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Data-driven learning design is the usage of data while deciding on the design of an eLearning course. Data-driven learning products are very effective at eliminating the hurdles to positive training results that often act as a hindrance to the progress of the learner in a traditionally designed course.

Although it may seem that the learners are engaged in training content, the assumption doesn’t need to be true. It is better to start looking at a few insights and trends that can inform future design decisions and lead to improved engagement and more effective courses. Using a data-driven design approach requires additional work for collating and analyzing learning data. It could be a little challenging for the learning experience designers as they are not usually associated with data analytics as part of their routine tasks.

In this article, let’s review some examples of how data-driven learning design can enhance your eLearning Courses.

1. Revising The Provided Instructions

The in-detailed instructions may be extremely helpful for the learners who are less experienced with eLearning; however, others may find it condescending.

Using a computer and mouse to drag things around is something all people know. You can see if using less or more detailed instructions lead to changes in activity engagement by experimenting with the instructional language.

You can check how the learners engage with the content. Then, you can change the instructions and measure the engagement level again. Consequently, you get appropriate data points for designing learning activities.

2. Using Storytelling In Your Content

At times, when a learner finds the training content uninteresting, they skip it totally. On the other hand, when they find the training content too boastful, they may still skip it.

In such cases, you can collect training data for identifying the course section that is often skipped by the learners. You can then add storytelling elements in the content and measure the learner’s behavior again. In the case of learners spending more time with the content, possibilities are storytelling works with them.

3. Analyzing Learning Activities

It is important to collect data on how learners engage with the learning activity. For example, see whether the learner is completing the course and how much time they are spending on a slide.

Check if they are returning to it. Such pieces of information are imperative in understanding the intensity of learner engagement with the course content. Eventually, you can use them to make data-driven learning design decisions for the future.

4. Checking The Device For Training Access

It has often been seen that the learners use multiple devices to access the training. This is something that many learning experience designers often don’t realize. Hence, it is imperative to track the various types of devices the learners use to log in.

Check if they majorly use mobile devices or they avail training only on desktops/laptops. This data is extremely crucial in understanding the type and size of the screen being used for learning as it dictates many learning design decisions.

Get Started With Data-Driven Design For eLearning

For a successful data-driven learning design, you must have access to the right set of eLearning authoring tools comparison guide.

To track data points and perform experiments, you must gather the required information consistently, which means having the right set of skills and infrastructure needed for the task.

A reliable eLearning development company with the right expertise can help you achieve this in a proper way.

By tracking a few metrics and learner’s data, you can make effective decisions and run experiments based on the collected data. This way, you can start looking at various patterns and find useful ways for improving your eLearning courses quickly.

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