The complete guide to adaptive learning : Why and how to develop skills in a personalized way ?

06.12.2023

Introduction to Adaptive Learning

The modern training landscape and the role of Adaptive Learning

In today’s corporate world, the training landscape has evolved considerably. It’s no longer just about standardized lectures, manuals and courses. Indeed, these traditional learning methods have given way to more innovative approaches adapted to our digital age.

E-learning, for example, has become a common method of training employees in a variety of skills and concepts. From online platforms to mobile courses and virtual reality training, companies are finding new ways to engage their employees in learning. This transformation is driven by a desire to meet the changing expectations of modern learners, who want learning to be more flexible, interactive and accessible anytime, anywhere.

In the midst of these new approaches, adaptive learning has emerged as a powerful means of improving training effectiveness. Unlike traditional training approaches that adopt a common approach to all learners, adaptive learning understands that each individual has unique learning needs. It therefore strives to personalize the learning experience for each learner.

The fundamental principles of Adaptive Learning

Adaptive learning is based on a number of principles that make it unique and particularly effective in improving learning outcomes.

One of the main principles of adaptive learning is the personalization of learning. Unlike traditional learning, which follows a common approach, adaptive learning recognizes that every learner is different. It therefore takes into account the learner’s prior knowledge, skills, learning preferences and even pace to adapt the content and learning path.

This means that if two learners take the same course on an adaptive learning platform, their experiences will be different. For example, if one learner shows a solid understanding of a concept, the platform may decide to present more advanced content or move on to a new topic. On the other hand, if another learner is struggling with the same concept, the platform may decide to present the material in a different form, or give them more exercises to reinforce their understanding.

This principle is particularly useful in a learning context, as it keeps learners engaged and optimizes learning efficiency. By adapting content to the specific needs of each learner, adaptive learning enables each individual to progress at his or her own pace and master skills more effectively.

Why Adaptive Learning is crucial in the world of corporate training

In the world of corporate training, organizations face a number of challenges. On the one hand, the rapid pace of technological change requires constant updating of employees’ skills. On the other hand, the diversity of learners, in terms of prior knowledge, learning styles and learning rhythms, makes it difficult to design training programs that effectively meet everyone’s needs.

This is where adaptive learning comes in. With its ability to personalize the learning experience for each individual, adaptive learning offers an effective solution to these challenges. By adapting the content and pace of learning to the specific needs of each learner, adaptive learning can help employees acquire the skills they need faster and more effectively.

Adaptive learning can also improve learner engagement. By providing content tailored to their skill level and learning style, adaptive learning can make learning more interesting and motivating, which in turn can lead to better learning outcomes.

For example, in the case of adaptive learning leadership training, experienced managers who already have a solid understanding of leadership principles might be directed towards more complex modules, such as transformational leadership or crisis management. On the other hand, those new to leadership roles could start with basic modules, such as effective communication or time management.

Cybersecurity is another good example. With phishing and social engineering threats becoming ever more advanced and difficult to detect, it is vital to raise awareness among all employees in order to protect the company’s systems. But not everyone is at the same level of understanding of the subject. Here, Adaptive Learning is perfect for delivering a cost-effective training program that adapts to each individual’s level and saves everyone time. People who know how to recognize threats will be able to complete the training much more quickly and save time, while less familiar employees will be able to develop their skills in depth.

By personalizing the learning experience, enhancing learner engagement and responding effectively to individual learner needs, adaptive learning plays a key role in training tomorrow’s employees, and in the effective management of a company’s time and budgets.

Understanding the Psychology of Learning for Adaptive Learning

The foundations of cognitive psychology for Adaptive Learning

Cognitive psychology is a branch of psychology concerned with the mental processes involved in learning and memory. It is at the heart of adaptive learning, as it helps to understand how individuals absorb, process and retain information, which is essential for creating effective learning experiences.

Cognitive psychology is used in adaptive learning to guide content and learning path design. By understanding how the brain works, learning designers can create experiences that maximize the memorization and application of new knowledge.

