What's Changing the Game for e-Learning?

Robertcordray
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E-Learning is evolving and changing how we live our everyday lives. Modern tools have enabled us to connect with other people and even institutions around the world immediately they make an online presence. Since the dawn of Artificial Intelligence (AI) and Machine Learning (ML), e-learning has changed because of algorithms, analytics, and predictions that create personalized e-Learning environments. How has e-learning evolved? What is driving the change in e-learning?

As we have seen, ML and AI are the biggest drivers of change in the eLearning space. Let us deconstruct and see what the future of e-learning is going to be like with these two aspects of technology.

What are AI and ML?

First of all, ML is part of AI; it involves algorithms that predict potential results based on the data of users. ML identifies trends and patterns, learns from the data it has been fed and then provides a personalized pattern. Additional information that the program gets makes it more intuitive. The whole chain is autonomous, from evaluating the extracted data from the LMS to predicting the needs of online learners relating to their past performance.

There are two types of ML frameworks:

  • Open Source
  • Proprietary

Additionally, more different tools are involved, from vision processors for enabling machine vision operations to Google’s tensor processing units. You can also find different types of ML libraries that support some programming languages.

Benefits of ML and AI in e-Learning

E-learning is growing because of various benefits that AI and ML bring to the table. We are going to look at some of these advantages.

Personalized e-Learning content

ML is tasked to provide specific content of e-Learning, based on an individual's past performance, to predict outcomes using algorithms. For instance, the history of an online learner reveals that they are used to tactile activities of e-learning. This makes the system adjust the course of e-Learning to include more content.

Additionally, online learners who show a particular skill will get recommendations that will help them build their abilities and talents. E-learning content is delivered in a personalized format. For instance, several e-Learning modules that are advanced may be skipped, and instead, basic knowledge becomes available for these online learners who are starting to learn a particular course.

Resource allocation

Online learners can get the right resources online to achieve their objective. This means that there is less training payroll hours and seat time. The information needed is retrieved easily because the online training resource is customized to meet personal objectives.

You can also use less time looking at LMS metrics & analyzing graphs and spend more time developing e-Learning content.

Content delivery process and automating the scheduling

ML tasks usually occur in the background. For instance, coursework scheduling based on the online learners’ performance simulation of e-Learning assessment. AI, on the other hand, is going to undertake these operations, to make it possible to generate e-Learning course maps automatically for online learners who enroll for the e-Learning courses. Online learners can also re-adjust their course whenever their need arises.

Improve ROI on e-Learning

Greater personalization and less time on online training results to more profits. Fewer resources are used on online training, but still, the desired outcomes are achieved, thanks to AI and predictive analytics software that can forecast the move online learners. This makes it possible for anyone to deploy online training resources anytime. For instance, online training gaps are revealed by ML algorithms. This allows one to funnel resources of online training to address inefficiencies and omit irrelevant part of the program, instead of dedicating online training resources to irrelevant assets that are not used by online learners.

Improving the motivation of learners

Individualized experiences are extended to online learners instead of general courses that have irrelevant topics. This makes it possible to avoid wasting extra time on online training processes, while still accomplishing goals and vital skills. This enables online learners to reach their potential as they learn at their own pace on learning activities that apply to their needs. ML of the future will enable connections to private virtual tutors who will offer coursework when required.

Conclusion

ML and AI will take some time before they take over the whole field of analytics. There is a lot developing in the space especially on the management on how big data is improving the e-Learning course design.

About Robertcordray

Robert Cordray

Robert Cordray is a former business consultant and entrepreneur with over 20 years of experience and a wide variety of knowledge in multiple areas of the industry. He currently resides in the Southern California area and spends his time helping consumers and business owners alike try to be successful. When he’s not reading or writing, he’s most likely with his beautiful wife and three children.

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