MUKESH KUMAR, CHAIRMAN, IT SUB COMMITTEE, BIHAR INDUSTRIES ASSOCIATION (BIA)

A   CONCEPT   ABOUT e- LEARNING

 

Definition of 'E-learning'

 

A learning system based on formalized teaching but with the help of electronic resources is known as E-learning. While teaching can be based in or out of the classrooms, the use of computers and the Internet forms the major component of E-learning. E-learning can also be termed as a network enabled transfer of skills and knowledge, and the delivery of education is made to a large number of recipients at the same or different times. Earlier, it was not accepted wholeheartedly as it was assumed that this system lacked the human element required in learning.

However, with the rapid progress in technology and the advancement in learning systems, it is now embraced by the masses. The introduction of computers was the basis of this revolution and with the passage of time, as we get hooked to smart phones, tablets, etc, these devices now have an importance place in the classrooms for learning. Books are gradually getting replaced by electronic educational materials like optical discs or pen drives. Knowledge can also be shared via the Internet, which is accessible 24/7, anywhere, anytime.

7 Important Characteristics Of Modern Learners e - Learning Professionals Should Know

 

Modern learners are ambitious. They know how to navigate their way around the virtual terrain, and they thrive in online social environments. But what do we really know about modern learners? More importantly, what are the traits that we should consider when creating online courses for this generation? In this article, you will find 7 important characteristics of modern learners that e - Learning professionals should have in mind when designing e - Learning courses for them.

 Important Characteristics Of Modern Learners 

Every modern learner is unique. Each may come from a different cultural and educational background, but there are also common characteristics that they tend to share. Knowing these characteristics can give e - Learning professionals the opportunity to turn e - Learning courses into customized e - Learning experiences. In this article, I’ll present 7 important characteristics of modern learners e - Learning professionals should have in mind when designing e - Learning courses for that particular audience.

  1. Easily distracted.

Modern learners have a lot on their proverbial plates. They are thinking about any number of things at one given time, making it easy to get distracted when they are trying to complete an online course. As e - Learning professionals, this can present quite a challenge. However, we can get over this trait hurdle by creating e - Learning courses that engage right from the start and hold it all throughout. Ask compelling questions that make them reflect upon the topic. Tell an emotional story that pulls at their heart strings or gets them fascinated about the subject matter. Integrate image and videos that evoke specific feelings, as well as characters with whom they can relate.

 2. Social learners.


Without a doubt, modern learners are more social than any previous generation. They spend a good portion of their day on social media networks, catching up with friends and reaching out to business contacts. In fact, in many respects, social networking has taken the place of face-to-face communication. Modern learners are more likely to post on a Face book page or respond to a tweet than make a call. This means that e - Learning experiences must be collaborative and social. To cater to your modern learning audience, you have to include online group projects and social learning online experiences via project management platforms and social media sites to adapt to the way students will learn.

 3. Crave constant knowledge.

This generation of learners demands information anytime and anywhere. If they want to learn more about a topic, they simply hop on their mobile or tablets and find out everything they need to know to satisfy their curiosity. While previous generations had to venture to the nearest library to expand their knowledge, now knowledge is always at the modern learners’ fingertips. In terms of eLearning, we can quench this constant craving for knowledge by giving them supplemental e - Learning resources that they can explore on their own. We can provide them with tutorials, walkthroughs that help them in their “moment of need”.

 4. Always on-the-go.

Your e - Learning course must be mobile-friendly. Your learners aren’t going to have the time to sit at home on their PCs and complete online modules on a regular basis, which means that you have to give them learning wherever they are. You can achieve this by choosing a Learning Management System that features a responsive design and automatically detects the learner’s device and offers the optimal display. However, you must also ensure that you don’t overload your learners by presenting too much information at once. Make it easily digestible by offering short bursts of information on a regular basis, such as five minute modules that each covers a specific sub-topic.

 Though they thrive in social learning environments, modern learners are also fiercely independent. They aren’t afraid to set out on their own to find the knowledge they need to achieve their goals, even if that means devoting all of their free time to the endeavor. If they aren’t finding the information they are looking for in their online course, then they will simply look elsewhere. They also search for learning experiences that give them control over the process, such as being able to choose when they complete the online modules and the nature of the e - Learning activity. For example, some may gravitate toward multimedia-based online courses while others may prefer interactive scenarios. As a result, e - Learning pros must design e - Learning courses that are customized for each learner by doing thorough audience research and providing many different types of e - Learning activities.

 The simple truth is that we live in a busy world where nobody really has the time or patience to sit through a lengthy e - Learning course. However, modern learners are particularly impatient. They are so used to getting information at a moment’s notice that you have to grab their attention and manage time effectively, or you run the risk of disengaging them. They know that there is an abundance of information just waiting for them on the internet, so they won’t waste their time with an online course that doesn’t cater to their needs.

 One of the most prevalent traits of modern learners is that they are overworked and overwhelmed. Many carry out a wide variety of job responsibilities, making it difficult to balance their home life with their careers. This means that they don’t have a great deal of energy or effort to devote to e Learning. They are also overwhelmed by the abundance of information that is online today. Give them time to take it all in and look for signs that your learners are actually absorbing the key ideas and able to retain them for future use.

While some modern learners may possess atypical characteristics, many will share these core traits. However, it’s always wise to carry out a detailed learner analysis before designing the e - Learning course, just to ensure that the needs of every individual are being considered.

Interested in learning more about how to engage modern learners? Read the article 10 Ways To Shake Up Your Online Classes to really change your student’s thinking by changing the routine way of teaching.

What Is the Purpose of eLearning?

 .E-learning (also called electronic learning) is any type of learning that takes place through or with a computer. E-learning is primarily facilitated through the Internet but can also be accomplished with CD-ROMs and DVDs, streaming audio or video and other media. The purpose of e-learning is to allow people to learn for personal accomplishment or to earn a professional degree, without physically attending a traditional university or academic setting. E-learning can be applied for all levels of schooling from grade school to graduate degrees, and is versatile enough to accommodate all learning styles.

Types

There are a number of types of e-learning that depend on the amount of physical interaction. Entirely online e-learning occurs without any face-to face interaction. Course work and materials are distributed electronically through email, websites, online forums and/or CDs or DVD-ROMs. Combined learning uses a combination of Internet-directed instruction, as well as face-to-face interaction. Most traditional colleges and universities use combined learning as students learn in physical classrooms, with instruction augmented by online lessons. For those learning for personal accomplishment, e-learning can also use a combination of e-learning types, as they can be entirely self-directed, or they can use the assistance of an expert in their selected field.

Location

Because the only requirements for e-learning (in most cases) requires a computer with Internet access or a CD/DVD-ROM drive, e-learning students can learn from home, libraries, Internet cafes or any other location that has Internet access. This is why e-learning is a preferred option for those who work full time or part time and cannot afford to travel to a physical school. The ease of location with e-learning also makes it preferable to stay-at-home parents with young or special-needs children. E-learning can also save money in that e-learners do not have to pay for gas, vehicle repair or parking validation if they choose to remain at home while taking courses online. Ease of location is also beneficial to students who are home-schooled, as they can gain social interaction through online communications as well as educational materials.

Pace

Another benefit to e-learning is that it is self-directed, meaning that the e-learner sets the pace of her learning. Unlike traditional methods of education, e-learning allows a student to pace her educational needs with her comfort level. This is especially true when a person chooses to use e-learning for personal accomplishment. For those seeking a professional degree, there may be requirements and course work that must be completed by a certain time, but these deadlines are usually more flexible than the requirements made at traditional learning institutions.

Drawbacks

The most controversial aspect of e-learning is for those who are seeking professional degrees. Some online learning institutions may not meet the academic standards set forth by private accreditation organizations. Accreditation standards are used to confirm a learning institution’s academic quality. This does not mean that an online institution that is not accredited has poor academic quality, but it can cause problems when an e-learner seeks to transfer credits from an online institution to a traditional educational facility. Another drawback to e-learning is that it can be difficult for individuals who are not primarily self-motivated. While self-directed learning has been appropriately praised for its versatility, it can be a trap for those who need the external motivations physical classrooms and instructors can provide.

