For a training course to remain effective and maintain its pedagogical usefulness over time, it is essential to analyze its results: completion rate, number of learner errors, time to complete a module... These data are “Learning Analytics”.
Come on, we'll explain everything to you!
Understanding Learning Analytics and their usefulness
What is Learning Analytics?
“Learning Analytics” (or “learning analysis”) are numerical elements that will allow trainers or training managers to analyze the effectiveness of their educational content. To do this, they collect, measure, use and decrypt this data during a training session (online or in person). The sooner these Learning Analytics are adopted by the company, the more accurate the analysis will be and will allow to adapt the courses offered to learners ! They are therefore real allies of vocational training.
5 Learning Analytics examples to analyze for your training
There is a multitude of data to analyze to improve your training. Some indicators will be used to determine the effectiveness of your content and the learning of your learners. Others will give you information on the format, length, and teaching material. And finally, some data will help you anticipate problems related to your training. Here is a non-exhaustive list of Learning Analytics to follow for your training:
- the completion rate of a course: This indicator is a good way to know if the module is too complex, too long, or if it does not meet learners' needs.
- The dropout rate: This data gives you an idea of how interested learners are in your training
- the average viewing time of the module: If this number is too low, it may mean that your course is too long or not dynamic enough.
- The number of errors in a quiz at the end of the module: if they are too high, the concepts may not be properly assimilated by the learners, or the course does not deepen certain points sufficiently. The questionnaire can also be too difficult!
- the duration of the learner's connection: If your learners do not connect or have very little time, it means that your training does not necessarily meet their learning needs. It could also be that The proposed modules are too long and that they don't take the time to form.
The benefits of Learning Analytics for your business
The personalization of your content according to the learners
By observing the behaviors of your learners (completion rate, duration of connection, duration of connection, modules completed, errors in the final test, etc.) you can adapt your content easily.
It will no longer be necessary to carry out long studies (and rarely completed) to know their feelings about your courses: the data will speak for itself!
For each learner, remove the modules that do not contribute anything, the blocking points, add optional courses to review the concepts that have been poorly assimilated. In short, Make learning 100% personalized and adapted to improve results!
Agility and flexibility in creating your content
Modifying a module or course should be quick and easy in order to be able to quickly adapt to learners' expectations and their results. With Learning Analytics analysis, you can draw quick conclusions based on the data collected. It will no longer be necessary to start from scratch for each training course; micro-changes should already help you achieve your results. You gain in agility, and your results appear more quickly.
The return on investment of your training in the long term
With the indicators of Learning Analytics, you know the completion rate of your courses, the duration to validate a course and the success rate in the end-of-module tests. So you are in a position to know if your training is effective.
Then compare this data with older data (a few months or years ago). This will give you an idea of how to improve your ways of education.
And finally, compare the evolution of this data (time savings, number of simultaneous learners, better module completion) with the cost of each training course to know their return on investment. ”If my learners complete this course in 5 minutes, compared to 8 minutes 6 months ago, how much did I save?”
Anticipating learners' needs and problems
What could be better than being able to know what a learner wants before they formulate it? With learning data analysis, you will finally be able to understand the needs and bottlenecks of the people who attend your courses, without having to ask them.
Indeed, satisfaction questionnaires are sometimes biased because of the wording of the questions, or the moment when the learner answers: he will forget his feelings if he takes several weeks to complete the form.
Some trainers even manage to adapt their live training according to learners' comments and questions (via a chat or videoconference).
