How Predictive Learning Analytics Helps Deliver Successful Training Programs

Written by Madisyn Villamil

Predictive learning analytics (PLA) allows L&D teams to reduce scrap learning in their programs. Organizations have realized most of what’s learned in training programs isn’t retained or used on the job, otherwise known as scrap learning. PLA combats this by helping companies anticipate individuals’ future learning outcomes.

As with any modern business investment, L&D professionals are turning to analytics to maximize the ROI of their training programs. PLA allows them to improve training efficiency, boost employee engagement, and increase on-the-job use of new skills. 

PLA uses historical and present data to make predictions about individual learners. From highlighting intervention opportunities for at-risk learners to singling out inefficient course components, PLA helps you anticipate and avoid training inefficiencies.

Let’s dive into the science behind PLA and how you can use it to improve your training programs. 

What is Learning Analytics

Learning analytics looks at data about learners’ interactions with course material to understand their performance and improve learning outcomes. The four types of learning analytics are:


Descriptive analytics indicate what is occurring, such as an employee failing an assessment.


Diagnostic analytics explore the why behind an occurrence. For example, further analysis finds the employee didn’t complete all of the training modules designed to prepare them for the assessment.


Predictive analytics gives insight into what is likely to occur in the future. If the instructor had access to predictive analytics, they could’ve inferred the employee had a higher probability to fail due to incomplete modules.


Prescriptive analytics helps instructors find solutions to what should be done. Let’s say the instructor surveyed the employee and found they had insufficient time to complete the training modules. Going forward, the instructor knows they should allot more time to training courses. 

The four types of learning analytics: descriptive, diagnostic, predictive, and prescriptive
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L&D professionals rely on learning analytics to evaluate the effectiveness of their training programs and identify areas for improvement. Edstutia helps L&D teams design immersive learning experiences in VR and offers real-time dashboard insights that capture, analyze, and report learning engagement and retention.

Back to PLA

For L&D professionals, predictive learning analytics (PLA) is crucial to maximizing return on L&D programs. Like looking into a crystal ball, PLA gives insight into issues or inefficiencies before they lead to scrap learning.

PLA involves collecting, analyzing, and interpreting data from learners’ past behaviors with the intention of predicting their future behaviors. By using historical data about learners, it can anticipate their future learning outcomes.

Businessman writing words related to predictive analytics
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PLA data constantly changes as a learner progresses through a course or training program. For example, let’s say your employee has failed the last assessment in a training module, which brings their predicted rate of failing the entire course to 50%. Their performance on their next assessment will determine whether the predicted failure rate increases or decreases.

The key advantage of PLA is its focus on individual learners and emphasis on the present and future rather than the past. With this data, L&D professionals can improve their programs and help employees succeed.

How Can You Use Predictive Learning Analytics?

Early Warning Systems

An early warning system (EWS) is a type of PLA that uses regression analysis and algorithms to identify poor-performing employees. 

To illustrate, let’s say you want to use an EWS to identify employees in your training course who are likely to drop out before completing it. Data such as previous course completion rates, average weekly time spent in the course, frequency of engagement with the course, and average score on assessments can predict the likelihood of an employee dropping out.

Woman looking discouraged at her computer
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Companies use an EWS to identify at-risk learners as soon as possible and intervene. When training instructors or managers can intervene and offer additional support and resources, the learner will feel supported and, ideally, achieve a better outcome. 

Predicting On-the-Job Use of New Skills

By focusing on the individual, PLA can predict whether or not an employee will bring their new knowledge and skills to their position and apply them. 

Suppose your employee is running through parts of a module very quickly and isn’t repeating failed modules. In that case, PLA may foreshadow the unlikelihood they’ll apply the learning material at work. After all, if you don’t retain knowledge, you won’t have the opportunity to use it. 

First-person view of a VR simulation for pilots
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Scenario-based assessments with immersive technology, such as VR simulations, can also shed light on the probability an employee will use their skills on the job. Employees who successfully complete a simulation demonstrate their ability to apply the learning material to real-life situations.

Identifying Inefficiencies in a Course

PLA can help course creators and instructors single out inefficient components of a training course. If employees pass your training modules with flying colors but fail the final assessment, you may ask yourself what went wrong and why they failed if PLA predicted they would pass.

You’ll want to examine your assessment and its relevance to the material in the modules. Perhaps the assessment was too challenging compared to the modules, or most of the assessment focused on a topic the modules glossed over.

