000 units; smart watches 5.5 million units and fitness trackers 12.6 million.

—Source: CTA.tech  bit.ly/CES2017TRENDS

Published in Insights

BY PRADEEP KHANNA

The other day, I went to meet someone in downtown Sydney, Australia. On my way, back on the local train, I looked at my mobile to check my emails and found a message asking me whether I would like to meet the person I had just connected with on my LinkedIn network. So, was this some form of artificial intelligence (AI) at play?

Yes! We now live in a brave new world where AI is the next frontier. We keep hearing about bots, chatbots, teacherbots, digital assistants, machine learning, deep learning and many more such words and often wonder what do they mean.

Just like virtual reality (VR), AI has been around for quite some time. In fact, I remember taking AI as a subject when doing my second master’s degree in computer science at University of Technology in Sydney 17 years ago. So, why so much fuss about AI now? AI will reshape how we live and work, but will AI also reshape the way we learn?

ABOUT BOTS, CHATBOTS, TEACHERBOTS AND A.I.

A bot is software that is designed to automate repetitive tasks. Bots have been around for quite some time. An example is use of bots for searching and cataloguing Web pages for search engines. Another example is shopping bots which pull out prices of an item from different vendors from the Internet. Some recent examples are bots making dinner reservations, adding an appointment to the calendar, or fetching and displaying information.

Chatbots are bots that conduct a conversation mirroring potentially a real-life conversation. Chatbots can either be simple rule-based or more sophisticated AI-based. AI-based chatbots get smarter as more interactions take place. The popularity of messaging apps has been lifting the demand for chatbots. Another way to look at the rise of chatbots is the user migration from Web to apps and now from apps to chatbots.

What are teacherbots? Just like a bot or a chatbot, a teacherbot can be a simple rule-based or smart AI-based. Simple rule-based teacherbots can automate simple teaching tasks whereas a smart AI-based teacherbot can become a teaching assistant (TA). A yet smarter AI-based teacherbot can also be personal tutor.

TEACHERBOTS AT WORK

There are two well known instances of teacherbot pilot projects at the University of Edinburgh in the U.K. and Georgia Tech in the U.S.A. The University of Edinburgh teacherbot project was led by its School of Education in collaboration with the School of Informatics and the Edinburgh College of Art. It was launched in April 2015 by Siân Bayne, professor of Digital Education. “Botty,” as this teacherbot was affectionately called, was created to engage on Twitter with students of Edinburgh’s e-learning and Digital Cultures MOOC on Coursera. This MOOC has had 70,000 enrollments across three course runs. The teacherbot’s primary role was to act like a TA. It could answer simple questions on deadlines, course content, etc. It was also able to answer some complex questions as well, based on AI that had been developed on stored tweets with Twitter hashtag #edcmooc. In “Botty’s” case, the students were aware that a teacherbot and not a human being was answering their questions.

Georgia Tech’s teacherbot was developed by Ashok Goel, a professor of Artificial Intelligence at Georgia Tech. Typically, the 300 students at Georgia Tech’s online AI course posted around 10,000 messages in online forums during a semester. Many of these questions were repetitive in nature. This was enough of a driver for Ashok to initiate work on the teacherbot. Leveraging IBM Watson’s technology platform and a databank of 40,000 questions and answers from past semesters, Ashok developed the smart AI overlay for the teacherbot, calling it “Jill Watson. The students were not told that TA “Jill Watson” was a teacherbot.

“Jill Watson” was launched in Jan 2016. As expected, its responses were not very accurate in the beginning, so responses were moderated by the human TAs before posting in the online forums. But by April, it had become sufficiently “intelligent” to answer the questions without human intervention.

The table on the following page compares the two pilot teacherbot projects.

ELM March A.I. Already Reshaping

Many factors determine the accuracy level of any AI project, including the AI technology layer at the infrastructure level, the size of the database, and the contextualizing smart AI layer. Looking at the above comparison between the two projects, the Georgia Tech project does have an advantage of using IBM Watson as a technology platform and having a database of 40,000 questions and answers from previous courses. No wonder, it performed better.

AI NEXT IN LEARNING?

The potential of AI to disrupt education and skills training sectors is immense. As Microsoft’s Bill Gates remarked sometime back, we already have online tutoring services where humans provide the services while the platform is online. Smart AI-based teacherbots can replace the humans to provide personalized learning. This has special relevance in lifelong learning scenarios where we will be dipping in and out the learning continuum all through our life.

