Learning analytics for beginners

Learning analytics for beginners

Data is hot. Also in the world of learning, but data for learning often can come across as very specialised and out of reach for most. More and more information about learning processes can be stored. Collecting, analysing, and interpreting data from learning environments in order to improve the learning process of participants is called “learning analytics”. It may sound complicated, but even in its simplest form it can be very useful.

We’ll show you how.

What is learning analytics?

When you, as a trainer or (training) organisation, have launched an online learning programme, you probably want to know to what extent the learning activities contribute positively to the learning process. After all, it’s about the outcomes. Because the possibilities in the field of online learning are becoming more and more advanced, more and more information about learning processes can be stored. With learning analytics you can gain valuable insights from that information. By building codes into the learning environment or learning platform, you can analyse the learning behaviour of the individual participant or of a group of participants.

Learning analytics may sound complicated but it is not. Learning analytics provides answers to simple questions such as: ‘What is the learner doing in this learning environment?‘, ‘How often does he log in?‘, ‘Which pages and videos does she click on?‘. Each action that the participant performs in the learning environment, such as a mouse click or the time spent on a page, can be stored in a database.

Not all information is actually relevant. Which information is interesting depends on the chosen didactic concept. For example, in a didactic concept that focuses on collaboration, it may be interesting to know how often participants respond to each other. In a didactic concept that focuses on theoretical knowledge transfer, you might want to know how much time participants spend reading an article.

Therefore, when designing a learning path, think at the beginning about what information you need to gain sufficient insight into the learning behaviour of the participants.

What is the purpose of learning analytics?

Learning analytics are used by trainers, developers and participants to better understand and improve the learning process. This is because what matters to the participants is not the training, but the result – what they can do with the new knowledge or skills.

Benefits of learning analytics for facilitators

Supervisors of participants, such as managers and trainers, gain insight into the learning process of participants. For example, they can see how much time the participants spend on the learning activity and whether this time investment determines the test results. This way, they know which knowledge and skills they still need to focus on and they can give the participants tailored advice. Because the learning environment monitors participants’ efforts and progress, the supervisor’s role changes from that of someone who checks and implements to that of a (re)designer of training and learning.

Benefits of learning analytics for developers

Developers of online learning gain insight into the relationship between learning activities and test results and use this data to optimise learning activities. For example, it may be the case that there is no positive relationship between viewing an instructional video and a test result. This may be a signal that the instructional video in its current form is not making a positive contribution to achieving the associated learning objective.

Benefits for participants

Participants gain more insight into their own learning process and, based on their progress, receive personalised advice on learning activities to follow. In addition, the participant can compare his/her own learning process with the learning process of other participants or the advice from instructors.

How you can use learning analytics

The use of learning analytics is still in its early stages, but it offers many opportunities for optimising both the learning and coaching process. Applying learning analytics is easier than you might think. You can already make use of it during the design, development and implementation of learning processes. Below are a number of examples of practical applications.

Participants’ progress in a learning platform (aNewSpring)

The progress of participants in a learning path is easily accessible. This way, the participant can see which learning activities he has already completed and which activities he still needs to follow to complete the learning path. Supervisors also have insight into the progress of participants and can see how long a participant has been working on each activity.

Adaptivity & learning analytics

Before starting a learning path, participants can take a self-test to gain insight into their prior knowledge. Based on the results, they automatically receive advice on the learning activities they still need to follow. This is in fact an example of learning analytics, as the results are used to help the participant even better. In this way, an adaptive learning path is created for each participant that meets his/her specific learning needs.

Improving assessment on the basis of learning analytics

Learning analytics can be used to measure the quality of an assessment. One of the tools used is the p-value. The p-value expresses the difficulty of a question. The p-value lies between 0 and 1 and indicates what percentage of the participants answered the question correctly. The easier the question, the higher the p-value. If the p-value is below 0.50, you need to find out the cause: is it due to a lack of knowledge or was the question not clearly formulated?

The future of learning analytics

In the years to come, the possibilities of learning analytics will definitely increase. As a training provider, you can use more and more data to generate user feedback, demonstrate the relevance and impact of the learning intervention and develop learning interventions further. With the help of user feedback, for example, you can gain insight into the so-called ‘failure points’ in the learning process. In other words: at what point in the process do participants make mistakes? The data on learning behaviour can help to find the cause of these errors and to improve the quality of learning activities.

In addition, learning analytics also provide a continuous insight into the performance of the participants. When sufficient measuring moments are built in, learning analytics can replace the function of a test. This data, possibly in combination with the observations of a trainer or manager, can provide sufficient insight into performance to make a test not even necessary anymore.

By means of learning analytics, learning providers, training organisations and trainers can better support the learning process of their participants and thus optimally contribute to achieving their organisational goals.

Curious about how you can use learning analytics in your organisation?
Please contact us. We would be happy to think along with you!