Benefits of PLA

Predictive analytics is helping modern businesses become more efficient across many industries and use cases. For example, retailers use predictive models to anticipate inventory needs, and airlines use predictive analytics to determine future ticket prices. 

When it comes to corporate learning, predictive analytics provides the following benefits:

Predicting Learner Performance

As opposed to other analytics focusing on the past, predictive analytics emphasizes the present and future. With PLA’s predictions about future outcomes, instructors can intervene and make changes to their course or provide extra support to at-risk learners.  

Instructor intervening with learner to provide additional support
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Taking these preventative measures helps trainers provide employees with a more individualized, adaptive learning experience. Meaningful training experiences translate to the on-the-job application of new skills and higher motivation, with 92% of employees saying successful training increases job engagement.

Closing Skills Gaps

Companies are investing more than ever in L&D–with 49% increasing their budget in 2022 to upskill their employees. Training must be relevant, individualized, and efficient to close the skills gap. 

By singling out inefficiencies in training programs and identifying learners who are off-track, PLA helps L&D professionals be more agile with training strategies and decisions. The ability to identify patterns, weak points, and the potential of courses and learners makes L&D teams more successful at closing the skills gap.

Identifying Causes of Scrap Learning

LinkedIn’s 2022 Workplace Learning Report found that 72% of L&D leaders say L&D has become a more strategic function in their organization. With so many dollars wasted on scrap learning each year, they’re focusing on designing training programs that make knowledge stick.

Identifying the causes of scrap learning is the critical first step to improving training courses. By focusing on individuals and not the training program as a whole, PLA helps instructional designers identify the following causes of scrap learning:

  • Content that isn’t relevant to training goals
  • Ineffective training delivery
  • Content quality issues
  • Employees who aren’t in the right training
  • Lack of opportunities to apply new knowledge on the job
  • Lack of engagement
Scrap learning is costing you employees

Reducing Costs

The average company spent $1,071 per employee on training costs in 2021. PLA reduces training costs by helping L&D teams identify the causes of scrap learning and more efficiently close skills gaps. 

Rather than learning through trial and error, L&D teams that have predictive analytics at their disposal can quickly determine inefficiencies and make improvements for better ROI with their training initiatives.

Collaborating Effectively

A mix of roles is crucial to designing and delivering training programs—UX designers, training analysts, L&D leaders, instructors, and learning coordinators, to name a few. Therefore, effective collaboration is vital to creating a successful training program, and PLA provides valuable insight that allows the individuals in these roles to get on the same page more quickly. 

L&D team collaborating through predictive analytics
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Challenges of Predictive Learning Analytics

Choosing What Behaviors to Analyze

Using any analytics in your organization comes with its unique challenges and the need for careful preparation. For PLA, your L&D team should carefully consider what behaviors you want to analyze and with what techniques. The chosen behaviors should align with overall training goals and pain points you’re trying to address. 

Suppose there’s a pattern at your organization of employees beginning training courses and not completing them. You may want PLA that helps you determine how likely each employee is to finish the course as they progress through it. This way, you can intervene at the right moment, provide extra support, and determine when and why they lost motivation.


Using PLA also requires preparation. If you adopt an early warning system, you should establish an intervention plan that trainers and managers know how to implement. What is the protocol for intervening with an at-risk learner? How will you determine if they’re getting back on track? These are important questions to consider before implementing PLA.

Integrating Predictive Learning Analytics With Your Learning Platform

Smooth integration with your learning platform is critical. However, navigating how to integrate PLA for a user-friendly experience can be challenging. Edstutia provides real-time dashboard insights that trainers can view right inside our VR campus. Key metrics on each learner and their progress enables instructors to intervene with at-risk learners and effectuate better outcomes.

Edstutia's real-time dashboard analytics for VR learning


Predictive learning analytics gives L&D professionals the insight to improve training programs and support individual employees throughout their learning journey. By highlighting the early warning signs of scrap learning, PLA helps organizations optimize their training investments.

Edstutia recognizes the importance of maximizing your L&D efforts to upskill your team effectively and efficiently. VR learning is up to 4 times faster than traditional learning and allows you to capture, analyze, and report on learning engagement and retention. 

Learn about our Enterprise Solutions and how Edstutia can help you create and deliver results-oriented training programs.