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Expert Opinion: 10 E-learning Trends that will Dominate in 2020 [Infographic]
Since the early days of e-learning, its benefits have significantly outweighed those of face-to-face training. The rapid growth of the internet and mobile devices has made e-learning flexible, time-saving, and cost-effective in training.
This has highlighted e-learning as an essential part of education market growth. Companies have adopted e-learning as a more flexible and effective way to train their employees. Additionally, customer education is becoming more and more popular as an innovative marketing tactic.
However, we are far from reaching an upper limit. The current and future trends in e-learning prove that it is a field for continuous innovation. It is true that the e-learning market size is expected to grow beyond $300 billion by 2025
What we saw last year emerging as a top trend will probably continue to build up and transform in the new year until it is mature or replaced by a better or different technology or method.
Where do we see e-learning heading for the new year?
What the Experts Predict – E-learning Trends that will Dominate in 2020
There are so many “so-called experts” out there promising you one-week success and opportunities that fall from the sky.
But, in reality, it requires a lot of hard work, study, experimentation, and persistence. You need to be continuously informed about the new trends in e-learning so that you can keep up with new students.
That is why we reached ten different e-learning experts to ask them what they believe the trends for 2020 are.
Their answers reveal exciting new trends that will change the e-learning scene, given that we will do really hard work to provide amazing learning experiences and stand out in the e-learning field.
Here are the top e-learning trends for 2020, according to the experts. Feel free to navigate to each one of them!
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|Table of contents|
|Curriculum as a Community|
|Smart Content Curation|
|Emphasis on the Instructional Designer|
|Mobile-Friendly Online Course Platforms|
|AI, AR, VR, MR, and VUIs|
1. Curriculum as a Community
Curriculum as a community is based in a learning environment where participants engage with the content and – more importantly – with each other. Online collaborative activities allow all participants to expose their ideas and thereby create an interactive canvas of diverse reactions and feedback.
According to Jeff Cobb, a veteran of 20 years in the e-learning industry and owner of learningrevolution.net:
Jeff refers to, as an example, an environment with carefully designed questions for students, 24/7 discussion, and a team of facilitators who work to connect learners and keep them engaged.
Indeed, creating a community will help you stand out. A community in online courses provokes the students to participate and learn together. Learning is a social activity by itself. We learn through contact and discourse with another person who is more competent in the field. Speech and conversation with one another generate knowledge that is negotiated and subjected to endless talk.
This is why Bill Brandon, Senior Editor at Learning Solutions Magazine, also believes that peer-to-peer applications that drive collaborative learning will be an actual trend for 2020.
Online collaborative learning engages students in higher-order thinking skills. It can be either synchronous or asynchronous, and usually is instructor-led and text-based. The instructor is the facilitator of the group discourse and acts as a mediator between learners.
In an online learning platform, what you need to look for is a built-in social network page where you can empower your active community and allow students to be bonded and actively involved in the school.
However, building a learning community is neither automatic nor straightforward to achieve. The instructor has to understand the structural elements of learners’ communication and their motivational layers. All you have to do is to learn easy ways to build an online learning community in 2020.
2. User-generated Content
An inevitable trend that arises from the curriculum as a community in 2020 is User-Generated Content. Christopher Pappas thinks that this trend will reign supreme in 2020.
User-generated content is what actually the phrase says: “Learners develop content and share it with their peers. It can refer to videos, blog posts, testimonials, pictures, tweets, ebooks, and any type of content.”
Imagine how you could decrease your time and money budget by using content generated by your students.
Obviously, user-generated content campaigns have been a constant player also in the marketing world because people crave stories from real people, and they want interaction with other humans. It is not a coincidence that 92% of people are more likely to trust a recommendation from another person over branded content.
User-generated content is so effective because it’s authentic, it creates a sense of community, and it’s cost-effective. This tactic, which is very easily done within an LMS, benefits learners and facilitates knowledge sharing.
Users can upload their resources to the system through any device and share their content. It really relies on the existence of a strong team where everyone helps each other to achieve shared goals.
Imagine people sharing examples (in the form of videos, pictures, or text) of how and why they used your products. And we are talking about REAL examples, not the ones you have to create on your own as your brand representative.