Cost

E-learning has also been praised as a lower-cost option for those seeking additional education. Costs for individual classes and learning institutions vary depending on location, degree or type of program, and area of study. Online universities generally charge the same way traditional universities do, by charging per credit hour. But also like traditional universities, scholarships, grants, and student loans are available as well.

Building An Ongoing Learning Culture At Discovery

"Looop helps us to deliver everyday learning content to targeted audiences through an easy-to-use mobile-friendly platform. Looop is a key part of our future learning strategy".

 

How To Build An Ongoing Learning Culture—Discovery’s Challenge

This is what Jennifer Wrigley, the Learning and Development Manager in Discovery Communications has stated when asked about Looop. So, let's take a look at what Discovery's challenge was:

Engaging Learners with Fresh, Easy-To-Access Content

Home to more than 50 beloved network entertainment brands, reaching over 480 million viewers worldwide, Discovery Communications delivers some of the most captivating content on the air.

The company’s teams and assets have grown rapidly over the years, and staying agile has been critical to their continued success. But as Learning and Development Manager Jennifer Wrigley has experienced firsthand, keeping learning current for thousands of geographically dispersed employees is no easy task:

"We are a massively changing and growing company operating in really diverse markets. Learning and Development is important, but it’s difficult to do consistently and well across all those markets.

An internal audit highlighted that the company’s existing Learning Management System wasn’t meeting the needs. In particular, online learning engagement rates were low, and although Discovery’s LMS had thousands of resources available, learners found it difficult at times to surface the information they needed:

"Users were accessing the LMS once or twice a year. That’s not really learning, that’s just a reference tool. There is a lot of content on the LMS, but it’s hard to find what you need. For example, when there are 20 different articles on ‘How to be a Great Coach’, most people don’t know where to start".

Discovery also needed a means of keeping content current and relevant for international learners:

"On the LMS, some of the content is quite old, it’s not Discovery-specific and is Americanized in style, which isn’t as suitable for our diverse international audience".

As a result, the learning experience was a far cry from the engaging content Discovery brings excited viewers every day.

"We’re a media company. We need learning that looks good, works smoothly, and is fun and interesting".

Jennifer and her team needed to find a way to bring relevant content to learners on demand, anytime, anywhere—without rifling through a large database to find it.

Solutions

Targeted Resources And A Simple, Searchable System

Upon finding Looop, Jennifer was immediately drawn to their fresh, learner-focused approach. Her initial conversations with Looop’s team were also very encouraging:

"Looop was very keen to support us. David (Looop’s Chief Learning Strategist) is the ex-Director of Learning, Talent, and OD at Disney, so he gets it. He understands the challenges, and everything in terms of learning, development, and people making time for it".

Jennifer decided to use Discovery’s Global Mentorship Program as a pilot project:

"Once a year people apply to the program, and we match them with a mentor for 6 months. We thought this would be an ideal format to test Looop’s platform and approach to learning".

First, Jennifer worked with Looop to create relevant, targeted content for this unique set of learners, and was surprised at how involved Looop’s people were willing to be:

"We created our own content and combined this with external content to try and inspire both mentors and mantes, David even helped us with filming and editing our employees to create short videos for our learners. These videos were just filmed on an iPhone—we kept it really simple—but they are very engaging and effective".

To help users get plugged into Looop and excited about the system, an email campaign was created to promote specific resources.

After a few weeks, engagement had already surpassed Jennifer’s expectations:

"We found that the majority of our learners were exploring more content within Looop, not just the resources we promoted. Users were writing comments and creating conversations inside the platform—something we’d never had before".

Throughout the Global Mentorship Program, Jennifer appreciated how Loop’s team remained involved, interested, and responsive:

"Looop really cares about their product and their clients. They will spend time with me, helping me make resources more engaging and relevant. They listen to our feedback and implement our requests. They’re really invested in our relationship and us being successful".

Results

Increased Engagement, Decreased Cost, And Inspired Learners

It quickly became clear that Looop attained much higher levels of engagement than the previous LMS:

"Within a month, over 45% of our users had already accessed the system and the majority were accessing our learning content multiple times and reading multiple articles".

For the first time, learners were proactively accessing content not only because they needed to, but because they wanted to:

"We got feedback that those using it really enjoyed it. People told us that Looop was a huge improvement from the previous site, that it was easy to use and that the content was relevant".

Better yet, now that the content is tailored and relevant, learners are actively contributing and taking ownership over their own development:

"In the feedback, learners have even told us which content they wished they had, and we’ve been able to turn around and create it for them".

Looop also had a positive impact on Discovery’s bottom line:

"Looop is much cheaper than our old solution. With the old LMS we were paying for content that nobody wanted. But with Looop, we’re paying for resources people want and actually use, so it’s the right investment".

On the strength of the mentorship program’s success, Jennifer and her team are now creating tailored learning content for other groups within the company:

"We now have several other programs on the go for groups like people managers and our director".

As for the future, Jennifer is excited about the way her team is transforming Discovery’s learning culture into a learner-focused, everyday experience:

"We’ve taken steps forward on our journey to help people see that learning isn’t just about workshops, but that you can learn through your phone everyday by finding relevant content, articles, videos, and tips".

Jennifer is confident she’s found the right tool—and the right people—to come along for the journey:

"Looop is targeted learning that’s easy to access in an engaging format. It’s mobile friendly, it’s modern, and completely customisable by you".

Find out how Looop can help your business to achieve exceptional results through Learning and Development by speaking with our team of experts.

 

Machine Learning And Artificial Intelligence: The Future Of eLearning

 

That does the future hold for predictive analytics and iterative automation in eLearning? In this article, I’ll discuss the many advantages of Machine Learning and Artificial Intelligence. I’ll also explore how these tech-cetered strategies will transform the e - Learning industry as we know it.

 

 

The Role Of Machine Learning And Artificial Intelligence In The Future Of eLearning

It’s an exciting time in the world of eLearning. Technology is constantly evolving and adapting to boost everyday efficiency and make our lives easier. Modern tools give us the power to connect from around the globe and bridge gaps as soon as they appear. One such advancement is the rise of Machine Learning and Artificial Intelligence. Namely, their role in the future of eLearning. Predictions, algorithms, and analytics come together to create more personalized e - Learning experiences. But how exactly will Machine Learning and Artificial intelligence (AI) transform the e - Learning landscape in years to come? And how can you start preparing for monumental changes today?

What Is Machine Learning And Artificial Intelligence (AI)?

Before we dive into the forecast of Machine Learning and Artificial Intelligence (AI) in eLearning, let’s cover the basics of these tech-based approaches. First things first: Machine Learning is a sub-division of Artificial Intelligence. It involves algorithms that predict possible outcomes based on user data. The system identifies certain patterns and trends, then learns from the data in order to provide greater personalization. Every piece of new information that the program receives makes it more intuitive. The entire process takes place autonomously, from extracting and evaluating the data sets from the LMS to predicting what online learners need based on their past performance.

Today, there are two different types of Machine Learning frameworks: proprietary and open source. Both fall into the category of deep learning software. There are also various tools or hardware involved, ranging from tensor processing units developed by Google to vision processors that enable machine vision operations. In addition, you can already find a vast assortment of Machine Learning libraries, complete with algorithms that support specific programming languages

Machine Learning Classifications

Machine Learning includes algorithms that allow the system to predict future outcomes and detected patterns based on specific user data. Here are the 3 common algorithm classifications that are used in Machine Learning:

1. Supervised

The system uses past examples and new data sets to predict the outcomes. In this instance, the programmer must provide the system with inputs and outputs in order to train the software. Over time, the system can automatically construct outputs or targets for new data sets.

2. Unsupervised

Does not involve any labels or data classifications. The system evaluates data in order to identify patterns and make inferences or predictions. It’s not a matter of mapping the input to an output, but detecting more obscure trends or insights in the data set.  There is also a sub-set category known as “semi-supervised”, which combines unlabeled data and human-based training. For example, the programmer provides the system with labeled online resources in order to map out certain inputs and outputs with greater accuracy.

3. Reinforcement

This Machine Learning category includes a specific task or goal that the system must complete. Throughout the process, it receives feedback in order to learn the desired behaviors. For example, the system encounters an error while performing the action or a reward for achieving the most favorable outcome. Thus, the program is able to learn the most effective approach via “reinforcement signals”.

