Before we start on adaptive learning technology, a reminder: one of the things we always tell customers is that employee security awareness is broken. One reason is that employees are not engaged with training content, mainly because most training programs are “one size fits all” and don’t address individual needs, previous knowledge, and especially paces and schedules.
And corporate training is something that organizations spend vast amounts of money on without getting the returns on investment as companies would hope. A study found that only 10% of corporate training is effective.
One of the most significant leaps in our view of cybersecurity training (and, frankly, corporate training in general) is the use of adaptive learning to improve user engagement and deliver tangible benefits to security leaders and organizations alike.
But what’s adaptive learning technology, and how can it help drive better results in cyber awareness?
What is Adaptive Learning Technology?
Adaptive learning is a mode of learning that utilizes technology and data to deliver education and training customized to each learner. It works with user input, from profiling questions to data from continuous use, to make the content more specific to the users’ needs and knowledge gaps. It also gives the users freedom to pursue learning at their own pace and schedules, personalizing the entire experience.
Adaptive learning technology is born from a need to address the inefficiencies of the previous learning models. Rather than sticking to a “one-size-fits-all” syllabus, which teaches every learner the same way, Adaptive Learning customizes the path to increase knowledge retention and stimulate interest in learning.
Although adaptive learning is primarily used in education, another prevalent use happens in the corporate world through corporate training. The methodology is heavily recommended for organizations, as the employees can learn more effectively.
Traditionally, corporate learning is a mandatory expense, grossly overlooking its effectiveness. However, as internal policies have multiplied, public agents pushed for more compliance, and data security became more at risk for cyberattacks, learning ROI became more prominent.
That is why adaptive learning technology has become so important: to match the rising tide of corporate content with employees with other priorities, therefore with little time and attention to spare. And the numbers back it up: according to a paper published by the International Journal for Quality Research, several studies have found evidence to highlight the superiority of adaptive learning platforms over non-adaptive learning methods.
What Makes up Adaptive Learning Technology?
A traditional Adaptive Learning platform has three main components. However, gamification often is added as the fourth one. It is a way to address the need for users to engage deeply with the content and gain motivation to push through and beyond.
- Learning Component (Expert Model) – The learning component will contain a repository of all training-related content and the learning paths that the users could potentially take. The learning component will convert the content library to a knowledge graph (something used to showcase interrelated elements), driving learning based on prerequisites and paths determined by the user journey.
- Learner Component (User Model) – This model is a repository of a learner’s profile, performance, behavior, and gamification attributes. It contains all the information about the learner. It determines the user’s skill levels to optimize their learning path. It stores information like ID information, knowledge level, program performance, usage behavior, and other details that make a unique learner “DNA.”
- Teacher Component (Tutor Model) – The component incorporates existing knowledge, difficulty level, and topic diversity of a learner to suggest a training activity that will help learners either maintain their current knowledge or add a new dimension in their knowledge repository. The teacher model uses the information from the previous two models to find an optimal learning path.
- Gamification Component – Gamification involves applying typical game-playing elements such as scorekeeping and leaderboards into a system to enhance its effectiveness. It has significantly improved user engagement levels in adaptive learning scenarios. By incorporating such features into adaptive learning platforms, there would be more incentive for the users to undertake the activity, and hence they would participate more actively. Organizations can use this component to identify what intrinsically motivates every user and use it to customize the assessment to further appeal to them.
How can Adaptive Learning Benefit Your Organization?
Now that we have an understanding of what Adaptive learning is and its different components, here are a few ways in which adaptive learning can benefit your organization.
- Eliminates learning loops – A problem that plagues traditional learning methods. A learning loop means that if a learner got a question or a set of questions wrong, they have to repeat the same questions without any support. This loop would result in frustration from the learner and would discourage them from learning. With AI-powered adaptive learning, they would be able to receive some feedback which would help facilitate learning.
- Greater Time Efficiency – A significant benefit of adaptive learning is that it helps employees save time taking these assessments. Since the process is adaptive, an employee won’t have to undertake training related to subject matter that they already know. Since the employees won’t be repeating things, they would be more energized and less bored, leading to increased productivity and knowledge takeup.
- Greater Cost Efficiency – A research report has found that companies that use Adaptive Learning spend 27% less in Learning and Development and deliver far greater business outcomes. The logic behind this is quite simple. Instead of making every user go through the same training, if an organization makes different learners take different courses (based on pre-existing knowledge), they would save up their time while improving content retention.
- Continuous improvement – The system of adaptive learning improves itself because it will keep adjusting (or adapting) itself to better refine the learning path for the user. In addition to improving itself, it can provide valuable insights and analytics related to performance. The supervisor can use these insights to help the employees focus on their weaknesses and improve on them.
- Increases motivation – The presence of gamification elements incorporated into an adaptive learning approach will increase learners’ motivation to take up new assessments and score higher. A bit of friendly competition between the employees will be conducive to learning and result in higher engagement.
Prominent Examples of Adaptive Learning Technology
Adaptive learning is present in more than typical classrooms. Companies are using it to drive user improvement in unique, customized journeys. Two prominent examples include:
- Nike – Nike’s fitness app uses adaptive learning to gauge the fitness goals, training habits, and current standing of new users. With that information, plus continuous app usage, Nike can deliver more personalized content tailored to improve that user uniquely.
- Duolingo – A popular language learning app, Duolingo uses adaptive learning technology to deliver tasks assigned to the learners that best suit their capabilities.
Adaptive Learning Technology in Cybersecurity
Time and time again, it has been proved that humans are the most vulnerable factor in cybersecurity. Due to this, cybersecurity awareness training is essential for employees everywhere. Given that cybersecurity knowledge amongst employees will not be consistent, we would need an adaptive syllabus. Due to this, organizations could use adaptive learning to impart customized knowledge to different employees.
Adaptive learning can ensure that the employees are efficiently getting knowledge. Organizations do not have to worry about employees learning what they already know because it would be unique for every employee. Right-Hand also uses assessments and a micro-learning approach to ensure longer retention of subject material.
By adding in adaptive learning, companies can help mitigate a significant portion of the human risk element that is present. Employees will be more aware of what they should and shouldn’t do once they undergo training like phishing readiness and compliance readiness training.
Finally, adaptive learning and its benefits create a compound effect, where cybersecurity budgets dedicated to hardware and software solutions (such as firewalls) are optimized. Once an organization learns where the weak spots are, it can direct its resources to work together with human readiness.
That said, investing in adaptive learning drives higher engagement and retention among users while providing security leaders and managers a highly effective human risk mitigation.