Executive Summary: Adaptive learning is designed for an evolving world where workforces are becoming more diverse. Employees from different age groups, ethnicities or backgrounds can be seen working together as a team. A more diverse workforce brings more diverse experiences and skills. This makes training a challenge. Training methods for such an assorted workforce must account for the skill levels of each employee. This is not possible with the ‘one-size-fits-all’ approach of traditional learning methods. Adaptive learning is designed to solve this by delivering custom learning experiences. These experiences address the unique needs of an individual through just-in-time feedback, dynamic learning paths, and resources. The use of adaptive learning programs is expected to grow exponentially in the near future. This blog focuses on what adaptive learning is, how it works, and its positive impact on enterprise training. It aims to serve as a guide for businesses to implement adaptive learning to train their diverse workforce.
Many of us might tend to start a training session motivated to learn something new, only to slog through information we may already know (or that could have been sent in an email). On the other hand, sometimes we might have no context or understanding of the program. These situations can arise due to the one-size-fits-all approach of employee training methodologies. Adaptive learning battles both of these circumstances by making learning more personalized. It is a smarter, more efficient way of training employees that solves the key problems of traditional training. Firstly, let’s take a look at the shortcomings of traditional learning methods.
Why Adaptive Learning is Required for Corporate Training?
At RapL, we have implemented adaptive learning for dozens of fast-growing organizations across various industries. Our biggest learning – traditional training methods are not enough to keep up with the fast-paced and ever-changing world of business. In the process of finding a solution for this, we have compiled 4 major issues with traditional corporate training:
- Instructions are linear, as entire courses and sessions are based on traversing through a list of topics one-by-one.
- A linear testing or evaluation process follows the linear teaching methodology. The prime focus of these evaluations is memory of the latest lessons. This evaluation process neither builds actionable intelligence nor a connection between topics.
- Having a fixed sequence of content and tasks that don’t change based on the skills and needs of the learner. This leads to a non-personalized, one-size-fits-all approach of learning.
- Lack of personalized feedback for learners. Without feedback, learners become frustrated, which leads to disengagement and lack of motivation.
An added challenge is the non-relevance of traditional training methods for new generation employees. Gen Z, born between 1996 and the early 2000s, now accounts for about 25% of working Americans. By 2030, they’re poised to represent 30% of all employees in the U.S. This new generation of learners requires a more adaptive and personalized approach. This trend can be seen in the companies using e-Learning for training and their related success. Nearly 2 in 5 Fortune 500 companies use e-Learning to train their staff.
Adaptive learning takes e-Learning a step ahead. It offers educators and learners flexibility and adds a touch of personalization to the learning experience. It supports employee role transitions, enables innovative teaching practices, and incorporates different content formats to support learner needs. Let’s take a closer look at what adaptive learning is.
What is Adaptive Learning?
Imagine each learner having access to a qualified personal tutor. This tutor is always on call — prepared to help learners anywhere, anytime, master the topic they are working on. The tutor can also quiz learners to sharpen their strengths and overcome their weaknesses. Adaptive learning is based on a similar strategy. It is an educational methodology assisted by technology and focused on personalized learning. Let’s take a look at its origin and development.
Adaptive Learning Theory
In the 1950s, Behaviorist, B.F. Skinner devised adaptive learning as a learning methodology. Skinner constructed a teaching machine that focused on effectively teaching new concepts, instead of memorization. The machine worked by allowing the student to practice new concepts by answering questions. Later in the 70s, artificial intelligence burst into education, and the Intelligent Tutoring Systems (ITS) were born. Their goal was to mimic human tutoring capabilities in an automated and computer based fashion. Since then, the adaptive learning system has evolved steadily to its current form.
A 2016 research paper defined adaptive learning as a process that “dynamically adjusts the way instructional content is presented to learners. The content is based on their comprehension of the material, as revealed in their responses.”
At RapL, we define adaptive learning software as
a software that personalizes learning by using artificial intelligence and machine learning techniques to “adapt” the learning path offered to an individual learner in real time.
The unique feature of such a learning software is the adaptability. This means the software’s ability to ‘adapt’ to a learner based on the learning path they are on. This adaptation may present the learner with learning content different from another learner. Software uses Artificial Intelligence (AI) or Machine Learning (ML) algorithms to adapt to the user.
Learning path is another keyword to understand. It is defined as the set of topics and questions a learner must go through to finish a course. The software can understand the learner’s success by two factors. The speed with which they go through the content and accuracy with which they answer questions. In the next section, let’s look at how adaptive learning software works.