The Benefits Of Machine Learning And Artificial Intelligence (AI) In eLearning

There are a variety of benefits that Machine Learning and Artificial Intelligence (AI) can offer online learners of the future, as well as organizations who invest in modern LMS platforms that feature intuitive algorithms and automated delivery of e - Learning content. Here are just a few of the most notable advantages:

1. More Personalized e - Learning Content

Machine learning algorithms predict outcomes, which allow you to provide specific e - Learning content based on past performance and individual learning goals. For example, an online learner’s history reveals that they prefer tactile e - Learning activities. Thus, the system automatically adjusts their e - Learning course map to include more serious games and e - Learning simulations that are kinesthetic by nature. Likewise, online learners who exhibit a particular skill gap will receive targeted recommendations that build related talents and abilities. Thus, they are able to gradually gather the building blocks that the skill set requires. The system also delivers the e - Learning content in a more personalized format. For instance, it may skip several e - Learning modules for more advanced online learners or take a more comprehensive, linear approach for those who still lack basic knowledge.

2. Better Resource Allocation

There are actually two benefits relating to resource allocation. The first is that online learners receive the exact online resources they require to fill gaps and achieve their learning goals. In the corporate sector, this equates to less seat time and training payroll hours. Employees get the information they need more rapidly, as every online training resource is custom-tailored to their personal objectives. The second benefit is better resource allocation for your L&D team. They can spend less time analyzing graphs and LMS metrics, and more time developing powerful e - Learning content. The system takes care of the Big Data and allows your L&D team to spend their time and energy elsewhere.

3. Automate The Scheduling And Content Delivery Process

Many Machine Learning tasks involve behind the scenes work that are tedious and time-consuming, but crucial nonetheless. For example, scheduling coursework for online learners or delivering online resources based on their e - Learning assessment results or simulation performance. Artificial Intelligence (AI) may be able to take over these operations in the near future, making it possible to automatically generate unique e - Learning course maps for every online learner who enrolls in your e - Learning course. They can also readjust their e - Learning course immediately whenever the need arises.

4. Improve e - Learning ROI

Less online training time and greater personalization translates into a broader profit margin. You spend less on online training without sacrificing the desired outcomes thanks to predictive analytics and AI-equipped software that can track and forecast every move on an online learner. This also gives you the power to deploy your online training resources where and when they’re required. For example, more effective data gleaned from Machine Learning algorithms reveals hidden online training gaps. In response, you can funnel online training resources to address the inefficiencies and omit other areas of the online training program that are no longer relevant, instead of devoting online training resources to maintaining assets that aren’t resonating with online learners or supporting current objectives.

5. Improve Learner Motivation

Online learners receive an individualized experience instead of a generic e - Learning course that touches on irrelevant topics. Therefore, they don’t have to dedicate as much time to the online training process, but still accomplish their goals and build vital skills. This gives them the added motivation they need to engage with the e - Learning content and reach their potential. They are also able to go at their own pace and participate in e - Learning activities that resonate with them. The Machine Learning systems of the future can be likened to a private virtual tutor, offering them the coursework they need just when they need it.

6. Create More Effective Online Training Programs

These benefits result in more effective online training programs that take all factors into consideration, instead of only acting on one piece of criteria. For example, online assessment results or survey findings. The Machine Learning system offers a comprehensive overview of Big Data and uses it to predict the outcome. Therefore, you can intervene before it’s too late and offer every corporate learner new personalized online training opportunities. You can even use AI to make peer-to-peer interactions more productive. For instance, match mentors to online learners who can benefit from their specific skills or past experiences.

How Machine Learning And Artificial Intelligence Are Transforming The e - Learning Landscape

What if you could create e - Learning content and then let the system take care of the more tedious tasks, such as reviewing charts and statistics to detect hidden patterns? What if you could provide immediate personalized e - Learning feedback and steer online learners in the right direction without any human intervention? Machine Learning and Artificial Intelligence have the potential to automate the behind-the-scenes work that requires a significant amount of time and resources. In the future, AI can help you develop and deploy more meaningful e - Learning experiences that bridge undisclosed gaps.

The system will be able to predict every eventuality and desired outcome in a matter of seconds. Then deliver e - Learning content that caters to online learners’ individual needs, preferences, goals, and areas for improvement. It takes intuitive e - Learning design to a whole new level, as the system knows what an online learner requires even before they do. As such, you have the power to custom-tailor every aspect of the e - Learning program based on an online learner’s past performance, job description, and learning preferences.

4 Tips To Prepare For The Machine Learning Revolution

A complete Terminator-esque AI takeover is still a ways off. However, you can start preparing for Machine Learning integration today with these simple tips:

1. Research Available Tech Tools

A good place to start is to researching current LMS platforms and e - Learning tech tools to get a sense of modern Machine Learning integrations. For example, some e - Learning software already has algorithms and automation features built in. You can also evaluate the LMS your organization is using to gauge its tech limitations. Then look for add-ons or third-party software that can help you optimize its efficiency. Consider attending trade shows or conferences to get the latest scoop on Machine Learning tools and applications.

2. Collect Current Big Data

You don’t want to wait to collect data until Machine Learning and Artificial Intelligence becomes a full-fledged reality. In fact, you should already be collecting Big Data from all available sources, even if you aren’t currently using it in your online training strategy. There’s no way to tell which data will be useful when it’s time to incorporate algorithms and predictive analytics. Machine Learning systems require the complete picture, not just a snapshot of the last few days or weeks. Compile and organize data from your LMS, website, and social media pages, in addition to survey results and on-the-job observations. Store it safely for later use after you’ve identified the patterns and trends that are relevant for today’s online training content.

3. Be Realistic About Machine Learning’s Role In Your Online Training Strategy

It’s important to bear in mind that Machine Learning isn’t going to be the ultimate solution. Though it will probably be a powerful tool to maximize the power of Big Data, some degree of human interaction will still be required. At least until the robots fully takeover and we’re all able to ride off into the sunset in our flying cars. You should be realistic about how much will actually be automated by the system and the role that AI will play in your online training strategy. Identify your objectives and evaluate the current tasks your employees perform in order to maintain the system and evaluate data sets. Then figure out which operations can be managed by Machine Learning algorithms in the future.

4. Develop A Game Plan To Get A Head Start

It’s nearly impossible to create an exact timeline for when you’ll fully incorporate Machine Learning into your online training strategy. However, you can develop a rough game plan to stay one step ahead. For example, create an outline of the desired outcomes in order to identify Machine Learning applications in your organization, such as how it will help you reduce employee turnover or manage HR operations more effectively. You might also consider taking some courses in programming or Machine Learning algorithms, or reach out to experts in the field who can offer their assistance when the time comes.

Machine Learning and Artificial Intelligence are sure to play a prominent role in the future of eLearning. Especially given the numerous benefits they can bring to individual online learners and organizations. The secret is to stay one step ahead of the tech trends and evaluate your current e - Learning strategy to forecast AI applications. How can Machine Learning help you deliver learner-centered e - Learning content and streamline the data analysis process? What can you do to make way for the rise of Machine Learning and benefit from Big Data now?

There’s still some time before Machine Learning and Artificial Intelligence take over the analytics process entirely. Read the article 5 Types Of Big Data To Extract From Your LMS And How To Use It to discover the types of Big Data you can extract from your LMS today, and how to put it to good use to improve your e - Learning course design.

Machine Learning

A great selection of e - Learning articles for Machine Learning. Information, benefits, disadvantages, technologies and valuable resources for Machine Learning. Find out everything you need about Machine Learning into e - Learning Industry database.

 

 Augmented Reality In Aviation: Changing The Face Of The Sector Through Training And Simulated Experience

 

The successful adoption of Augmented Reality (AR) has come a long way the past few years, particularly as the global economy has grappled and the need to improve operational efficiency and safety training has increased.

 

Artificial Intelligence: Preparing Today For An Efficient Workforce Of Tomorrow

 

AI Chat bots In e-Learning: Trends Embracing Across Digital Landscape

  

How To Forecast, Identify And Incorporate 7 SQL Trends In 2018

 

Machine Learning Applications In Corporate eLearning

 

 

How ‘Deep’ Is Deep Learning?