You will have valuable examples of your product’s fans. All you have to do is give incentives to people to produce such material.
3. Smart Content Curation
One of the learning trends for 2020 is Smart Content Curation. It refers to the manner we treat our educational material, given that our students don’t have time to discern the necessary information in them.
According to Christopher Pappas, the founder of eLearning Industry’s Network, the largest online community of professionals involved in the eLearning field, a smart content curation, requires excellent content organization, omission, and optimization.
That means that every time we design new learning content, we have to think:
- Is our information targeted enough and suitable for our target audience?
- Which sites can we rely on for up-to-date, relevant information?
- How will we deliver the targeted takeaways learners need?
- How will we show that we own our learning material?
- How will we curate the course design?
All these questions must be thoroughly answered by an online instructor, because as Christopher says:
And Panos Siozos, the CEO and co-founder of LearnWorlds, adds that:
To achieve optimal content delivery, instructors must adopt a plan to design a course. We are talking about instructional design – the systematic development of instructional material using instructional theory.
It is the process of analyzing learning goals and adapting specific learning strategies and methods to achieve those goals and ensure the quality of instruction. Here is an example of an engaging instructional method:
Poppy Hill, a talent development, e-learning expert, and a Certified Performance Improvement expert (CPT), founder of Polygon performance, emphasizes storytelling as a method to make your content engaging.
People are evolutionarily hardwired for stories. Storytelling and e-learning are a natural pair.
Poppy also recommends one method to follow: Once upon a time (Introduction, People and Setting), Suddenly (Problem Action, Conflict), and then (Solution/Journey, Elements) Happily ever after (Results, Action).
There is a variety of such methods used in e-learning you can search for to compose your instructional design. Instructional design is often developed by a certain person in a company that is qualified in refining and organizing the learning content.
The need for such qualified people is what forms the following e-learning trend for 2020.
4. Emphasis on the Instructional Designer
According to David Hopkins, an experienced leader and manager of learning design and learning technology, author, blogger, Certified Member of the Association for Learning Technology (CMALT) and Fellow of the Higher Education Authority (FHEA), what is constant is our attention to clarity and quality when producing learning materials, as “Technology comes and goes.”
And who is responsible for the clarity and quality of the learning material?
From translating original content to the appropriate adoption and use of the technology to deliver the training, which is more important than everything, is this person.
More and more companies now hire instructional designers. And experts now emphasize their role for 2020. An instructional designer:
- Organizes the learning content
- Decides over the learning objectives
- Simplifies the learning content
- Designs from the learners’ point of view
- Makes learning experience fun and engaging
- Keeps communication with learners and a lot more
For David, “the individual must become the focus of the learning experience, not the technology delivering it.” Technology – in the form of the learning platform – still has a part to play, but the focus is on how to use this technology to support the creation of learning materials.
According to this theory, the instructional designer (who often doesn’t have only one capacity in a company) is the one businesses should pay attention to.
Instructional designers are going to be very important in learning and development in 2020. According to David, when designers are supported properly in a company, a wide variety of skills, creativity, and capability is possible.
This picture depicts the severity of roles of an instructional designer:
5. Interactive Videos
Video has always been a trend in e-learning. And it will continue to be among the future trends. They have been scientifically proven to increase learner engagement and participation, thus maximize knowledge retention and ability to visualize the “unexplained”:
Video-sharing platforms, such as YouTube and Vimeo, and the explosion of mobile devices, have given a real resurgence in video use.
A rising trend within video learning is Interactive Videos. As Phil Mayor, the Creative Director of Elearning Laboratory, an instructional design company, states:
Interactivity in videos can be considered a major influencing factor of the learning success because it is transforming passive watchers to active learners.
LearnWorlds is one of the platforms that provides interactive videos. In fact, it is the only one from the most successful platforms that do so and gives a lot of guidance on how you can jazz up your courses with interactive videos through a variety of free blog posts and free courses.
What are interactive videos? They are videos that help students explore further resources apart from the video content with links and interactive objects appearing in the video.
They allow users to navigate in the content through content tables, bookmarking, interactive transcripts, and navigational buttons.