One of the key concepts of cognitive psychology that is often taken into account in adaptive learning is that of cognitive load. Cognitive load refers to the amount of information that working memory can process at any given time. If a task is too complex and overloads the working memory, the learner may have difficulty understanding and retaining the information.

Adaptive learning uses this concept to adjust the complexity of content according to each learner’s ability. For example, if a learner has mastered a topic, the system may present more complex information to challenge him or her. On the other hand, if a learner struggles with a topic, the system can simplify the information to reduce cognitive load and help the learner understand.

How do adults learn? Implications for Adaptive Learning

Andragogy, or the science of adult learning, proposes several key principles that define how adults learn. According to Malcolm Knowles, the father of andragogy, adults are autonomous and self-directed, accumulate a base of experience and knowledge, are oriented towards learning that is directly related to their working lives, and are motivated by internal factors such as personal satisfaction.

Adaptive learning is perfectly aligned with these principles. It enables a self-directed approach by giving learners control over their learning, allowing them to progress at their own pace and according to their own objectives. What’s more, adaptive learning recognizes and values learners’ prior knowledge and experience, adapting content to their current skill level.

In the professional context, adaptive learning offers relevant learning by focusing on skills and knowledge that can be directly applied in the workplace. What’s more, by providing real-time feedback and enabling learners to see their progress, adaptive learning also stimulates internal motivation.

Different learning styles and their impact on Adaptive Learning

It is generally accepted that there are three main learning styles: visual, auditory and kinaesthetic. Visual learners prefer information presented in the form of graphs or diagrams, auditory learners benefit from verbal or aural information, and kinaesthetic learners learn best by physically doing or manipulating things.

Adaptive learning can take account of these different learning styles by personalizing the way information is presented. For example, for a visual learner, an adaptive learning platform might use videos or infographics to explain a concept. For an auditory learner, the same concept could be explained through a podcast or audio narration. And for a kinaesthetic learner, adaptive learning could offer simulations or interactive activities.

By adapting to each learner’s preferences, adaptive learning can improve engagement, comprehension and retention. That’s the beauty of adaptive learning: its ability to meet the individual needs of learners, whatever their learning style.

Personalizing learning through Adaptive Learning

Personalization of learning is a concept that emphasizes the importance of creating learning experiences tailored to each learner’s unique needs, interests and abilities. This means giving learners a degree of control over their learning journey, offering content that is relevant to them, and helping them to achieve their personal learning goals.

Adaptive learning facilitates this personalization by using artificial intelligence to adapt the content and learning path for each learner according to their performance and responses. It can, for example, offer more complex challenges to those who have already mastered a subject, or provide extra review and support to those who are struggling.

This personalization has profound implications for skills development. It not only ensures that learners acquire the skills they need, but also does so in a more effective and engaging way. Ultimately, adaptive learning enables every learner to reach his or her full potential.

Real-time feedback and Adaptive Learning

Real-time feedback is a crucial element of learning. It enables learners to immediately understand what they are doing well and where they need to improve, helping them to adjust their efforts and progress more effectively. What’s more, real-time feedback can boost learners’ motivation and commitment by showing them that they’re making progress and giving them immediate satisfaction.

Adaptive learning offers real-time feedback by using AI to analyze learners’ performance and provide immediate feedback on their responses. This can take the form of explanatory comments, corrections or suggestions for improvement.

The impact of real-time feedback on skills development is enormous. It enables learners to correct mistakes quickly, deepen their understanding, and boost their confidence in their skills. In this way, adaptive learning accelerates skills development and improves learning efficiency.

Skills-based approach to Adaptive Learning

The skills-based approach to business in education and training focuses on demonstrating the specific skills and knowledge needed to succeed in a given task or job. Instead of focusing solely on the transmission of information, it concentrates on the development of applicable and measurable skills.