 

Machine Learning And Artificial Intelligence: The Future Of eLearning

 

Survey Results - Impact Of AI/Machine Learning On Workforce Capability

 

 

Machine Learning Process And Scenarios

 

 ntroduction To Machine Learning

We’ll try to answer all the basic questions related to machine learning in this and the following articles; know what it is and what could be achieved with it.

 

This Is How Artificial Intelligence Will Shape e - Learning For Good


 
Artificial Intelligence (AI) promises to impact our future and shape its development in many ways. But what is AI, how is it different from Machine Learning and, more importantly, how Artificial Intelligence will shape eLearning?

How Artificial Intelligence Will Shape eLearning?

In an age where everything is changing –and changing fast– it’s easy to forget how much we’ve progressed. While we may not have floating cars or robotic teachers, we are on the brink of some very exciting and dramatic developments across all industries. As one of the principal drivers of progression, it’s no surprise that learning –and education in general– has been a focus of technological advances. While e - Learning is not a new concept, its popularity is increasing, especially as technology becomes more affordable. A big barrier for e - Learning is the cost of developing content. According to a survey conducted by Chapman Alliance, developing one hour of e - Learning content can take anything from 49 to 125 hours. In comparison to the 22 to 82 hours that it takes for instructor-led training (ILT), it is easy to see how the costs stack up. Even though e - Learning wins in the long run due to its scalability, it can still pose a barrier to companies that can’t afford the initial investment. Developing ways to repurpose existing content can mitigate the expense of content development, and recent advances in Artificial Intelligence (AI) could be the silver bullet that’s needed. Here is how Artificial Intelligence will shape e - Learning in the future.

Artificial Intelligence Doesn’t Equal Machine Learning

There is quite a lot of confusion about the difference between AI and machine learning. While many big companies use them interchangeably, they are not the same thing. Related, sure, but different.

Google defines AI as “the theory and development of computer systems able to perform tasks normally requiring human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages”.

Think of it like this: If a computer gathers information about birds and refines it over time, it would be considered machine learning. If that computer then categorized those birds based on that information, it could be said that the computer used AI in order to achieve that task. That is, the computer used AI to categorize the birds based on the refined information that it gathered during machine learning. Machine learning is to Artificial Intelligence what books are to education.

Machine learning is a system where a computer can learn without being explicitly programmed – it has dynamic parameters whereas, up until a couple of years ago, the sum total of AI was a set of static parameters cleverly pre-programmed by a developer.

Machine learning can be separated into 3 categories:

1. Supervised Learning.

Machines are given data that is well-labeled and tagged with the correct answer. They’ll process it and once training is complete, apply it to unseen data. Accuracy is directly proportional to the size of the data set.

Example: Labeled data about birds, where the machine gathers information about each avian individual.

2. Unsupervised Learning.

Machines are given specific data (that is, in a particular category) but it isn’t labeled.

Example: Data about birds, but no additional information.

3. Reinforcement Learning.

Machines are given unlabeled data that is graded after processing. This means that the computer is told how accurate the output is, so it can reinforce the decisions it made along the way. This method requires many data sets in order to become accurate.

Example: A game of chess. The machine has no preprogrammed moves (apart from the game rules) but the output is graded, so it knows whether it won or lost. If it won, it can then go and reinforce the decisions it made.

Deep Learning And Artificial Neural Networks Explained

As we progress in the field of AI, new techniques are being developed to improve the effectiveness of machine learning, constantly pushing us towards true autonomy. One such technique is an Artificial Neural Network. This is quite a popular method as it has opened the door to deep learning as well as making the applications of AI far-reaching and meaningful.

An Artificial Neural Network is a technique applied to machine learning. It’s composed of a network of nodes or neurons, loosely resembling the human brain and neural system.

Data is sent to the input layer (A) which manipulates data and then passes it on to the second layer (B & C). These nodes then further manipulate the data and pass it on to the output layer (D).

Each node manipulates the data based on weights that are adjusted through training processes.

On the other hand, deep learning  is more intricate as it consists of multiple hidden layers and produces a far more complex network called a “deep neural network”. This is where AI starts getting very interesting. Deep learning is the jump from telling computers what to do to giving them examples of what to do and letting them figure out how to apply it to other situations; the jump from predefined steps to predefined models.

Deep Learning has pushed the field of AI forward recently, producing incredible results in areas like speech and image recognition. Where most machine learning methodologies try to model the world, deep learning attempts to model the human brain in order to create and maintain its own representations of the world.

What Classification Could mean For e-Learning

One area that is being vastly improved by deep learning is classification. Training a machine to be able to recognize data and classify it accurately has multiple useful applications: Image categorization, translation, and caption generation to name a few. Classification is particularly beneficial for eLearning. Using machines to help classify and organize content, would greatly reduce the cost of content development.

Classifying information accurately in terms of subject matter is extremely beneficial as it minimizes the resources needed to repurpose content. However, without automation, it can be an extremely tedious and time-consuming task.

Deep learning promises to solve this challenge. More importantly, however, it has the potential to improve classification and instructional design altogether. Identifying related concepts across subjects is powerful and would not only reduce the resources required to repurpose content, but also allow machines to produce new content in subjects that are not initially developed.

For example, while subjects like Physics and Optometry are completely different subjects, there are concepts within the material that would overlap – light for example. Sifting through all of the content in each subject to identify related concepts would take many man hours and subject knowledge, yet with deep learning, machines have the potential to perform that task quickly and efficiently.

Suddenly, thanks to machine learning and classification in particular, the applications of AI in e - Learning become extremely tangible and have the potential to reduce the cost of content development drastically.

Applying Adaptive Learning Environments

The application of AI to e - Learning content is not just a cost-saving solution; it also opens up a whole new way of looking at learning itself.

People learn in different ways and at different paces, so one of the major challenges in classrooms is maintaining a balance between engaging the quicker learners and accommodating the slower ones. In this type of situation, adaptive learning environments can allow for a completely individualized pace.

This includes environments that can accommodate individual learning styles and can run in parallel to each other. Thus, creating a far more effective learning environment, and increasing the chances of a group of individuals assimilating information accurately over a set period of time.

Apart from the quality of learning, AI presents an extremely valuable solution for training in industries with a high rate of dynamism. Companies that need to update their course material on a continuous basis will benefit from adaptive learning environments once machines have the ability to accurately predict how course material needs to improve and change.

Intelligent learning environments can also analyze data across all personalized training instances, to recommend improvements and highlight inefficiencies that would not be possible otherwise. Of course, there is the benefit of using AI for translating content into other languages - this alone could save industries millions every year.

Once a machine has developed the ability to create new content, personalization of learning will exponentially improve. Adaptive learning technologies would give rise to completely personalized environments with content that not only changes but are actually created based on the individual needs of the learner. So, as you can see this technological advancement would vastly improve the quality of education that the learner receives in various ways.

Ethical Implications Of Artificial Intelligence

One point of contention with deep learning, and AI in general, is that of responsibility. With traditional training environments, the trainer is responsible for the information being relayed to learners and assumes the responsibility to ensure that it is accurate. If not, they are liable.

However, with AI the creators of the algorithms are not the creators of the content their algorithms produce. This presents a massive problem if something goes wrong; machines cannot be held accountable in the same way that humans can.

That being said, in a discussion at the Disrupt London tech conference, Google’s DeepMind CEO, Mustafa Suleyman, spoke about the responsibility designers and technologists have to think consciously when building these systems. He mentioned that the creators could unwillingly introduce their biases into the systems being built – without realizing it.

In March Microsoft released a Twitter chatbot as an experiment. It learned as people tweeted to it. In less than 24 hours, it went from tweets like “humans are cool” to “Hitler was right” showing the impact that our own flaws can have with AI systems.

Holding the creators of algorithms liable is not technically fair though. The systems learn from the data being processed, not from the algorithms themselves. And in verticals where safety and compliance is non-negotiable, such as a learning environment, this could present a radical problem.

It is up to the people managing the AI systems to ensure that the data being processed is fair and accurate. Just like a teacher’s responsibility to teach with accurate material.

The benefits of deep learning and its application to e - Learning are undeniable. Both the quality of learning as well as the cost of it is bound to be disrupted at a fundamental level. But there are potential consequences that are difficult to predict, and therefore to address.

Improvements in learning and information transferal are exponentially improving human progression, and like our predecessors, we need to take risks for the sake of our future.