What’s more, they include questionnaires with feedback at the exact moment you want.
Also, interactive videos, in some cases, support dialogues around the video content.
We can now understand why the traditional video is no longer sufficient on its own.
Craig Weiss, the CEO and Lead Analyst of The Craig Weiss Group and founder of FindAnLMS, also believes that the scene in the learning authoring tools will change:
6. Learning Analytics
With Learning Analytics, instructors view student feedback in questionnaires, assignments and also see the video engagement – in short – how students perform in general. This data shows how much time each learner engages with each activity and helps instructional designers optimize courses.
Most training platforms provide Learning Analytics, which is the industry-leading tool for tracking and reporting student behavior that can assist your course development journey every step of the way.
Ryan Tracey, the E-learning Provocateur, creator of one of the top e-learning blogs, and Learning Innovation Manager at Macquarie Group, thinks that data is the next big trend for 2020.
He emphasizes on the importance of learning analytics by saying that “Only with such data can we be confident that we’re adding value to the business.”
Also, Craig Weiss expects to see more systems incorporating or connecting with Business Intelligence Tools, which will result in extensive metrics and data. This video explains more about learning analytics.
In training platform, there are also several tracking tools concerning course sales like:
- Google analytics
- Google tag manager
- Mix panel
- Facebook pixel
Microlearning is still a rising trend for 2020 and will grow in importance both in the private and public sectors because of its engaging nature.
It refers to small bite-sized chunks of information that aim to teach specific skills. Means of microlearning can be texts, images, videos, audio, tests, quizzes, games, or a combination of them.
Not strange that microlearning is still a trend. As Panos Siozos says:
It is a fact that long modules are boring for learners. Smaller chunks of content are easier to employ and also improve learners’ retention levels.
According to Barbara Anna Zielonka, an experienced learning designer and EdTech specialist, top-10 Global Teacher Prize finalist, with a proven track record in the education sector:
Microlearning is less time-consuming, and cheaper-to-produce and is surprisingly effective for corporate and commercial training. Personalized and bite-sized courses are ubiquitously easy to read. Also, smaller courses with interactive elements appeal to all different types of workers. This is why Microlearning is a win-win for employees and employers.
Phil Mayor also sees Microlearning as a trend for 2020.
8. Mobile-Friendly Online Course Platforms
In conjunction with the still rising trend of microlearning, ubiquitous learning is taking more and more space. The ability to learn anywhere anytime is more real now than ever before.
Panos Siozos says:
It’s more important than ever to make our courses accessible to all so that they can watch it from anywhere and anytime. Apart from computers, it should be made available for mobile phones, tablets, or any other device that learners are using. This is why:
For this, its imperative in 2020 to invest in a totally mobile-friendly platform to host your online courses and start thinking about a post-laptop learning strategy.
9. Virtual Conferences
Another popular trend in e-learning for 2020 is Virtual conferences. A virtual conference is an online event that brings together a group of people with similar expertise so that they can learn from one another. They occur entirely online rather than in a physical location.
According to Jeff Cobb virtual conferences have solved the problem of declining attendance at traditional face-to-face events. Organizations now hold conferences to gain more traction and they start capitalizing on the advantages that virtual conferences provide. Such advantages are the ability to:
- Provide a more personalized experience
- Leverage existing e-learning content and
- Spark ongoing community and conversation
Hosting a virtual conference has a lot of benefits:
- You can build a large email list fast
- You can raise your visibility
- You build rewarding relationships with influencers
- You become an authority
- It is a great business model
This is why Jeff also says:
10. AI, AR, VR, MR, and VUIs
In 2020 we will also see some changes in the field of user experience. AI, AR, VR, MR, and VUIs are expected to increase attention and improve the learning experience.
Bill Brandon thinks that:
Voice user interfaces (VUIs) allow learners to use a system with voice or speech commands. Already accessible through smartphones, VUIs allow us to give commands to computers, eyes, and hands-free users can easily interact with a product.
Being able to verbalize a Google search instead of typing each character individually makes information easily accessible and ubiquitous. This might support students to research facts, check spellings, and look up words or synonyms without too much distraction from the main task.
