how to use machine learning to design adaptive course solutions

Machine learning technology and its algorithms that collect data to detect patterns and behaviors can be incorporated in eLearning through Learning Management Systems (LMS) to create adaptive learning solutions. Learners can now login in their LMS, have a personalized learning path with specific courses relevant to their particular needs.

Full Answer

What is adaptive machine learning and how does it work?

Changing the system's behavior requires a lot of resources. Adaptive machine learning is based on its main feature – adaptability. It doesn't matter whether the environment is static or constantly changing. The system can change its behavior without involving losses and applying outdated info.

How do we use machine learning in the assessment system?

In the web-based system for assessment we use machine learning algorithms for modeling students’ knowledge. We applied clustering of students in similarity groups, attribute (or the most relevant exercises) selection and classification (of students in ability groups) on three different domains.

Can adaptive learning systems improve skill mastery?

Adaptive systems do particularly well with instant feedback and ensuring skill mastery. Investigators note some areas for improvement: The cost of developing content for these systems is high. These systems often can't contextualize learning the way a human can.

What is adaptive learning in AI?

The core is AI's inference engine that applies logical rules to make assumptions. Adobe Captivate is an example of an adaptive learning tool that may be placed in the “simple” category. It helps instructors to create branched scenarios.

How do you design adaptive learning?

Design Principles for Adaptive Learning – The 5 CsClarity on learning objectives.Content curation from existing training resources.Chunking content for prescriptive learning.Creativity in instructional methods and activities.Creating different learning experiences for the same objective.

What is adaptive learning in machine learning?

Adaptive learning, also known as adaptive teaching, is an educational method which uses computer algorithms as well as artificial intelligence to orchestrate the interaction with the learner and deliver customized resources and learning activities to address the unique needs of each learner.

What is adaptive learning solutions?

Adaptive learning — or adaptive teaching — is the delivery of custom learning experiences that address the unique needs of an individual through just-in-time feedback, pathways, and resources (rather than providing a one-size-fits-all learning experience).

What are the five steps of machine learning solution?

5 Steps to Understand and Implement Machine LearningUnderstanding machine learning technology.Preparing the foundation.Implementing baseline solutions.Applying existing technology in new ways.Exploring bespoke algorithms.

What is an example of adaptive learning?

For example, if a math student struggles to answer grade-level questions about long division, the adaptive learning program will provide assistive scaffolding. The program may also use differentiated instruction principles, delivering content that the student can capably process.

What is adaptive technology explain how it can be used in the learning process?

Adaptive technology empowers teachers with the ability to easily and continuously improve their instruction, equiping them with analytics and authoring tools to better understand their students' learning and modify their lessons accordingly.

What are the 7 steps to making a machine learning model?

It can be broken down into 7 major steps :Collecting Data: As you know, machines initially learn from the data that you give them. ... Preparing the Data: After you have your data, you have to prepare it. ... Choosing a Model: ... Training the Model: ... Evaluating the Model: ... Parameter Tuning: ... Making Predictions.

What are the 3 key steps in machine learning project?

There are three types of machine learning: Supervised Learning, Unsupervised Learning and Reinforcement Learning....Split up your dataset in three parts: Training, Testing and Validation.Training data will be used to train your chosen algorithm(s);Testing data will be used to check the performance of the result;More items...•

What are steps in designing machine learning application?

The 7 Key Steps To Build Your Machine Learning ModelStep 1: Collect Data. ... Step 2: Prepare the data. ... Step 3: Choose the model. ... Step 4 Train your machine model. ... Step 5: Evaluation. ... Step 6: Parameter Tuning. ... Step 7: Prediction or Inference.

Abstract and Figures

In the web-based system for assessment we use machine learning algorithms for modeling students’ knowledge. We applied clustering of students in similarity groups, attribute (or the most relevant exercises) selection and classification (of students in ability groups) on three different domains.

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Abstract

There is a broad consensus that digital learning environments can facilitate personalized learning processes. For the past several years, adaptive learning has been promoted as a potential solution for transforming higher education.

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ResearchGate has not been able to resolve any citations for this publication.

What is Machine Learning?

Machine Learning (ML) is a subset of Artificial Intelligence that uses statistical techniques to give computer systems the ability to “learn” from data previously collected.

How can Machine Learning be used in education?

Due to the learning ability of this technology using data and discovering patterns, machine learning could drive the implementation of adaptive learning functionalities in Learning Management Systems (LMS).

What is Adaptive Learning?

Adaptive learning is a trend in education that promotes the personalization of learning content using appropriate technology, such as Machine Learning. Adaptive learning is the strategy of adjusting the path and pace of learning to each learner’s needs.