17 Essential Steps In The Software Upgrade Process

 

Many organizations are facing upgrades to their learning management software as a result of GDP Regulations. This article outlines the most important steps and considerations in the upgrade process.

 

Software Upgrade Process: 17 Steps To Follow

With the General Data Protection Regulations (GDPR) coming into force across EU countries on 25 May 2018, many software suppliers are releasing system updates in order to comply with these requirements.

The complexity, time, cost, and resources required to upgrade your software will depend on a variety of factors including whether your site has been customized—either via plugins, with changes to the core system, or by an integration with another system.

There are, however, a set of steps and considerations an organization would typically undertake to perform a successful LMS upgrade.

1. Identify and Engage Your Stakeholders

Remember to include both internal and external stakeholders. End users, trainers, content developers, administrators, support and hosting teams, QA and testing teams, system owners, external suppliers, and service partners. Work out how each group interacts with the system, how an upgrade will affect them, and what level of ongoing engagement they might require.

2. Undertake Change Control Planning

Besides the technical work involved in upgrading your system, effective change control will probably be the biggest challenge surrounding a software upgrade. Keeping the balance between adequate communication and consultation, and overwhelming stakeholders with detail can be difficult. Consider assigning a dedicated Change Control Manager to the process and include developing a communication plan and (at a minimum) a high-level project plan as deliverables for that role.

3. Perform A Site Review

Performing a site review involves taking a stock take of any and all customizations within the site. Identify all bespoke developments and dig out the specifications and other relevant documentation relating to this work. Assess each development for upgrade or reimplementation. Is this customization in the core system or has it been developed as a plugin? Is this feature (or something very similar) now available in the new software version? Is the current development or the business need for this feature still applicable? Is your software integrated with any other systems or applications?

4. Establish Upgrade Requirements

Based on your site review, do you need to re-develop customizations and integrations or upgrade bespoke plugins? Do you need to make allowance for historical data from those customizations? Can you upgrade to the latest version of the software from your current version or do you need to upgrade to a more recent version first? Check the technical specifications of the new software—do you need to upgrade, install, or purchase anything as part of the upgrade? Do you also need (or want) to upgrade your theme/design or will it work on the upgraded version?

5. Make An Upgrade Plan

Who will perform the upgrade? What needs to be upgraded? What needs to be redeveloped? Does anything need to be newly developed? How will the upgrade be performed? Is there any guidance on the upgrade from the software supplier? What time, people, resources, and budget do you need to apply to the upgrade process?

6. Make A Roll-Out Plan

When will the upgrade be performed? Will there be an outage? How long will it be? What sort of message needs to go out to stakeholders? What will be the implementation process? What is your rollback plan?

7. Backup Everything

Store that backup somewhere very safe and secure. Backup again, and store that file somewhere else safe and secure.

8. Run A Trial Upgrade

The best way to know if any issues will crop up during the upgrade is to try upgrading a copy of the live site. It will identify any code and/or database conflicts and where further work will be required. It’s important to allow plenty of time for this step and to engage with relevant stakeholders.

9. Upgrade A Staging Site

Once the technical aspect of the upgrade has been completed and any issues resolved, it’s time to upgrade a copy of the live site to a staging or testing environment. Remember to switch off any email processes so users aren’t messaged as part of any testing and make sure the staging site is only available to permitted users.

10. Perform Testing

This includes functional, non-functional and technical testing, quality assurance, data integrity, security, performance, and theme/design testing including browser, and device testing. Vital to the testing process is a shared mechanism for capturing and tracking issues. Issues should be described in detail, have a unique identifier for tracking purposes, have a current status, and be allocated to someone until fully resolved. It is also important to allow time for, and manage expectations around, user acceptance testing. While an upgraded system might be functional, it might not fulfill all of the business requirements it was implemented to address.

11. Undertake Configuration

With your freshly upgraded staging site now deployed, tested and issues resolved, site administrators will need some time to ensure any new features and functionality introduced by the updates are configured correctly. This might involve turning on or off features or updating settings to best accommodate your users.

12. Prepare Documentation

Preparing help files or user manuals, as well as technical documentation detailing any custom developments or deployment requirements is vital to the success of your current upgrade as well as any future upgrades. Leverage any documentation provided by the software supplier or your technology vendor and highlight the differences between versions to emphasize important changes in the new release.

13. Provide Training

Training the right people at the right time not only requires adequate training resources but adequate planning. Trainers and the trainees need to be available at the same time, in a suitable space. The amount of training, the type of training, and the length of training will depend on the end-user facing functionality and feature changes within your upgraded software. For some helpful hints on selecting a training provider.

14. Prepare Your Infrastructure

If your upgrade introduces a number of significant changes or you are timing the software upgrade with a re-release of the system, you will need to ensure your hosting setup will be able to cope with an increase in access and usage—if only for a short period of time.

15. Go Live

You have a few options around your go-live including a complete switchover or running the old system and updated system in parallel for a period of time. Refer back to your rollout plan to make sure everyone knows their role and responsibilities for the release.

16. Offer Support

There will probably be an initial demand on support resources after an upgrade has been released as users become acquainted with the new system. Changes to ongoing support might be required as a result of new system features and functionality or changes to processes and policies. Ensure the support team has enough documentation, training, and coverage to meet demand going into the upgrade release. For some helpful hints on selecting a supporting vendor.

17. Perform An Upgrade Review

It is important to review the upgrade project in retrospect to identify what worked well and where improvements can be made for the next time an upgrade is required.

 

2018 Web Design Trends For Better User Experience

 

2017 was mostly about web animations, design sprints, conversational interfaces, micro-interactions, bespoke illustrations, and chat bots. But many trends like floating navigation menus, inventive use of colors, bold typography, biometric authentication, etc. are emerging into the list this year. In this article, I am going to discuss some key features that ensure better User Experience in 2018.

Follow These 2018 Web Design Trends For A Better User Experience

Cohesive experiences were predicted in 2017 and became a necessity soon. Many other web design trends originated several months ago and are now improving and penetrating the mainstream.

In today’s creative and advanced World Wide Web, users expect innovative and sophisticated designs.  The pioneering UX consultancy ‘Clear left’ co-founder, Andy Budd thinks the subtle shift to digital service design from UX design that is going to grow. And as a result, we are going to experience more innovations in the field of digital design this year.

So, according to the future predictions of leading designers and strategists, web designing is evolving every single day with distinct developments. It is necessary for us to aware of and stays ahead of the curve. Let's have a look at the following:

1. Creative Layouts

The release of CSS Grid in 2017 is a milestone in web design. With this, the designers will have a level of control over the rendering of elements on a webpage for the first time. Over the past 20 years, the evolution and maturation of CSS allow the users to use the properties like feature queries and enables to present a different design based on the specific features of particular user browser supports. Nearly 80% of people accessing the web use a browser that supports CSS Grid. Other specific-layout properties like CSS Shapes, Flex box, and Writing-mode also become even more popular. Users wish to see more creative and productive layouts that utilize concepts of Graphic Design including vertical whitespace and overlap, yet still dynamically feasible layout on older browsers.

2. Variable Fonts

Variable fonts enable the possibility to tailor the width of the character for various screen sizes and languages, to provide better text grade adjustment both foreground and background contrast.
Better line length for users with low-light scenarios and with optical character size represented at smaller sizes for better usability.

3. Artificial Intelligence

Voice User Interfaces and chat bots (conversational UIs) were trending in 2017. Many of these interfaces aren’t driven by Artificial Intelligence (AI). There are a lot of articles, case studies, and talks on the intersection between AI and UX design, and are the most notable intersection in the field of robotics and finally, the UI is headless. An expert AI bot is not entirely feasible and exists distinct milestones ahead. It is an agent-assisted one follows a viable approach. It can handle all kinds of user requests to a greater extent that the user cannot identify whether they are talking to a human or not. It tries to fulfill the claims of the users, and in case it doesn’t know how to handle that, a human has to take over. These chat bots which are powered by AI not only replace a human being—but for the need to sleep—, but they are also time-saving, plus they offer solutions. With all these benefits, they stood, and still do stand at the top in the world of web design.

4. Ethical Designs

Inclusive and sustainable designs indicate broader design movements. The ethics are becoming more and more critical and are often designed to interrupt, encourage digital addiction, and distract quite dramatic consequences. The field experts to foster the adoption of web designing have created a framework called Ethical Design. This conceptual framework allows designers and their teams to develop systems, products, and services that do not harm human situations. They extend to the users and other living things in the way that is involved in the lifecycle of the system, product, and service.