To remember trends also popular in 2020:
- The main task of augmented reality (AR) is to “enrich” real objects with other capabilities and characteristics.
- Virtual reality (VR) bridges the gap between theory and practice. One of the most beneficial features of virtual reality is its ability to cut distractions.
- Mixed reality (MR) is a totally immersive practice through which learners can interact with a mix of real and virtual worlds.
- Artificial intelligence (AI) models that use algorithms to collect learner data.
Poppy Hill states that:
And she gives the following example:
“I’ve used VR in e-learning by creating a movie in a dairy farm to pasteurize milk. The movie took place in a dairy pasteurization plant, and the learner could walk around with an IPAD and press buttons to open doors and learn about everything in the room. The customer would never let people into their processing plants to learn, so these videos were the next best thing. They are easy to make, and you don’t even need special glasses!”
With the e-learning trends we highlighted here, signs are clear that the e-learning industry will only grow stronger in the coming years. As technology develops, these trends will get better and better.
Trends come and go, but the e-learning trends seem to maintain a specific route:
Learner-centered, personalized, accessible, and engaging e-learning.
Organizations need to pay close attention to these new developments and adapt what best suits them. As Jeff Cobb, says:
Based on the above trends, these are our suggestions for 2020:
- Create a strong sense of community
- Allow users to exchange content
- Curate your learning material based on the needs of your audience
- Hire creative instructional designers
- Incorporate interactive videos in your training
- Use data to improve your courses
- Create bite-sized courses where appropriate
- Use a mobile-friendly course platform
- Organize virtual conferences, and
- Adapt leading-edge technology that skyrockets learning experiences
If you like what the experts told us, give them a small thank you by sharing their advice on social media!
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MyStory: Step by Step process of How I Became a Machine Learning Expert in 10 Months
Not so long ago, using the pivot tables option in Excel was the upper limit of my skills with numbers and the word python was more likely to make me think about a dense jungle or a nature program on TV than a tool to generate business insights and create complex solutions.
It took me ten months to leave that life behind and start feeling like I belonged to the exclusive world of people who can tell their medians from their means, their x-bars from the neighborhood pub, and who know how to teach machines what they need to learn.
The transformation process was not easy and demanded hard work, lots of time, dedication and required plenty of help along the way. It also involved well over hundreds of hours of “studying” in different forms and an equal amount of time practicing and applying all that was being learnt. In short, it wasn’t easy to transform from being data dumb to a data nerd, but I managed to do so while going through a terribly busy work schedule as well as being a dad to a one-year old.
The point of this article is to help you if you are looking to make a similar transformation but do not know where to start and how to proceed from one step to the next. If you are interested in finding out, read on to get an idea about the topics you need to cover and also develop an understanding of the level of expertise you need to build at each stage of the learning process.
There are plenty of great online and offline resources to help you master each of these steps, but very often, the trouble for the uninitiated can be in figuring out where to start and where to finish. I hope spending the next ten to fifteen minutes going through this article will help solve that problem for you.
And finally, before proceeding any further, I would like to point out that I had a lot of help in making this transformation. Right at the end of the article, I will reveal how I managed to squeeze in so much learning and work in a matter of ten months. But that’s for later.
For now, I want to give you more details about the nine steps that I had to go through in my transformation process.
Step 1: Understand the basics
Spend a couple of weeks enhancing your “general knowledge” about the field of data science and machine learning. You may already have ideas and some sort of understanding about what the field is, but if you want to become an expert, you need to understand the finer details to a point where you can explain it in simple terms to just about anyone.
- What is Analytics?
- What is Data Science?
- What is Big Data?
- What is Machine Learning?
- What is Artificial Intelligence?
- How are the above domains different from each other and related to each other?
- How are all of the above domains being applied in the real world?
Exercise to show that you know:
- Write a blog post telling readers how to answer these questions if asked in an interview
Step 2: Learn some Statistics
I have a confession to make. Even though I feel like a machine learning expert, I do not feel that I have any level of expertise in statistics. Which should be good news for people who struggle with concepts in statistics as much as I do, as it proves that you can be a data scientist without being a statistician. Having said that, you cannot ignore statistical concepts – not in machine learning and data science!
