What is adaptive learning?
Adaptive learning is a mode of learning that utilizes algorithms to deliver training customized to each person’s unique learning style and level of knowledge. It 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 in the same way, adaptive learning results in increase knowledge retention and may even stimulate an interest and eagerness to learn more.
As public agencies and consumers push for more data security and compliance, training employees has become a crucial part of successful business enterprises. Instructing employees can minimize the risks for cyberattacks and data breaches.
While adaptive learning has traditionally been used in educational environments, it can provide the solution that many companies seek because it is cost effective and can be accomplished much more succinctly than traditional methods of instruction. 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.
Corporations who invest in adaptive learning technology will find a much greater return on their investment as employees will be more willing to participate in the training and will retain what they learn.
How does adaptive learning work?
While traditional adaptive learning contains three components, Right-Hand Cybersecurity has added a fourth component – Gamification – to make learning fun and engaging.
- 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 users journey.
- Learner Component (User Model) – This model is a repository of a learner’s profile, performance, behavior, and gamification attributes. It determines the user’s skill levels to optimize their learning path. It may stores information such as user ID , knowledge level, program performance, usage behavior, and other details that make a unique learner “DNA.”
- Teacher Component (Tutor Model) – This component incorporates existing knowledge, difficulty level, and topic diversity of a learner to suggest a training activity that will help the learner 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 is more incentive for the users actively participate in the training. Organizations can use this component to identify what intrinsically motivates each user and then customize the instruction for each participant.
How can adaptive learning benefit your organization?
- Adaptive learning eliminates learning loops that plague traditional learning methods by forcing the learner to repeat the instruction if questions are answered incorrectly. This may be frustrating and discouraging to the user. With AI-powered adaptive learning, the user receives feedback to reinforce the concepts that need to be reviewed.
- Adaptive learning saves time. Since the process is adaptive, employees won’t have to undertake training related to subject matter that they already know. By focusing on new concepts rather than repeating those already mastered, employees may be more energized and engaged in the instruction, have a willingness to participate, and succeed in retaining the information.
- Adaptive learning is cost efficient – A research report by Joshbersin Academy found that companies that use adaptive learning spend 27% less on staff development and education. Because adaptive learning is individualized, employees can focus on the topics and skills they need. This may reduce boredom and increase retention.
- Adaptive learning continuously improves itself to better refine the learning path for each user. It also provides valuable insights and analytics related to performance. The supervisor can use these insights to coach employees to remediate weaknesses and improve performance.
- Adaptive learning increases motivation with the incorporation of gamification. Employees can challenge themselves to get a higher score or have a bit of friendly competition with their peers.
Adaptive learning in cybersecurity
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Right-Hand’s adaptive learning can save organizations time and money.
The human element is the most vulnerable factor in cybersecurity which makes awareness training essential for employees everywhere.
Similar to Nike’s fitness app and Duolingo’s language training application, Right-Hand’s Ally allows the user to learn at his or her leisure and at his or her own pace. The information can be obtained in short lessons of 10 to 15 minutes. This eliminates the need to spend hours or days training employees on compliance requirements and cybersecurity risks.
Organizations can use adaptive learning to impart customized knowledge to different employees.
Right-Hand’s micro-learning approach can help mitigate a significant portion of the human risk element in cybersecurity.
Finally, adaptive learning and its benefits create a compound effect, where cybersecurity budgets dedicated to hardware and software solutions are optimized. Once an organization learns where the weak spots are, it can direct its resources to work together with human readiness. In