Automated assessments are a natural application of AI in education and skills training. This application gets further amplified when  large number of assessments are being done in an online environment. Use of technologies like Facebook’s facial recognition technology and proctoring are classic examples.

Are we already there in the brave new world where AI is reshaping the way learn? IN these early days, where we are seeing AI-based projects being rolled out in different parts of the world. In the first instance, the focus appears to be on automating routine teaching tasks. This is akin to the Robotic and Process Automation (RPA) implementation onslaught we are seeing in other industries.

“Jill Watson” is estimated to have taken 1500 hours to develop. When many “Jill Watsons” are produced in 15 hours is when we will see real disruption in education and skills training.

Developments in AI in education and skills training will to an extent follow the developments in AI in general. With all major technology innovators investing heavily in AI, it appears certain that our working and learning will get reshaped by AI in future.

—Pradeep Khanna is founder & CEO of Global Mindset (www.globalmindset.com.au) and Technology-enabled Innovations in Learning & Teaching (TILT). He works on enhancing collaborative learning across boundaries and by leveraging technology. Khanna has also been Global Delivery Leader for IBM GBS Australia/New Zealand. He lives in Sydney, Australia, and can be contacted via email at This email address is being protected from spambots. You need JavaScript enabled to view it.

 

Published in Insights

6 STRATEGIES FOR MAKING YOUR TRAINING STICK - BY DEAN PICHEE, CEO, BIZLIBRARY, INC.

THE SCIENCE OF LEARNING

Many professions use science to improve the outcomes of their work. For example, architects use the principles of physics and math to design buildings that will function safely and last decades or even centuries. Architecture is often equated with art, but it's the science behind the art that truly makes it work.

In much the same way, we as learning and HR professionals need to understand and use our knowledge of the science of learning to improve the outcomes of our efforts in training employees. What does science tell us we should do to improve the way employees learn?

Here are six things you can start doing today:

1. Make learning bite-sized. Use short, relevant, video-based training (microlearning) focused on individual concepts.

2. Space training out over time. Employees should use the information they learn during training within the first 24 hours after the training event and in the days and weeks to come. Time is on our side here!

3. Add post-training reinforcement. Use quizzes, polls, videos and other resources to reinforce key concepts.

4. Mix it up. Combine training of multiple related skills rather than focusing on one skill at a time. Scientists call this learning concept interleaving.

5. Make it difficult. Resist the temptation to make training easy for learners. Challenging them actually increases the learning impact. One of the ways to make it more difficult is to increase the amount of time between testing and retrieval opportunities.

6. Write to remember. Your brain will recognize more of what's important when you write what you learn.

WHAT WORKS?

We call microlearning and post-training reinforcement "Burst and Boost Training." Using a combination of "bursts and boosts" is a proven way to get more ROI from your employee training program. Bursts of microlearning have been proven by cognitive psychology to be the most effective way learners receive information. Cognitive load theory states that we have mental "bandwidth" restrictions. The brain can only process a certain amount of information before reaching overload. To improve training content, chunk it down into bite-sized bursts to lower the cognitive load. Microlearning is very popular today and is a key component of BizLibrary's online training solutions.

Boosts, or post-training reinforcement, has been shown to increase long-term memory. Testing can actually INCREASE learning more than any other study method. Scientists call this idea "The Testing Effect," and numerous studies have shown that long-term memory is increased when some of the learning time is devoted to retrieving the to-be-remembered information. Incorporating tests and quizzes into employee training programs is more than just measuring the amount of learning that has taken place ... it's a critical part of the learning itself. Resist the temptation to skip testing and boost learning!

THE GREAT TRAINING ROBBERY

Stop the great training robbery that occurs when we deliver programs that are too long, too boring and easily forgotten. Microlearning is the first step. It's also crucial to add on-going reinforcement. Think of post- training reinforcement as the deadbolt on the door of your house, keeping the valuable information you're delivering to your employees from being forgotten and ultimately, maximizing the ROI of your program.

Published in Insights

Online learning continued to grow ex- ponentially, partially fueled by com- panies like Udemy, Lynda.com and Coursera. With employers more willing to accept that this type of courseware is necessary, we expect other related trends to emerge. The top five learning predictions for this year are:

1. EDUCATION HACKING

The churn in technology advancement - both software and hardware - leaves a lot of traditional educational facilities in a tough spot. Most times, universities and colleges find that their course- ware is being rapidly obsolesced by new advancements that occur in 9-12 month increments.

An example of this rapid obsolescence can be seen with some of the new cloud computing companies. Amazon Web Services boasted that it has over 700 significant changes to their cloud computing infra- structure each year. That means that if you’re going to participate in that arena, you can’t expect to find that content in traditional degree courses.