Adaptive learning aligns perfectly with this approach. Using algorithms and AI, adaptive learning can track learners’ progress, identify their strengths and weaknesses, and adapt content to focus on skills that require more development. It can also provide practical scenarios and simulations to help learners apply their skills in practical ways.

The competency-based approach has many advantages for corporate skills development. It ensures that employees develop skills directly applicable to their role, improves training efficiency, and helps companies better align their training with their strategic objectives.

Advantages and disadvantages of Adaptive Learning for corporate training

Advantages of Adaptive Learning

Thanks to its approach based on psychology, adaptability and AI, Adaptive Learning offers numerous advantages for learners and trainers alike.

Adaptive Learning improves training efficiency
Adaptive Learning increases learners’ retention of information
Adaptive Learning increases learners’ commitment to their training, since they don’t have to “go over” things they already know.
Adaptive Learning ****offers a “competency-based” approach to meet business needs.
Adaptive Learning makes it possible to track learner data and progress.
Adaptive Learning enables learners to progress at their own pace
Adaptive Learning saves between 20% and 70% of training time for each learner (37% on average according to our analyses).

Disadvantages of Adaptive Learning

But nothing is perfect, and Adaptive Learning and the solutions available on the market do have a few drawbacks.

The initial investment for a platform like Domoscio Hub is often higher than for traditional e-learning. However, this initial investment is quickly recouped thanks to the savings in training time for each learner.
Trainers may take longer to get to grips with the platform and the tool, due to the greater design complexity inherent in Adaptive Learning. At Domoscio, we’re with you every step of the way, from setting up the tool to designing your course, to ensure you get the best possible experience.
Last but not least, Adaptive Learning requires a significant amount of instructional design time, since content must be found or generated, and positioning questions created for each piece of content. In general, we’re talking about 2 months of instructional design for each course.

Adaptive Learning technologies and tools for businesses

Overview of Adaptive Learning platforms for companies

An adaptive learning platform is an educational system that uses artificial intelligence to personalize the learning path for each individual. These platforms use data collected during learning sessions to tailor content, pace and level of difficulty to the learner’s specific needs.

For a company looking to implement adaptive learning, it’s crucial to look for certain key features in a platform. Firstly, it should offer robust personalization that can adapt to each learner’s skills and needs. Secondly, it should provide real-time feedback to help learners understand where they are and where they need to go. Finally, it should include powerful analysis tools to help trainers monitor and evaluate learners’ progress.

The role of AI in Adaptive Learning

Artificial intelligence (AI) is a field of technology that focuses on the creation of systems capable of operating and learning autonomously, often by mimicking human thought processes. In the context of adaptive learning, AI plays a key role in enabling the personalization and adaptability of learning content.

Indeed, AI powers adaptive learning by collecting and analyzing learner data, enabling learning content to be dynamically adjusted to individual needs. For example, AI can identify areas in which a learner is struggling and adapt content to focus more on these areas, or suggest additional resources to aid comprehension.

AI offers several advantages in adaptive learning. It enables large-scale personalization, which would be impossible to achieve manually. What’s more, it can offer real-time feedback to learners, which can improve motivation and engagement.

However, AI is not without its challenges. One of the main ones is privacy and data security, as AI relies on the collection of large amounts of data. What’s more, AI can sometimes be perceived as impersonal or unintuitive, so it’s important to combine it with elements of human interaction to deliver a complete learning experience.

AI is undoubtedly a valuable asset for adaptive learning, enabling unprecedented personalization and adaptability in corporate training. However, it is crucial to understand the challenges and implement measures to overcome them in order to make the most of this technology.

How to design effective Adaptive Learning courses

Planning adaptive learning for skills development

Planning an adaptive learning path is a crucial step in ensuring its effectiveness. It involves defining learning objectives and mapping out a course that will enable learners to achieve these objectives in a personalized and flexible way. Here are the Top 5 keys to an adaptive learning project.

To begin with, it’s important to understand the skills required for a specific role or function in the company. These skills define the learning objectives. For example, a customer service role may require problem-solving, communication and stress management skills.

