Why You Get Personalized Learning Wrong And How To Fix It With Learning Analytics

Why You Get Personalized Learning Wrong And How To Fix It With Learning Analytics
Summary: Personalized learning is becoming increasingly popular due to its ability to improve the quality of learning experiences and increase employee productivity. However, there are some common mistakes that can prevent your personalized training program from getting the results you want. Learn the 3 most common mistakes L&D professionals make when it comes to personalized learning and how you can use learning analytics data to fix them.

Learning Analytics Helps In Creating A Personalized Learning Path For Modern Learners

Quality learning experiences are high in demand [1]. They are no longer a nice-to-have option. Employees expect and actively seek out the companies that provide them. In fact, a staggering 93% of employees would stay at a company longer if it invested in their careers [2].

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Learn how to create and optimize learning experiences by analyzing learners' patterns and behaviors.

However, today's workforce is full of diverse learners that have different skills, abilities, roles, locations, and learning preferences. Creating learning programs that appeal to the modern workforce is one of the toughest challenges L&D teams are currently faced with.

Personalized learning, when implemented correctly, provides the perfect solution. Personalized learning is an instructional approach where the curriculum, content, format, and delivery method are optimized to meet each individual student’s needs. This method engages learners and quickly improves their skills by providing employees with a customized learning plan based on their learning patterns. This also increases both knowledge retention and application.

Unfortunately, there are many mistakes Learning and Development teams tend to make when it comes to personalized learning. To help you better understand how to effectively use personalized learning, we have put together this article on the 3 most common personalized learning mistakes and how they can be fixed using learning analytics.

What are most people doing wrong when it comes to personalized learning?

1. Starting At The Beginning

Most training programs start at the beginning and this makes sense because this is the way most things are done. However, a personalized learning program needs more information about the learner to truly become customized to their needs.

The truth is that some information can be gathered without an assessment, but it is not enough to determine the most effective materials and training delivery methods.

A more effective way to gauge a learner’s preferences and needs is to begin with an assessment and use learning analytics so you can make data-informed decisions. This is important even with online learning programs that are adaptive. Getting a clear baseline is essential before you can move forward.

Learning analytics can clue you into what concepts your employees already know, what they don’t, and if they need a refresher course to help them remember relevant information before they begin. This way, they start exactly where they need to. This prevents learner frustrations and increases engagement.

Learning analytics is the collection analysis and reporting of learner data while using machine learning for the purpose of understanding how to improve their experience. The best personalized learning programs collect data about learners in the beginning to improve learning experiences.

2. Providing Too Many Options And Information At Once

It is important that the training content provided makes sense for the individual, their role, and their learning objectives. Providing content that is too general to be relevant or too specific is a quick way to lower learner engagement. Employee engagement is essential for your organization's success. According to a study by Gallup, only 13% of employees worldwide are engaged at work [3].

Learning analytics can be used to determine which content is the most relevant for each employee. For example, learning analytics can be used to help you divide your audience into different segments and groups. Then managers can assign relevant content to that segment of learners. This keeps employees engaged and prevents information overload.

3. Relying Solely On Online Learning

Personalized learning does not strictly mean online learning. This is a common misunderstanding. Effective personalized learning is usually paired with a blended learning strategy. This includes educational technology and many different formats to instruct students and guide them through the learning process.

Learning analytics can give L&D managers insights and information that will help determine which topics students may learn best in a social or instructor-led environment. It can also be used to identify areas where individuals may be struggling or may have a different learning style. These insights will give L&D teams the tools they need to better assist learners.

The Difference Between Personalized Learning And Self-Led Learning

Although modern Instructional Design techniques often use both self-led learning and personalized learning strategies, they are different. Self-led learning is where the learner takes the initiative and has more choices in which content they learn about and when. Personalized learning creates individual learning paths.

AI and advanced analytics are used to create these learning paths to ensure that learners are not only learning the content most relevant to them but also that the content is delivered in a way that makes the most sense for that learner.

For example, all employees need to take compliance training, however, one employee may only need a refresher course and another employee may need the content to be delivered in a different language. The content can also be tailored to assist those with learning disabilities. For example, an employee with dyslexia may find video content more useful than written content. Personalized learning takes these factors into consideration when creating learning paths.

During self-led learning, learners may have the option to choose topics, training materials, delivery methods, and when they participate in training. This strategy is less useful for compliance purposes. It is not the ideal option if you need employees to be all on the same page regarding a certain subject. In these cases, personalized learning would be a more effective strategy.


68% of employees say that training and development is a company’s most important policy [3]. This is one of the many reasons it is not only important to provide training to employees but to provide quality learning experiences.

Personalized learning is one way you can level up your employee training game. Today’s workforce is diverse. They come from many different backgrounds, have different knowledge, learning patterns, and preferences. Many employees are working from home or in different locations across the globe.

Learning analytics can help your organization ensure that they are using data to create personalized learning experiences that will make an impact and increase productivity.

To learn more about how learning analytics can improve your employee training programs, read the eBook The Power Of Learning Analytics: Measuring L&D Outcomes For Business Performance. If you're curious about how to use learning analytics for remote workforce engagement, join the webinar, too!


[1] How Learning Experiences Will Change in 2020: An Interview with Nolan Hout

[2] 2018 Workplace Learning Report

[3] The Worldwide Employee Engagement Crisis