5. Brand Experience

A website is the first medium of a brand to interact with the customer and is an extension of the brand to attract customers. Implementing new trends is an excellent source for creating inspiration and for generating a fresh experience and best ways to connect with the customers. Developing a website seamless brand experience of a website consumes a lot of time. A compelling web brand experience must include uniform narrative and well-targeted woven across each customer touch point. There is no sure-shot strategy to building and communicating a brand experience for better success rates. Understanding and using a transparent process helps him to develop an immense amount of clarity and direction.

6. The Power Of Storytelling

The primary way to absorb, store, manage, access, and communicate information as well as to connect with others is through stories. It is a fact that our brains respond to content just by looking for the story that perfectly explains the situation and helps to create mental models, narratives, and cognitive maps. For any website and digital media, storytelling isn’t about only content and can be delivered on multiple levels to communicate efficiently. This must be leveraging all web designing aspects and blend them carefully into a beautifully recounted comprehensive package.

Conclusion

The crucial thing to consider is that advanced web design not only focused on websites but accessed on desktop or mobile. The web designers have to see on a broader angle and make decisions by following the evolving trends. The techniques should be applied based on how they affect the brand experience on he whole.

 

Each observation was taken at a time to re adjust the weights. Same way we will make predictions for the future data points.

If you like what you just read & want to continue your analytics learning\

Signal Processing and Machine Learning Techniques for Sensor Data Analytics

An increasing number of applications require the joint use of signal processing and machine learning techniques on time series and sensor data. MATLAB can accelerate the development of data analytics and sensor processing systems by providing a full range of modeling and design capabilities within a single environment.
In this webinar we present an example of a classification system able to identify the physical activity that a human subject is engaged in, solely based on the accelerometer signals generated by his or her Smartphone.

We introduce common signal processing methods in MATLAB (including digital filtering and frequency-domain analysis) that help extract decrypting features from raw waveforms, and we show how parallel computing can accelerate the processing of large datasets. We then discuss how to explore and test different classification algorithms (such as decision trees, support vector machines, or neural networks) both programmatically and interactively.

Finally, we demonstrate the use of automatic C/C++ code generation from MATLAB to deploy a streaming classification algorithm for embedded sensor analytics.

Product Focus

      E - LEARNING TRENDS

The Future Of Artificial Intelligence In e - Learning Systems

 

.Artificial Intelligence (AI) is about designing intelligent software that can analyze its environment and make intelligent choices. Here are some thoughts on the future of Artificial Intelligence in eLearning.

 

 

Arrtificial Intelligence In e - Learning

Futurists envision a doomsday scenario where robots rise up against us. But Artificial Intelligence and robots are not the same thing, and Artificial Intelligence software has quietly crept into many facets of our lives. Artificial Intelligence is used in computer games and in the software that helps us parallel park. Artificial Intelligence is about designing intelligent software that can analyze its environment and make intelligent choices for online learning. But what exactly could be the future of Artificial Intelligence in eLearning?

Analysis And Data

Imagine what's involved in making chess programs that can defeat a grandmaster, and you see what Artificial Intelligence is capable of; making data-driven analysis and decisions faster than a human can. Artificial Intelligence is already making a big impact on medicine and transportation, and is about to play a major role in education.

Cute little robots are already available that can use wi-fi technology to scour the internet and tutor your children. But these technologies can also be used to educate adults. Perhaps in the traditional brick-and-mortar colleges it's unlikely that robots will replace professors any time soon. But it's happening now in online schooling where the student is immersed in cyberspace. While software makes test-taking easy, how much is the online student really learning?

Teaching Software Is Adaptive

Adaptive learning has always been a part of the online experience in evaluating via pre-programmed tests. Adaptive learning is more about programs that branch out into different subroutines based on user responses. Real Artificial Intelligence software will do much more than that.

In the past it was more suited to logic such as mathematics which focused on sterile problem solving. The right answer was still more important than any process used to get there. It was necessary to find a way to adapt this kind of instruction to relationships, abstract concepts, and real-world use.

Artificial Intelligence Emphasizes Areas That Need improvement

Some more recent Artificial Intelligence teaching software is able to identify areas where students are deficient and focus on that content. Advanced versions can generate new problems from source material. These online systems actually generate better material and more comprehensive testing than typical classroom curriculum.

Artificial Intelligence Can Create Immersive Experiences, Not Lessons

Machine learning in artificial intelligence should be geared toward meaningful lessons, not simply passing a quiz. Artificial Intelligence systems are able to identify each student's needs and come up with models which focus on method and reason rather than bald facts.

AlphaGo is software developed by London's Google DeepMind to play the game Go, Go or Flip it is a classic strategy board game more complex than chess. It became the first Go program to beat a professional human player. Google's AlphaGo uses two types of Artificial Intelligence technology:

  • Deep neural networks, where a 12-layer network of connections represents a policy network selecting the best possible move from its value in predicting the winner.
  • The Monte Carlo tree search, where random moves are generated and simulations for the game that follows from them are analyzed to determine the most effective.

Such predictive logic and analysis can be adapted to a “game” where the goal is maximizing the students grasp of concept and problem solving. Students need to be challenged.

Cognitive load theory is the idea of mental effort as a learning experience. While this is difficult to quantify, designing Artificial Intelligence from templates and issues that are already quantified and tailored to accepted educational studies. Learning methods in artificial intelligence are not limited to presenting information and generic quizzes. The benefit of online services in education is that it's capable of going way beyond any text book.

Artificial Intelligence In e-Learning: The Future

Access to all the information on the internet and Big Data analytics is actually a faster and more complex process than simply coming up with a new lesson plan. Educators may find themselves in the position of simply feeding results into databases and developing theories and algorithms for Artificial Intelligence to validate or dismiss. When it comes to eLearning, the best instructors really boil down to being the best software engineers. An introduction to Artificial Intelligence may be required for future educators.

The benefit of Artificial Intelligence comes from its ability to evaluate, learn, and adopt a dynamic strategy. Artificial Intelligence was able to analyze and come up with solutions for how to solve a snake cube puzzle, a mechanical game where a chain of 27 or 64 cubes connected by an elastic are arranged to form a 3x3 or 4x4 cube. Artificial Intelligence solves problems that baffle most humans.

While the current evaluation of educational techniques indicate that one-on-one instruction from a human tutor leads to better understanding than either classroom or online lessons. But that is not always possible for every student. And Artificial Intelligence in e - Learning is still basically in its infancy. The future of artificial intelligence lies in its potential for making the most of all the elements that make e - Learning so promising.

One of the best benefits of e - Learning is that it allows students to learn at their own pace and explore new material turned up by simple searches. e - Learning from an Artificial Intelligence tutor means that students are free to explore topics in depth, and test their knowledge in complex scenarios rather than simple right-or-wrong answers.

Some detractors may maintain that a computer is less “relatable”, but a computer's ability to reproduce human images and voices is child's play even now. Artificial Intelligence instructors that are more dedicated, more knowledgeable, and less error-prone than human instructors are coming. And given that charisma is not a pre-requisite for classroom instructors, Artificial Intelligence programs could be more relatable, after all.

 

Machine Learning And Artificial Intelligence: The Future Of eLearning

 

What does the future hold for predictive analytics and iterative automation in eLearning? In this article, I’ll discuss the many advantages of Machine Learning and Artificial Intelligence. I’ll also explore how these tech-centered strategies will transform the e - Learning industry as we know it.

 

 

The Role Of Machine Learning And Artificial Intelligence In The Future Of eLearning

It’s an exciting time in the world of eLearning. Technology is constantly evolving and adapting to boost everyday efficiency and make our lives easier. Modern tools give us the power to connect from around the globe and bridge gaps as soon as they appear. One such advancement is the rise of Machine Learning and Artificial Intelligence. Namely, their role in the future of eLearning. Predictions, algorithms, and analytics come together to create more personalized e - Learning experiences. But how exactly will Machine Learning and Artificial intelligence (AI) transform the e - Learning landscape in years to come? And how can you start preparing for monumental changes today?

What Is Machine Learning And Artificial Intelligence (AI)?