Once the competencies have been defined, an adaptive learning path can be designed to develop these skills. This pathway could include a variety of learning content, such as interactive lessons, videos, quizzes and hands-on activities, which are adapted according to each learner’s needs and progress.

You can refer to the White Paper “Integrating Adaptive Learning” available here for a more in-depth look at implementing Adaptive Learning in business.

Content creation for Adaptive Learning: best practices

Creating content for adaptive learning differs from traditional learning in that it requires greater personalization and adaptability. Here are some best practices to keep in mind :

Flexibility: Content must be designed in a modular way to allow for adjustments and adaptations according to learners’ needs.
Interactivity: The use of interactive elements can help keep learners engaged and facilitate better understanding.
Immediate feedback: Incorporate feedback mechanisms to enable learners to understand their mistakes and learn independently.

Using data to drive adaptive learning in the enterprise

The use of data is at the heart of adaptive learning. By gathering information on learners’ performance, preferences and behaviors, learning paths can be adjusted to best meet their needs.

For example, if the data shows that a learner is having difficulty with a particular concept, the system can adapt the content to focus more on that area. Or, if a learner prefers a certain type of content (e.g. video vs. text), the system can prioritize this type of content in the learning path.

Ultimately, the use of data makes learning more effective and relevant to each individual, helping to maximize the ROI of corporate training.

How to implement Adaptive Learning in your organization

Preparing your organization for Adaptive Learning: A checklist

Preparing your organization for the implementation of adaptive learning requires detailed planning. Here’s a checklist to help you get ready:

Organizational alignment: Make sure all company departments are aligned with the goal of implementing adaptive learning.
Management support: Get the support of the company’s top management. This will facilitate the implementation process and help overcome potential obstacles.
Resources: Make sure you have the necessary resources, including staff, technology and budget.
Training: Provide training for employees on how to use the new adaptive learning technology.

Change management when implementing adaptive learning

Implementing adaptive learning can involve major changes within your organization. You may encounter challenges such as resistance to change, the training needed to use the new technology and adapting to new learning methods.

To manage these changes effectively, it’s essential to clearly communicate the benefits of adaptive learning to all stakeholders, provide sufficient training and secure the support of the company’s senior management.

How to choose the right adaptive learning provider for your organization ?

Choosing the right adaptive learning provider is a crucial step towards successful implementation. Here are a few criteria to consider when making your selection :

Technological capabilities: Does the supplier offer technology that meets your organization’s specific needs ?
Customer support: What type of support does the supplier offer ?
Reputation: What is the supplier’s reputation in the industry ?
Cost: Does the cost of the service match your budget ?

The selection process may involve researching suppliers, comparing their offers, requesting demonstrations or trials and, finally, making a decision based on analysis of the information gathered.

Monitoring and evaluating the effectiveness of Adaptive Learning

How to measure the success of Adaptive Learning in your organization

Evaluating the effectiveness of adaptive learning in your organization relies on monitoring certain key performance indicators (KPIs):

Course completion rate: This KPI measures the percentage of learners who have completed their learning paths. A high completion rate can indicate that courses are engaging and adapted to learners’ needs.
Test scores: Average scores on tests and assessments provide an objective measure of knowledge assimilation. It’s important to examine these scores across the organization and by learner, to identify areas of success and those requiring improvement.
Learner feedback: Encourage learners to give feedback on learning paths. Satisfaction surveys, discussion forums and individual feedback sessions can provide valuable information about learners’ experiences.

Using learning analytics to optimize adaptive learning

Learning analytics can improve the effectiveness of adaptive learning by providing actionable data. Here’s how to use it :

Tracking learning behavior: Modern adaptive learning platforms collect data on how learners interact with content. This can include time spent on each module, answers to questions and navigation paths.
Learning data analysis: The data collected can be analyzed to identify trends, patterns and problem areas. For example, if many learners spend a lot of time on a certain module, this could indicate that it is too difficult or poorly explained.
Data-driven adjustments: Insights gained from analysis can be used to fine-tune learning content. This may involve modifying problematic modules, adding more resources for difficult topics, or changing the order of modules to improve efficiency.