What does eLearning have to do with Machine Learning and Adaptive Learning?

Machine learning technology and its algorithms that collect data to detect patterns and behaviors can be incorporated in eLearning through Learning Management Systems (LMS) to create adaptive learning solutions.

Applications of Machine Learning in Education

Some specific applications of machine learning technology in education are:

Keeping a human in the loop

But please, don’t worry with all this technology. Humans, real educators and instructors are still needed

In Summary

We don’t need to panic, educators, instructional designers, we are all needed to fine tune these technologies and to provide the human touch, perspective and knowledge to the learning experience.

What is adaptive learning?

Adaptive learning uses programming to manage some of the heavy lifting that teaching requires. This technology responds to the level of knowledge a learner displays and presents interactive challenges to help them progress. The best part about adaptive learning is that it works on all levels of education.

Is Matific a good platform?

Matific for a Good Role Model. Although there are other quality math learning platforms on the market, Matific is something worth mentioning as well. It offers more than just a regular lecture/demonstrate assignment structure for students to grow on.

Does adaptive technology work?

Adaptive technology doesn’t only work for material as complicated as a CPA review course. It also works well for students in higher education. One specific example is the courseware provided by Knewton on its Alta platform.

What is adaptive learning?

An adaptive learning system will contain the same types of materials. But the order will change for each learner. The system decides which content to show the learner based on two things: If the learner's goal is only a subset of the content, the system can limit the content. The prior knowledge also comes into play.

What was the first computerized adaptive testing system?

An early and influential computerized system was the Lisp tutor , also known as LISPITS (1983) at Carnegie Mellon University.

What is the learner element in a graph?

The learner element is a model of the learner's current state of ability. So for each given node in the expert graph, the learner model has a probability associated with it: 1-99%. The system updates this graph every time the learner performs an activity. If a learner answers a question correctly, the probability increases. If the learner answers incorrectly, the probability decreases.

Why does myelinate help the brain?

Your brain with myelinate that pathway -- strengthen the myelin around the axon to support electrical signals. Because of the strengthened myelin, this path will be more likely to fire in the future. In other words, you learn. Even in the smallest scale, our brain is a massive graph of connected neurons.

What does the tutor do after each update to the learner model?

After each update to the learner model, the tutor will update the path it will take to optimize for that learner.

What happens if a learner answers incorrectly?

If the learner answers incorrectly, the probability decreases. Each learner has their own learner model. So each time there's a new learner in the system, there's a new learner model. Later, the tutor will use the learner model to decide how to order the learning content.

What is an expert model?

The expert model is a large, connected graph of everything you want the learners to know. As the name suggests, you have an expert on the topic -- or experts on topics -- to create the model. This model is static. The expert model only changes when the scope of learning outcomes change.

What is adaptive learning?

Adaptive learning allows you to address the learning needs of each, leading to deeper and more efficient training than a one-size-fits-all approach.

What are some common training situations in which adaptive learning may (or may not) be a viable approach?

Here are some common training situations, in which adaptive learning may (or may not) be a viable approach: Teaching people who know what they don’t know. Teaching people with differing skills and interests. Teaching people whose needs differ by group (department, job function, etc.)

What is a business course?

A course to teach business skills to a large audience of health care professionals includes a self-assessment in the first module designed to help them identify areas of learning need, which is then targeted in subsequent instruction. The idea is to draw their attention to shortcomings in their skill set, which motivates them to pursue topics at greater depth.

What is a trial monitoring course?

A course to teach trial monitoring guides learners along with a compelling third-person linear narrative through various stages of a trial, with optional links ("Tell me more...") along the way. These allow learners to dive deeper into parts of the story they want to know more about. A law student may want to know more about a particular legal concept, while a retired judge may already know and skip it.

What is screen learning?

Screen learners to identify their role, department, job function, and so on (either by asking them directly at the beginning or by pulling up profile information automatically), then adjust training accordingly.

Is adaptive learning a panacea?

Adaptive Learning Is Not A Panacea. First, though, a warning. Adaptive learning is not always the right choice. There are times where it is useful, and times where it gets in the way. And, there are different ways to achieve it, depending on the situation. If you’re carrying something in the office and slide on a slippery spot on the floor, ...

Can You Create Adaptive Learning?

Some market their platform’s unique capability to provide adaptive learning by touting Artificial Intelligence (AI) or similar magic to achieve performance ends.

Yes, You Can!

There are a variety of ways you can apply adaptive learning to your online training.

So What Are You Waiting for?

These are just 6 examples of how you can add adaptive learning to your training design, without having to invest in risky technology, hire specialized (and expensive) technical expertise, or spend more time developing content than you need.