2. TECHNOLOGY BOOT CAMPS

These are coding boot camps that compress the learning process into weeks instead of semesters. Their popularity has spread quickly with venues like General Assembly, which has opened up campuses throughout the country to meet demand. 

But don’t count the universities out just yet. Many entities are expected to announce their own versions of these technology boot camps, which offer professional courses versus credential courses to their students. The University of Phoenix has launched one such venture called Red Flint, in Las Vegas, Nevada. You can expect to see them increase that capability as they re-tool to be more responsive to current trends.

3. MICRO-CREDENTIALS

These are non-degree courses that offer expertise in niche areas like technology, but also other areas where there is a shortage of talent. These courses cost a fraction of typical education venues and can be stacked to create a customized educational experience, i.e., the “hacked” education venue.

With more employers warming to online certificates, and people changing jobs more often, expect this particular trend to grow exponentially. In an age where there is continuous change, the need for continuous learning is a foregone conclusion.

4. APPRENTICESHIPS

This is expected to be another area where we’ll see greater growth, as evidenced by the agreement between Amazon and the U.S. Department of Labor announced earlier this year. This particular program announced an apprenticeship program to train veterans for tech jobs at Amazon. One of the unique benefits of this type of program is that the veterans can earn a salary while learning the skills needed for the job. We expect other major software and technology companies to follow this trend.

5. BRICKS AND CLICKS

We see this as a more accepted venue as educators in the corporate space focus on the unique job roles that have to be brought up to speed across their enterprises and ecosystems. While it was pretty easy to dump everything into the classroom venue in the past, the huge economies of blending online training with classroom venues will continue to push this trend further.

We expect that the ultimate solution in the next few years will be the enactment of the 20/80 model. That model suggests that 20% of the training will occur in the classroom, while 80% of the training is being provided by a combination of online and embedded learning - the latter of which is training within an application, or like in the Amazon apprenticeship program, right on the warehouse floor where employees can access the training at the point of need.

Published in Ideas

Economists have predicted that a rapid period of innovation follows an economic downturn. We are in that innovation cycle. We once could count on an obsolescence cycle of 24 months (thanks to Moore’s law), which was condensed to six months (the life of a smartphone). Now, we are learning and evolving instantly thanks to A.I. and machine learning.

In 2017’s Annual E-learning User Study conducted by Elearning! magazine, 65.6% of respondents are using machine learning today, and 46.9% are planning to purchase. Artificial intelligence is deployed by 31.8% of respondents with 72.7% planning to deploy over the next 12 months, a 228% compounded annual growth rate (CAGR). Augmented reality and virtual reality are close behind with 68.6% and 67.6% planning to deploy. (See article E-learning User Study.)

These advancements are transforming our practices, ecosystems and knowledge base. In the article titled, “Three Disruptive Macro Trends Shaping Enterprise Technology,” we tapped Gartner and leading learning technologists to share their insights and implications (see article Disruptions in Enterprise Technology). Dr. Shawn Dubravac from Consumer Technology Association also identified five transformational technology trends (see article 5 Transformational Technology Trends). Pradeep Khanna also shares his views on A.I. in learning (see article The AI Effect: Are You Ready), and Joe DiDonato makes five learning predictions for 2017 (see Top 5 Learning Predictions for 2017). All conclude that technology’s rapid evolution is spurring transformation at home and at work.

Nothing is gained without the steadfast commitment by our peers, partners and technologists. Elearning! magazine recognizes 28 Learning! Champions who have made extraordinary contributions to the learning industry. Three professionals earned our Lifetime Achievement Award: Elliot Masie, Kevin Oakes and Joe DiDonato. We are honored to feature all 28 thought leaders, trail blazers, innovators, mentors, and high performers inside (see article 2017 Learning! Champion Awards). You will hear from these champions across the year via articles, conferences, Web seminars and blogs. The 2016 Learning! Champion, Dr. Christopher L. Washington, shares his article titled “The Evolution of E-learning and Learning Analytics” on article The Evolution of E-learning and Learning Analytics  .

Thank you to all the learning professionals, technologists and colleagues who continue to advance learning everywhere.

Let’s keep learning!

—Catherine Upton, Group Publisher

Published in Ideas

Every day the enterprise learning ecosystem becomes more complex making a few questions even more important for learning and development leaders. What is the current state of the training function in your organization large or small? How do you evaluate the effectiveness of your training?