Ultimately, the use of data and analytics can enable learning optimization based on learner performance and preferences.

Case studies and best practices in Adaptive Learning

Engie Customer Case : Individualizing customer advisor training with intelligent positioning

Challenges

Engie’s France BtoC department, more specifically the Consumer Division, is dedicated to selling energy and related services to residential customers in France. One of its major challenges is to offer the best possible customer experience, which requires optimizing the training process for its customer advisors. This ensures that they are well-equipped to meet customers’ needs and expectations. Given the diversity of these advisors’ profiles, individualizing their training paths is essential to adapt them to their specific needs and involve them more effectively in their skills development. At the same time, the aim is to maintain a constant assessment of their knowledge of Engie’s business processes.

Project

To tackle this challenge, Engie decided to experiment with Domoscio Hub and Domoscio Lock solutions during a three-month trial. The aim is to provide its advisors with a personalized training experience, from the acquisition of skills to their long-term consolidation.

As a first step, Domoscio Hub makes it possible to accurately assess each advisor’s level of knowledge by means of a positioning questionnaire. Taking into account the professional requirements defined by Engie, Domoscio Hub designs a learning path including recommendations for personalized pedagogical content, optimizing the time each advisor devotes to training.

Once the required skills have been acquired, Domoscio Lock provides ongoing support to the advisor to ensure that the concepts are memorized and facilitate their practical application in the field.

Benefits

Customer advisors are the face of ENGIE France BtoC’s Consumer Division, so it’s vital to ensure that they master the subjects essential to their professional performance. By using Domoscio Hub and Domoscio Lock, each advisor is offered training content tailored to his or her specific needs, thus fostering commitment and optimizing the time dedicated to training.

Following a three-month pilot phase, the results obtained were as follows :

Proven benefits: 20% improvement in mastery of target skills after 3 months.
High commitment: 95% of advisors followed all the recommendations.
Successful memorization: 100% of employees retained the skills acquired after 3 months.

The future of Adaptive Learning in the enterprise

Emerging trends in Adaptive Learning

Adaptive learning is a constantly evolving field, taking advantage of technological advances to constantly improve the way we learn. Here are just a few of the emerging trends in this field :

Even more advanced AI: Artificial Intelligence (AI) has already revolutionized adaptive learning by enabling large-scale personalization. However, we’ve only just scratched the surface of what’s possible. As AI becomes more sophisticated, we can expect even higher levels of personalization. For example, AI systems capable of understanding the context and nuances of interactions with learners to provide more precise and nuanced feedback.

Immersive Learning : Virtual reality (VR) and augmented reality (AR) technologies offer new ways of engaging learners. They enable the creation of immersive simulations where learners can practice skills in a safe environment that mimics the real world. What’s more, these technologies can also be used to improve engagement and retention by transforming learning into a more interactive and immersive experience.

Microlearning : Microlearning – learning in small units or short durations – continues to grow in popularity. It aligns well with adaptive learning by enabling learning units to be customized to the specific needs of each learner. What’s more, microlearning is well suited to today’s digital age, where attention spans are limited and information is consumed in small quantities.

Social and collaborative learning : Although adaptive learning is often associated with individual learning, there is great potential for integrating elements of social and collaborative learning. For example, by using technologies that enable learners to share their knowledge and experience, or by combining adaptive learning with group learning approaches.

However, it should be noted that implementing these trends will not be without its challenges. Issues of privacy and data security remain a concern, particularly with the increased use of AI and machine learning analytics. In addition, the adoption of new technologies will require culture change and adequate training for trainers and learners.

However, despite these challenges, the future of adaptive learning looks promising, with the potential to radically transform the way we learn and develop skills.

Additional resources :

White papers : Adaptive learning, anchoring: AI in learning in detail

Business Cases : Smart Data and Artificial Intelligence to meet our customers’ challenges

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