E-Learning and Your Big Data – Effective Analysis

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eLearning is constantly evolving and the possibilities that could arise from using big data are huge for the sector. Capturing a learner’s experience can obtain extremely valuable data, but this information will only prove useful if it results in meaningful change. A clear idea on big data and associated learning analytics can help you to design more personalized courses. This should push up learner satisfaction and engagement with the eLearning courses that you offer.

So you’ve been collecting feedback, reports and analytics on your eLearning for a long period of time. eLearning is constantly evolving and changing and the big data available will help you prepare for the next trend. So how is this data best analyzed and assessed?

Big Data

In the context of the eLearning industry, big data is the data created by learners while undertaking a course or module. For instance, if an employee completes a training module regarding company ethics, their progress, results and any additional data created during the course is considered to be “big data.”

When harvested effectively, this information can result in many new possibilities for eLearning and managing your data effectively can streamline your instructional strategies.

The information available will empower and enhance online training and it also provides metrics to consider and learn about several points. Examples of this are learning styles and preferences; any areas learners are getting stuck at and when and why they are getting stuck; to be able to provide a personalized learning experience and whether the learning has resolved the organization’s requirements.

Knowing where to find big data is often a big challenge for any eLearning professional or specialist so broadening your horizons and collecting as many metrics as possible is the best approach” explains Greg Dalke, a Data Analyst at Brit Student.

The most significant examples of data resources are Learning Management System analytics, focus group and questionnaire findings and social media polling.

After the establishment of data resources, you will need to compile all of the information and streamline the data to suit your needs.

Your needs will determine how valuable this data is – some metrics will be more valuable than others. It’s therefore important to establish goals and objectives before you start to analyze any obtained data. Being as specific as possible is essential – what are you setting out to find out?

For instance, if your objective is to improve employee performance, you must establish the skills or tasks you are focusing on. You will also need to determine how you are going to measure current levels of staff competence. However, it’s inadvisable to discard any acquired data because even if it isn’t useful now, it may become so in the future and is therefore worth holding onto.

All data is a valuable asset and needs to be protected carefully and as such, every source should be encrypted and online security measures enacted to ensure data safety. It is also prudent to extensively know those who have access to your data and how it is being used.

Priority List

If you have several objectives, you may need to create a data priority list. This will allow you to focus on one issue at a time and allow you to establish any issues that need to be addressed immediately as well as any issues that can be addressed in the future.

For instance, a learning module that is too challenging will inevitably result in learner frustration and the module not being completed by increasing numbers of staff. This in turn will result in a lack of learner motivation so careful analysis of feedback taken from learners should help establish problem areas and how to improve your course.

If you’ve chosen data that is relatable to your objectives, you will establish trends and patterns. When observing a trend, analyze the available data and start creating a plan of action to make any relevant updates and improvements” says Ruth Bohanon, a Data Professional at Australia 2 write.

Scalable analytics

Due to the nature of information, it will only increase in size over time and consequentially, you must have a scalable, flexible analytics tool. Learning Management Systems are amongst the most effective analytics tools in eLearning. They make it possible to track learner progress and a view of their learning habits.

If the scale of your big data is becoming too much for your organization, you may need to outsource to a consultant who will be able to help you effectively analyze and manage your data.

Big data can make a sizeable impact in eLearning and it can be used as part of a long term approach for problem solving in the sector. However, competently managing this valuable information will significantly enhance your organization’s ability to provide eLearning effectively.

About the Author

Katrina Hatchett is a tech blogger at PhD Kingdom. She has been involved in various video marketing projects, where her main aim is to create video content and consult businesses on how to do the same, as well as improving overall communication effectiveness. Also, she writes for Next Coursework, academic service.


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  1. Thank you for sharing such interesting information, a debt of gratitude is on the way. Loved your effort in putting the results of the conducted analysis. I too think our future generation will be more inclusive towards e-learning, which would save time and becomes efficient.