Why use Adaptive Learning?
Technology-based adaptive learning systems help learners save time, which in turn drives productivity. With the salient features that adaptive learning provides, immediate assistance is available for learners. The system provides resources specific to the learner. This makes learning more personalized compared to a one-size-fits-all approach.
An added advantage of adaptive learning is that managers and administrators can analyze the data captured by the software. This data helps identify the needs of individual teams to personalize courses and learning paths further. The adaptive learning technology uses a cloud-based delivery, making it highly scalable. The content is delivered through mobile devices, and can be accessed anytime anywhere. This helps hundreds of employees train simultaneously. At RapL, we have researched and compiled various adaptive learning platforms and examples. Based on our research, we provide a condensed explanation of how adaptive learning software works in the next section.
How Does Adaptive Learning Work?
At a basic level, adaptive learning software assesses a learner’s mastery of a concept or skill in real time. The assessment is used to dynamically adjust the next lesson or practice activity for the learner, to improve learning.
Adaptive learning typically occurs on a web-based platform. The software contains all the important information related to the topics. It works by identifying (in real-time) the particular concepts or skills significant to each learners’ progress. The software can then make calculated decisions for the best course of action for the learner. Each learning path is personalized to the learner based on their unique needs.
Adaptive learning software:
- Evaluate how learners worked through the material.
- Use artificial intelligence and machine learning techniques to evaluate data on the learning paths and performance of previous learners.
- Determine appropriate review or practice activity for each learner.
As a result, individual learners using adaptive learning software will have unique and non-linear paths through the material. For example, a new joiner is presented with the original lesson on product knowledge. However, an existing employee is directed to a refresher on product knowledge once in a while. If the system finds gaps in knowledge, it will remind the learner and present a few practice activities from the knowledge repository.
Adaptive Learning Algorithm: Creating Individual Learning Paths
Adaptive learning has four building blocks, namely knowledge domain, learner model, tutoring model and user interface.
There are a number of real time adaptive e-Learning implementations in the market. The architecture of any Adaptive e-Learning System has certain building blocks. Each of the implementations uses the following blocks in different ways:
- Knowledge domain: Contains the knowledge that needs to be transmitted and taught to learners.
- Learner model: Contains information about the knowledge, capabilities, preferences, learning style, etc. of each learner.
- Tutoring model: Represents the set of algorithms or instructions that make decisions about which content elements, exercises, materials, etc. are presented to the learner to increase retention. The tutoring model bridges the gap between the knowledge and the learner.
- User interface: Allows the learner/user to interact with the system and present whatever content has been selected by the tutoring model.
The difference between the several adaptive learning solutions in the market is mainly their tutoring model, and the technology used. The technology defines how the solution makes decisions about the content to be shown to a specific user.
There are two main possibilities:
Rule based systems
Recommendation based systems
An expert designs the teaching sequence for content to appear.
One or more algorithms analyze what the learner ‘knows’ and what the learner should experience next?
The expert’s model tells the technology how to react in a unique situation and display or repeat content.
Algorithms and machine learning display “the right content at the right time” for students as they learn.
This gives the educator more agency and control over what the learner experiences.
Algorithms such as Bayesian Knowledge Tracing (BKT) (which estimates the rate at which learning occurs) and Item Response Theory (IRT) (model used in psychometry) are used to determine what the learner experiences.
Rule based systems are more useful in scenarios with a specific learning path.
Recommendation based systems are more suited for assigning content from a pool of learning paths.
By using preset rules or algorithms, adaptive learning acts like an instructor having a one-on-one conversation with the learner to make the lessons more personalized and impactful.
Why is Adaptive Learning Important?
Adaptive learning has multiple benefits in corporate training.
- Personalized learning for a multigenerational workforce
Each employee has unique learning agility. Their tenure in the company or their position, their skills, knowledge, and experiences are some factors that set them apart in the workplace. Adaptive learning can cater uniquely to each individual. Besides, Forbes magazine suggests that millennials will constitute more than 75% of the workforce by 2025. Millennial employees are more adept in using digital tools and understand ease of use, innovation, creativity, and flexibility. Training them requires personalized content and individual learning paths. Adaptive learning is designed to deliver training content that can even help millennials engage better.