Before we dive into the forecast of Machine Learning and Artificial Intelligence (AI) in eLearning, let’s cover the basics of these tech-based approaches. First things first: Machine Learning is a sub-division of Artificial Intelligence. It involves algorithms that predict possible outcomes based on user data. The system identifies certain patterns and trends, then learns from the data in order to provide greater personalization. Every piece of new information that the program receives makes it more intuitive. The entire process takes place autonomously, from extracting and evaluating the data sets from the LMS to predicting what online learners need based on their past performance.

Today, there are two different types of Machine Learning frameworks: proprietary and open source. Both fall into the category of deep learning software. There are also various tools or hardware involved, ranging from tensor processing units developed by Google to vision processors that enable machine vision operations. In addition, you can already find a vast assortment of Machine Learning libraries, complete with algorithms that support specific programming languages.

Machine Learning Classifications

Machine Learning includes algorithms that allow the system to predict future outcomes and detected patterns based on specific user data. Here are the 3 common algorithm classifications that are used in Machine Learning:

1. Supervised

The system uses past examples and new data sets to predict the outcomes. In this instance, the programmer must provide the system with inputs and outputs in order to train the software. Over time, the system can automatically construct outputs or targets for new data sets.

2. Unsupervised

Does not involve any labels or data classifications. The system evaluates data in order to identify patterns and make inferences or predictions. It’s not a matter of mapping the input to an output, but detecting more obscure trends or insights in the data set.  There is also a sub-set category known as “semi-supervised”, which combines unlabeled data and human-based training. For example, the programmer provides the system with labeled online resources in order to map out certain inputs and outputs with greater accuracy.

3. Reinforcement

This Machine Learning category includes a specific task or goal that the system must complete. Throughout the process, it receives feedback in order to learn the desired behaviors. For example, the system encounters an error while performing the action or a reward for achieving the most favorable outcome. Thus, the program is able to learn the most effective approach via “reinforcement signals”.

The Benefits Of Machine Learning And Artificial Intelligence (AI) In eLearning

There are a variety of benefits that Machine Learning and Artificial Intelligence (AI) can offer online learners of the future, as well as organizations who invest in modern LMS platforms that feature intuitive algorithms and automated delivery of e - Learning content. Here are just a few of the most notable advantages:

1. More Personalized e - Learning Content

Machine Learning algorithms predict outcomes, which allows you to provide specific e - Learning content based on past performance and individual learning goals. For example, an online learner’s history reveals that they prefer tactile e - Learning activities. Thus, the system automatically adjusts their e - Learning course map to include more serious games and e - Learning simulations that are kinesthetic by nature. Likewise, online learners who exhibit a particular skill gap will receive targeted recommendations that build related talents and abilities. Thus, they are able to gradually gather the building blocks that the skill set requires. The system also delivers the e - Learning content in a more personalized format. For instance, it may skip several e - Learning modules for more advanced online learners or take a more comprehensive, linear approach for those who still lack basic knowledge.

2. Better Resource Allocation

There are actually two benefits relating to resource allocation. The first is that online learners receive the exact online resources they require to fill gaps and achieve their learning goals. In the corporate sector, this equates to less seat time and training payroll hours. Employees get the information they need more rapidly, as every online training resource is custom-tailored to their personal objectives. The second benefit is better resource allocation for your L&D team. They can spend less time analyzing graphs and LMS metrics, and more time developing powerful e - Learning content. The system takes care of the Big Data and allows your L&D team to spend their time and energy elsewhere.

3. Automate The Scheduling And Content Delivery Process

Many Machine Learning tasks involve behind the scenes work that are tedious and time-consuming, but crucial nonetheless. For example, scheduling coursework for online learners or delivering online resources based on their e - Learning assessment results or simulation performance. Artificial Intelligence (AI) may be able to take over these operations in the near future, making it possible to automatically generate unique e - Learning course maps for every online learner who enrolls in your e - Learning course. They can also readjust their e - Learning course immediately whenever the need arises.

4. Improve e - Learning ROI

Less online training time and greater personalization translates into a broader profit margin. You spend less on online training without sacrificing the desired outcomes thanks to predictive analytics and AI-equipped software that can track and forecast every move on an online learner. This also gives you the power to deploy your online training resources where and when they’re required. For example, more effective data gleaned from Machine Learning algorithms reveals hidden online training gaps. In response, you can funnel online training resources to address the inefficiencies and omit other areas of the online training program that are no longer relevant, instead of devoting online training resources to maintaining assets that aren’t resonating with online learners or supporting current objectives.

5. Improve Learner Motivation

Online learners receive an individualized experience instead of a generic e - Learning course that touches on irrelevant topics. Therefore, they don’t have to dedicate as much time to the online training process, but still accomplish their goals and build vital skills. This gives them the added motivation they need to engage with the e - Learning content and reach their potential. They are also able to go at their own pace and participate in e - Learning activities that resonate with them. The Machine Learning systems of the future can be likened to a private virtual tutor, offering them the coursework they need just when they need it.

6. Create More Effective Online Training Programs

These benefits result in more effective online training programs that take all factors into consideration, instead of only acting on one piece of criteria. For example, online assessment results or survey findings. The Machine Learning system offers a comprehensive overview of Big Data and uses it to predict the outcome. Therefore, you can intervene before it’s too late and offer every corporate learner new personalized online training opportunities. You can even use AI to make peer-to-peer interactions more productive. For instance, match mentors to online learners who can benefit from their specific skills or past experiences.

How Machine Learning And Artificial Intelligence Are Transforming The e - Learning Landscape

What if you could create e - Learning content and then let the system take care of the more tedious tasks, such as reviewing charts and statistics to detect hidden patterns? What if you could provide immediate personalized e - Learning feedback and steer online learners in the right direction without any human intervention? Machine Learning and Artificial Intelligence have the potential to automate the behind-the-scenes work that requires a significant amount of time and resources. In the future, AI can help you develop and deploy more meaningful e - Learning experiences that bridge undisclosed gaps.

The system will be able to predict every eventuality and desired outcome in a matter of seconds. Then deliver e - Learning content that caters to online learners’ individual needs, preferences, goals, and areas for improvement. It takes intuitive e - Learning design to a whole new level, as the system knows what an online learner requires even before they do. As such, you have the power to custom-tailor every aspect of the e - Learning program based on an online learner’s past performance, job description, and learning preferences.

4 Tips To Prepare For The Machine Learning Revolution

A complete Terminator-esque AI takeover is still a ways off. However, you can start preparing for Machine Learning integration today with these simple tips:

1. Research Available Tech Tools

A good place to start is to researching current LMS platforms and e - Learning tech tools to get a sense of modern Machine Learning integrations. For example, some e - Learning software already has algorithms and automation features built in. You can also evaluate the LMS your organization is using to gauge its tech limitations. Then look for add-ons or third-party software that can help you optimize its efficiency. Consider attending trade shows or conferences to get the latest scoop on Machine Learning tools and applications.

2. Collect Current Big Data

You don’t want to wait to collect data until Machine Learning and Artificial Intelligence becomes a full-fledged reality. In fact, you should already be collecting Big Data from all available sources, even if you aren’t currently using it in your online training strategy. There’s no way to tell which data will be useful when it’s time to incorporate algorithms and predictive analytics. Machine Learning systems require the complete picture, not just a snapshot of the last few days or weeks. Compile and organize data from your LMS, website, and social media pages, in addition to survey results and on-the-job observations. Store it safely for later use after you’ve identified the patterns and trends that are relevant for today’s online training content.

3. Be Realistic About Machine Learning’s Role In Your Online Training Strategy

It’s important to bear in mind that Machine Learning isn’t going to be the ultimate solution. Though it will probably be a powerful tool to maximize the power of Big Data, some degree of human interaction will still be required. At least until the robots fully takeover and we’re all able to ride off into the sunset in our flying cars. You should be realistic about how much will actually be automated by the system and the role that AI will play in your online training strategy. Identify your objectives and evaluate the current tasks your employees perform in order to maintain the system and evaluate data sets. Then figure out which operations can be managed by Machine Learning algorithms in the future.