So what you need to do is to understand certain concepts and know when they may be applied or used. If you can also completely understand the theory behind these concepts, give yourself a few good pats on your back.
- Data structures, variables and summaries
- The basic principles of probability
- Distributions of random variables
- Inference for numerical and categorical data
- Linear, multiple and logistic regression
Suggested exercise to mark completion of this step:
- Create a list of references with the easiest to understand explanation that you found for each topic and publish them in a blog. Add a list of statistics related questions that one may be expected to answer in a data science interview
Step 3: Learn Python or R (or both) for data analysis
Programming turned out to be easier to learn, more fun and more rewarding in terms of the things it made possible, than I had ever imagined. While mastering a programming language could be an eternal quest, at this stage, you need to get familiar with the process of learning a language and that is not too difficult.
Both Python and R are very popular and mastering one can make it quite easy to learn the other. I started with R and have slowly started using Python for doing similar tasks as well.
- Supported data structures
- Read, import or export data
- Data quality analysis
- Data cleaning and preparation
- Data manipulation – e.g. sorting, filtering, aggregating and other functions
- Data vizualization
Know that you are set for the next step:
- Extract a table from a website, modify it to compute new variables, and create graphs summarizing the data
Step 4: Complete an Exploratory Data Analysis Project
In the first cricket test match ever played ( see scorecard ), Australian Charles Bannerman scored 67.35% (165 out of 245) of his team’s total score, in the very first innings of cricket’s history. This remains a record in cricket at the time of writing , for the highest share of the total score by a batsman in an innings of a test match.
What makes the innings even more remarkable is that the other 43 innings in that test match had an average of only 10.8 runs an innings, with only about 40% of all batsmen registering a score of ten or more runs. In fact, the second highest score by an Australian in the match was 20 runs. Given that Australia won the match by 45 runs, we can say with conviction that Bannerman’s innings was the most important contributor to Australia’s win.
Just like we were able to build this story from the scorecard of the test match, exploratory data analysis is about studying data to understand the story that is hidden beneath it, and then sharing the story with everyone.
Personally, I find this phase of a data project the most interesting, which is a good thing as quite a lot of the time in a typical project could be expected to be taken up by exploratory data analysis.
Topics to cover:
- Single variable explorations
- Pair-wise and multi-variable explorations
- Vizualization, dashboard and storytelling in Tableau
- Create a blog post summarizing the exercise and sharing the dashboard or story. Use a dataset with at least ten columns and a few thousand records
Step 5: Create unsupervised learning models
Let’s say we had data for all the countries in the world across many parameters ranging from population, to income, to health, to major industries and more. Now suppose we wanted to find out which countries are similar to each other across all these parameters. How do we go about doing this, when we have to compare each country with all the others, across over 50 different parameters?
That is where unsupervised machine learning algorithms come in. This is not the time to bore you with details about what these are all about, but the good news is that once you reach this stage, you have moved on into the world of machine learning and are already in elite company.
Topics to cover:
- K-means clustering
- Association rules
- Practice K-means clustering on 3 different datasets from different industries or interest areas
Step 6: Create supervised learning models
If you had data about millions of loan applicants and their repayment history from the past, could you identify an applicant who is likely to default on payments, even before the loan is approved?
Given enough prior data, could you predict which users are more likely to respond to a digital advertising campaign? Could you identify if someone is more likely to develop a certain disease later in their life based on their current lifestyle and habits?
Supervised learning algorithms help solve all these problems and a lot more. While there are a plethora of algorithms to understand and master, just getting started with some of the most popular ones will open up a world of new possibilities for you and the ways in which you can make data useful for an organization.
Topics to cover:
- Logistic regression
- Classification trees
- Ensemble models like Bagging and Random Forest
- Supervised Vector Machines
You have not really started with creating models till you have done this:
- Take a dataset, create models using all the algorithms you have learnt. Train, test and tune each model to improve performance. Compare them to identify which is the best model and document why you think it is so
Step 7: Understand Big Data Technologies
Many of the machine learning models in use today have been around for decades. The reason why these algorithms are only finding applications now, is that we finally have access to sufficiently large amounts of data, that can be supplied to these algorithms for them to be able to come up with useful outputs.