- More efficient training – less time off the job
Adaptive learning’s personalized approach reduces the time taken by the typical learner to achieve mastery, compared to other learning approaches. There is no need to focus on what people know already. Instead, adaptive learning focuses on where they need to become competent. For workers in fields such as sales, call centers, retail, or nursing, for whom time off the floor can be challenging, efficient training can directly reduce time-offs and hence contribute to company earnings.
- More flexible learning environment
It’s crucial to understand that different employees have different learning requirements and schedules. Adaptive learning is a cloud-based tool that can be accessed through laptops and mobiles. This gives learners the flexibility to access learning material based on their schedule and location – at home, during lunch, or even while commuting. This can significantly improve motivation and engagement to complete training modules.
- Helps determine the focus areas
Adaptive learning provides extensive data to leaders and L&D teams. This data helps L&D teams reassess the relevance and impact of the course material. Deep insights and analysis of completion can be instrumental in creating impactful courses. The L&D team also receives real time progress data of employees, which can help provide support where needed.
For example, a sales employee has a higher completion rate for behavioral content, and a lower one for product knowledge. This could imply a limited understanding of the product. In the longer run, this can affect the total sales figures of the company. However, with the proper data, the HR team can make necessary changes to the module to emphasize product knowledge. This will help improve the employee’s product knowledge and the company’s sales revenue.
This means adaptive learning systems are an aid to the learner, as well as L&D teams. It works as a bridge between the two to derive the most value from training.
- Improved Skills
The question-based approach used by adaptive learning helps establish what the learner knows and the areas they require training in. This process is used to create learner profiles, depending on the data generated. The analysis is then used to develop personalized learning content to develop skills. This content helps the learner focus on areas that require improvement.
How do you use adaptive learning to train employees?
Adaptive learning is the future of employee training, as it gauges the ability of each employee and personalizes the learning path. This is done to challenge them based on their unique learning needs. Using artificial intelligence based adaptive learning systems greatly helps L&D teams. It can provide personalized learning, a tailored path and pace of learning with minimal efforts. In the long run, this approach can help employees train more effectively and efficiently.
Adaptive learning provides a more interactive and engaging approach to enterprise training. It enables employees to learn efficiently, unlike traditional methods. At RapL, we have built learning solutions for various industries. We have extensively researched what the market currently has to offer to help create a company’s personalized learning needs. These offerings can be condensed into five broad options:
- Artificial Intelligence: programmable machines/software that take up human tasks of assigning and altering learning paths based on simulated human intelligence and discernment.
- Microlearning: an instructional unit that provides a short engagement in an activity intentionally designed to elicit a specific outcome from the participant. Specific learning paths can be designed.
- Branching content: different routes within the course content based on the learner’s responses.
- Content recommendation: A system to give suggestions that might be of interest to the learner.
- Audience segmentation: The act of dividing learners into subgroups based on certain criteria, such as communication behaviors, learner skill or expertise, demographics and the like.
While each of these options has its pros and cons, they can be used individually or clubbed for better results. To generate the best results, a comprehensive strategy is required for your workforce.
How RapL Uses Adaptive Learning
In the past, RapL worked with a B2B trading platform that brings manufacturers, traders, retailers and wholesalers to a single platform. The problem for this client was to provide relevant learning material for its employees and stakeholders. There were a set of unique challenges in this situation:
- Different content was required for permanent employees, users and contract workers.
- There was no system to check if the users went through the content.
- The content had to be present for all users anytime, anywhere.
For this client, RapL provided an adaptive learning solution with microlearning content. The adaptive learning environment allowed specific content to be displayed to specific users. Microlearning content was more impactful and quicker to consume, which helped increase course completion among users.
Read more about it in detail here.
Why should a company consider adaptive learning methods to train their employees?
Over the course of the blog, we have established that adaptive learning is an essential L&D technology for now and the future. Adaptive learning is useful to improve learner outcomes and make training more accessible and effective for all employees. Adaptive learning is here to stay and will be increasingly adopted in corporate training environments to manage training challenges.
With a tailored and personalized approach, adaptive learning offers unique, flexible, effective and engaging learning experiences, in addition to a positive ROI.
Organizations from various industries are challenged to provide continuous development that is effective and personalized. With its data-centric approach, adaptive learning is exponentially growing to enable companies to keep pace with the evolving learning dynamics.
At RapL, we are continually helping industry leaders improve enterprise learning and drive productivity through everyday learning. Log on to getrapl.com to learn more about adopting smarter ways to enhance workforce training.
We have compiled a visual summary of the blog that you just read. Download it now