Why eLearning Pros Should Use Predictive Analytics

Why eLearning Pros Should Use Predictive Analytics
Summary: There are always new technologies emerging to help eLearning professionals. Predictive analytics aims to predict your learners' behavior, help them achieve their goals, and facilitate the design of your courses.

How Does Predictive Analytics Help eLearning?

Predictive analytics leverages data like learner performance, participation, and other behaviors, to identify patterns and predict how learners will do in the future. This method helps educators and eLearning pros to make data-driven changes in their strategy and planning to support their learners and achieve the best possible outcomes. In this article, we'll explore the advantages of using predictive analytics, as well as the things you should look out for when deciding to implement it.

5 Benefits Of Predictive Analytics In eLearning

1. Personalized Learning

When using past data from your Learning Management System (LMS), you can analyze patterns in your learners' behavior. Those can be their learning preferences, strengths, or areas for improvement. Then, you can draw conclusions and create tailored learning paths for each of your learners, considering their interests and capabilities. This optimizes their learning experience and holds their attention, thus increasing their engagement and motivation.

2. Identification Of At-Risk Students

Knowing everything about your learners' attendance and performance lets you see who is more likely to stop attending the lessons, fall behind, or even drop out altogether. Once you spot who they are, you can use your LMS to deliver them personalized guidance and help them navigate the challenges they may be facing. This guidance can be in the form of tutoring, mentoring, or providing additional learning recommendations.

3. Improved Course Design

Analyzing data on learners' engagement and interactions with the lessons makes it easier for you to pinpoint which elements of the eLearning courses are effective and which are not. This way, you can create more relevant material based on your learners' preferences. For example, if predictive analytics shows that your learners are struggling to complete a specific lesson. So, you can incorporate tips or microlearning modules to support them.

4. Improved Student Outcomes

The main purpose of predictive analytics is to help students achieve their learning goals. Everything stated above, like personalized courses and additional support, will lead students to complete the lessons more easily and quickly reach their learning milestones. This leads to higher completion and student retention rates across the board.

5. Reduced Costs

By identifying which resources are effective in helping students, you can cut down on unnecessary ones and reduce costs. You also save time since you don't focus on resources that have nothing to offer to the overall learning experience. Consequently, all the money you save can be reinvested in another aspect of your learning program that will benefit your students more and improve the quality of the courses.

What Are The Risks?


Algorithms have the risk of showing biases towards certain groups of students. This happens because predictive analytics relies on historical data, which may be based on discrimination or past injustices. For example, it can show incorrect results, categorizing students based on their gender, race, and other factors, resulting in those students being unfairly assessed. To overcome this, ensure the algorithm is trained correctly.

Privacy And Security

When dealing with the collection and analysis of data, vigilance is essential. This data includes personal information on students, like their names, addresses, and grades, which are sensitive and are at risk of potential security breaches. Ensure that the data is protected and have a crisis aversion plan in place. Also, it's beneficial to have security awareness training for your learners, teaching them to look out for phishing and scam emails and avoid sharing their credentials online.

Lack Of Transparency

The algorithms are created in order to provide insights and feedback to educators concerning their learners' learning behavior. However, they're not always transparent. Specifically, it's not clear to eLearning professionals how predictive analytics creates conclusions, and this, in turn, isn't clear to learners who receive their grades and assessment results. This can be confusing and lead to mistrust. So, make sure you're as transparent as possible and complement the system's results with your comments, as well.

How To Implement Predictive Analytics In eLearning

Goals And Objectives

First, it's helpful to identify what you want to achieve with your eLearning program and what you need to measure with predictive analytics. For instance, do you want to increase participation in the courses? Or do you want to offer personalized lessons to your learners? After identifying your goals, it's easier to know how you'll leverage predictive analytics; thus, it's easier to communicate this information to your learners.

Data Collection And Analysis

Next, collect data that's useful to you and aligns with your objectives, like performance data, participation, engagement, login information, etc. This can be gathered from your LMS, surveys, or other learner interactions with educational platforms. Then, define how you'll analyze data to find trends and patterns. You can do this with Machine Learning (ML) or algorithms.

Choose The Right Tools

To implement predictive analytics, you have to select the right technologies to deliver and track the program. To make your decision easier, consult with an expert and take your organization's needs into account. You can also do your own homework and check out online forums and reviews to gather the necessary info.

Build And Test Predictive Models

The final step is to use algorithms and other methods to create predictive models for what you want to measure. Whether evaluating completion rate or student performance, building these models often requires statistics analysis expertise. Fortunately, there are eLearning companies that have the right skillset and tools for the task. Finally, you need to test the models you build to determine if the data they collect is accurate.


Predictive analytics has the potential to change the way eLearning professionals shape their strategies and lessons. It's a revolutionary way to predict future outcomes for your eLearning courses and prevent learners from failing their lessons or abandoning them completely. However, you shouldn't over-rely on predictive analytics, and it shouldn't be your only source of information. Instead, use predictive analytics in combination with your own expertise and various assessment tools that can offer you greater insights into your learners' learning patterns and behavior.