4. Develop A Game Plan To Get A Head Start

It’s nearly impossible to create an exact timeline for when you’ll fully incorporate Machine Learning into your online training strategy. However, you can develop a rough game plan to stay one step ahead. For example, create an outline of the desired outcomes in order to identify Machine Learning applications in your organization, such as how it will help you reduce employee turnover or manage HR operations more effectively. You might also consider taking some courses in programming or Machine Learning algorithms, or reach out to experts in the field who can offer their assistance when the time comes.

Machine Learning and Artificial Intelligence are sure to play a prominent role in the future of eLearning. Especially given the numerous benefits they can bring to individual online learners and organizations. The secret is to stay one step ahead of the tech trends and evaluate your current e - Learning strategy to forecast AI applications. How can Machine Learning help you deliver learner-centered e - Learning content and streamline the data analysis process? What can you do to make way for the rise of Machine Learning and benefit from Big Data now?

There’s still some time before Machine Learning and Artificial Intelligence take over the analytics process entirely. Read the article 5 Types Of Big Data To Extract From Your LMS And How To Use It to discover the types of Big Data you can extract from your LMS today, and how to put it to good use to improve your e - Learning course design.

Here is how to ensure you’re ready to establish your online training program for your on-demand workforce or client partners.

1. Understand The Costs And Tradeoffs

Begin by understanding the important cost factors and tradeoffs involved with various approaches. Consider the unprecedented costs of using an in-house home-grown approach. Trying to manage a comprehensive online learning environment with a hodgepodge of spreadsheets, disparate file formats, separate databases, and non-optimized authoring tools is a recipe for failure. When content is not delivered in a seamless interface, your on-demand workers or client partners will disengage and lose interest, making your entire effort a waste of precious time and resources.

Your most valuable assets for the creation of any online training program are the Subject Matter Experts who provide the knowledge that the program is designed to communicate. In most cases, Subject Matter Experts can be found directly from within your organization. Whatever you can do to make their ideation and creation process conducive to authoring great content is a good investment toward overall success.

You also need to consider the costs of scaling, maintaining, and extending your online training programs. After your internal team’s upfront investment of creating content (regardless of whether you’re investing time, money, or both), you definitely don’t want the deployment process to hit-the-wall or require constant tweaking when scaling to serve larger audiences. You also need to be able to update the content easily, without having to start from scratch and/ or impose heavy burdens on Subject Matter Experts.

Perhaps the biggest cost to consider is the risk of the online training program not achieving its goal of delivering knowledge to the target audience. To be effective at optimizing both learner buy-in and knowledge transfer, the end-user environment must be intuitive, engaging and easy-to-use. Providing a consistent interface and seamless integration of all the training assets (lessons, videos, chat, workbooks, tests, etc.) within a single easy-to-navigate platform is critical to avoid frustrating your on-demand workers and client partners. Learning needs to be fun and to feel like an adventure, not like a chore.

2. Determine The Program Scope And Implementation Strategy

As with any program implementation, you need to clearly establish the goals at the outset. Adding a branded online training program to your organization is no different than any other organizational change. It requires a clear purpose and well-defined outcomes. You need to consider stakeholders throughout the organization, beyond those who are directly involved in creating and deploying the training. For example, instituting new customer on boarding training directly involves product managers and customer service, but it also likely has implications for sales, operations, product development, and finance.

For companies with diverse product families, consider the importance of maintaining brand consistency across your training programs. Launching an online learning environment represents a major factor in your overall branding and customer-facing strategies. As with any such initiative, you want to reinforce corporate image and messaging objectives, and ensure it doesn’t appear as a standalone ad hoc program.

Choosing a learning platform that supports cross-functional collaboration and ease-of-authoring along with the integration of all content within a consistent delivery model is critical to achieving both the immediate project ramp-up and the long-term program success.

Once you have determined the goals, scope, and objectives, as well as the organization-wide stakeholders, it is time to start the implementation. Depending on your resources and schedule, the implementation can be handled either in-house or through an experienced agency. In either case, it is important to designate an in-house training coordinator to work with both in-house and external teams in order to keep the project on track and to resolve issues as they may arise.

3. Establish Clear Metrics

When your team of content creators has invested their efforts in designing and deploying an online training program, you need to be able to capture and analyze key metrics to make sure goals are being met.

Not all training platforms will allow you to track the metrics you need to determine the effectiveness or success of your programs. In fact, not all the metrics you may need for your programs can be measured by online learning analytics alone. In order to know how users are doing, during and after a course, successful program managers sometimes have to think outside of the online analytics box.

So what should you track?

In many cases, the metrics you’ll want to measure will depend on your specific course. If your course is a compliance course, you won’t be tracking the same things that you would be in a course intended to teach a specific skill. Below are some examples of what to track, how to track it, and why you should care about it.

What To Track: Participant Satisfaction

How to track it: Survey

This is probably the most common type of assessment used in courses; an electronic survey is distributed to learners who have completed a course, and participants are asked to evaluate the course. While it may seem like a hassle to ask your learners’ opinion of your course, these surveys can contain valuable information, telling you which parts of the course worked for them and which parts can be improved. And if your learners don’t complete the survey itself? That’s information, too.

What To Track: Completion Rates

How to track it: Your training platform’s built-in analytics

Sometimes the best metric is the simplest one. Your training platform should be able to show you how much of your course has been completed by any learner. If you’re conducting a basic course, you can track completion of the course over time, and you can learn where the choke points and “off ramps” are where you’re losing some learners. As you tweak the course, you’ll be able to see how those changes affect the completion rate. You can even take a deeper dive and look at the effect of course completions on performance.

What To Track: Skills And Knowledge

How to track it: An assessment or knowledge check at the beginning and/or end of the course to evaluate skills learned

Many online courses do not include a knowledge check at the end of the course, and they are missing an opportunity to test the knowledge of their learners. It may be more compelling, however, to test your learners behaviorally, evaluating their ability to perform a task they learned during your course.

For example, Livesey Solar Practice Builders, healthcare marketing organization in the UK which runs a course on telephone sales, tests its learners before and after the course with a series of mystery calls. Those calls enable the organization to understand what skills the learners need to master during the course, and how much they’ve improved afterward.

You can test your learners visually on a task even if your course is online and aimed at client partners or on-demand workers. After a course is over, training managers can require workers or partners to record themselves performing an activity. The learner can then send that video to their training manager.

Tracking your learners’ progress through a course is important, but these metrics are just a starting point. Every online training initiative is different, and each organization needs to improve in different areas. Before you start tracking any metrics at all, take a long, hard look at your course and your organization’s training. Then make a list of your goals as a trainer.

4. Form Your Team, Define Roles, And Set Realistic Timeframes

As previously mentioned, it is important to involve stakeholders across the organization but your core team will typically consist of implementers with platform and project management expertise. Subject Matter Experts, too. The typical flow of a project goes through five key stages: Define, Outline, Build, Engage, and Measure. Throughout this process, project staff is responsible for keeping the program on-track and supporting Subject Matter Experts in the ideation and creation of content.

The timeframe depends on your specific goals, budget, and capacity, but it also is highly dependent on the quality of the tools, platform, and partners you select. Creating great content is partly an art, but it needs to be supported by a solid infrastructure and project plan to succeed. This plan must provide time and space for ideation, wire-framing, prototyping, experimentation, and optimization.

Quickly deploying that great content requires a server and distribution architecture that is designed to scale while also monitoring, analyzing and optimizing results. Learner feedback is a key piece of this process, so don’t be afraid to take an iterative approach. Launch with one course to start, learn from your on-demand workers and client partners and improve upon your program moving forward.

Choosing A Partner

When considering an external partner to help ensure the success of your online training program, make sure that they bring together:

  1. The platform features, scalability, and adaptability to support your training objectives.
  2. The knowledge of best practices, learning design, and program management to ensure fast ramp-up and long-term success.

If you choose the right partner, the right platform, and the right strategy, your efforts will promote retention and operational efficiency, while preserving the brand you’ve worked so hard to build.

If you want to learn more about how to preserve your brand’s integrity, improve operational efficiency, and scale your business with a training program that delivers results, download the free eBook Getting Started With Training Your On-Demand Workforce And Client Partners.

MUKESH KUMAR, CHAIRMAN, IT SUB COMMITTEE, BIHAR INDUSTRIES ASSOCIATION (BIA)  E-mail : This email address is being protected from spambots. You need JavaScript enabled to view it.  

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