Data engineering and architecture is a field of specialization in itself, but every machine learning expert must know how to deal with big data systems, irrespective of their specialization within the industry.
Understanding how large amounts of data can be stored, accessed and processed efficiently is important to being able to create solutions that can be implemented in practice and are not just theoretical exercises.
I had approached this step with a real lack of conviction, but as I soon found out, it was driven more by the fear of the unknown in the form of Linux interfaces than any real complexity in finding my way around a Hadoop system.
Topics to cover:
- Big data overview and eco-system
- Hadoop – HDFS, MapReduce, Pig and Hive
Do this to know that you have understood the basics:
- Upload data, run processes and extract results after installing a local version of Hadoop or Spark on your system
Step 8: Explore Deep Learning Models
Deep learning models are helping companies like Apple and Google create solutions like Siri or the Google Assistant. They are helping global giants test driverless cars and suggesting best courses of treatment to doctors.
Machines are able to see, listen, read, write and speak thanks to deep learning models that are going to transform the world in many ways, including significantly changing the skills required for people to be useful to organizations.
Getting started with creating a model that can tell the image of a flower from a fruit may not immediately help you start building your own driverless car, but it will certainly help you start seeing the path to getting there.
Topics to cover:
- Artificial Neural Networks
- Natural Language Processing
- Convolutional Neural Networks
- Open CV
- Create a model that can correctly identify pictures of two of your friends or family members
Step 9. Undertake and Complete a Data Project
By now you are almost ready to unleash yourself to the world as a machine learning pro, but you need to showcase all that you have learnt before anyone else will be willing to agree with you.
The internet presents glorious opportunities to find such projects. If you have been diligent about the previous eight steps, chances are that you would already know how to find a project that will excite you, be useful to someone, as well as help demonstrate your knowledge and skills.
Topics to cover:
- Data collection, quality check, cleaning and preparation
- Exploratory data analysis
- Model creation and selection
- Project report
- Get in touch with a stakeholder who will be interested in your report and share your findings with them and get feedback
Machine learning and artificial intelligence is a set of skills for the present and future. It is also a field where learning will never cease and very often you may have to keep running to stay in the same place, as far as being equipped with the most in-demand skills is concerned.
However, if you start the journey well, you will be able to understand how to go about taking the next step in your learning path. As you must have gathered by now, starting the journey well is a pretty challenging exercise in itself. If you choose to start upon it, I hope this article will have been of some help to you and I wish you the very best.
Finally, I will confess that I got a lot of help with my ten-month transition. The reason I was able to cover so much ground in this amount of time, along with a busy schedule at work and home, was that I enrolled for the Post Graduate Program in Data Science and Machine Learning offered by Jigsaw Academy and Graham School, University of Chicago.
Investing in the course helped in keeping my learning hours focused, created external pressure that ensured that I was finding time for it irrespective of whatever else was going on in life, and gave me access to experts in the form of faculty and a great peer group through other students.
Transforming from being non-technical to someone who is comfortable with the machine learning world has already opened up many new doors for me. Whatever path you choose to make this transformation, you can do so with the assurance that going through the rigor will reap rewards for a long time and will banish any fears of becoming irrelevant in tomorrow’s economy.
About the Author
Madhukar Jha, Founder – Blue Footed Ideas
Madhukar Jha believes that great digital experiences are created by concocting a perfect mix of data driven insights, understanding of behavioural drivers, a design thinking approach, and cutting edge technology. He applies this philosophy to help businesses make world class products, run campaigns that rock and tell compelling stories.
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Ставка мошенников на то, чтобы обычные люди завлекали в лохотрон новых участников, как это было в «Кэшбери» и других финансовых пирамидах.
На сайте лохотрона Tender Expert расположены положительные отзывы следующих «инвесторов»: Ирина Давыдко, Валерий Иващенко, Алексей Нестерович, Кирилл Высотников, Станислав Новиков, Ольга Емщикова.
Имён организаторов лохотрона, разумеется, нет. В тюрьму они не хотят.
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