Only 8% of CEO’s in LinkedIn’s 2017 Workplace Learning Report say they can see a measurable impact from their company’s Learning and Development. These CEO’s are getting quantifiable activity data from other business functions, so why not L&D?

Chances are, your learning has now spilled out of the confines of an LMS, and touches a TMS, HRIS etc. You may have many of these systems in your organization along with new 3rd party providers, self-directed learning, or apps and portals available to your learners. You are probably spending L&D budget on micro-learning, self- paced learning, gamification, mobile, and more. Surveying aside, how effective are those new initiatives and training techniques? Are you able to track anything more than completions? Are you even able to track completions?

The first step to providing measurable impact is to baseline the effectiveness of your current training by getting better interaction data wherever learning occurs. You can baseline ALL of your current training across multiple learning technologies and you can start today.

It is relatively easy to get all of your training initiatives reporting better learning activity data in the form of Experience API (xAPI) activity streams to a Learning Record Store (LRS). Think of xAPI as a digital mesh that will get all of your proprietary learning technologies talking in the same analytics language. You can mine xAPI activity streams for patterns and react to them. You can keep your LRS data totally anonymous if you would like. xAPI is also technology agnostic so when you add new technologies or remove technologies within your ecosystem it is non- disruptive to your learning activity reporting. But most importantly, an LRS will provide you the learner activity data for formative and summative evaluation.

BENEFITS OF LRS:

1. Baseline your current training with better evaluation data.

2. Begin to build learner competency and performance profiles.

3.  The proper implementation of xAPI/LRS is the first step toward:

  1. Intelligent/Automated Tutoring
  2. Adaptive Learning
  3. Predictive Analysis
  4. Sustainment and Improvement of Training Systems

 

The path to modern training technology and the future of learning starts with xAPI and the implementation of a Learning Record Store. At Riptide, we have been working and engineering learning technology using xAPI since just after it’s inception. Before it was even called xAPI we were generating activity streams to early versions of our LRS, which is now our Storepoints LRS product. We are on the workgroup that created xAPI 1.0 and we are working with it daily.

Interested in learning more on how a Learning Record Store would work within your unique learning ecosystem? Visit www.RiptideLearning.com and request a free consultation today!

—Nick Washburn is Director of Learning at Riptide Software. Contact him at This email address is being protected from spambots. You need JavaScript enabled to view it.

Published in Ideas

 

The 7th Annual Enterprise Learning! Conference is now accepting submissions for the September event. The theme is ‘Building the High Performance Organization’ and focuses on the strategy, best practices and technologies that drive performance. The event seeks thought leaders and presenters with expertise in learning, talent development, business performance and learning and workplace technologies.

ELC17 is also host to the 2017 Learning! 100 and Learning! Champions Awards honoring the top 100 learning organizations and those making extraordinary contributions to the learning industry. Attendees herald from Amazon Web Services, AT&T, NASCAR, Dept. of Defense, and others.

To submit to ELC17, visit: http://www.2elearning.com/rss2/item/56671-enterprise-learning-conference-2017-call-for-papers

 

Published in Latest News

 

After seeing the many AI-powered toys, consumer products and smartphones at CES this month, it is important to ask about AI's future and security. This week, tech billionaires from LinkedIn and eBay are donating $20 million to the Ethics and Governance of Artificial Intelligence Funds to “protect” society from AI. The Fund will distribute money to researchers working on the tough ethical problems raised by AI.

“There’s an urgency to ensure that AI benefits society and minimizes harm,” said LinkedIn’s founder, Reid Hoffman. “AI decision-making can influence many aspects of our world – education, transportation, healthcare, criminal justice and the economy – yet data and code behind those decisions can be largely invisible.”

Last year, Elon Musk and other technology leaders invested $1 billion in OpenAI.org, a non-profit artificial intelligence research company whose mission is to assure safe and secure use of Artificial Intelligence.  OpenAI’s mission is “to advance digital intelligence in the way that is most likely to benefit humanity as a whole, unconstrained by a need to generate financial return,” according to their website.

Musk has been critical of AI and its potential harm.  “I think we should be very careful about artificial intelligence. If I had to guess at what our biggest existential threat is, it’s probably that. So, we need to be very careful,” said Musk. “I’m increasingly inclined to think that there should be some regulatory oversight, maybe at the national and international level, just to make sure that we don’t do something very foolish.”

Musk will be co-chair of the non-profit with technology venture capitalist Sam Altman, who has backed Reddit. Similar initiatives are being funded by IBM, Google and Apple.

 

Published in Ideas
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