The world is increasingly data-driven, and it has become more imperative than ever for organizations to focus on big data and analytics in every sphere of the industry they represent. Leveraging big data and learning analytics not only gives L&D professionals a competitive edge but also helps them promote a continuous learning culture.

To put it simply, learning analytics is the collection, measurement and reporting of learners’ data (while they are engaged in the learning process), which can be used to gain valuable insights into the learning experience and find ways to optimize and improve that experience.

Why Learning Analytics?

There are multiple reasons to leverage learning analytics: improved engagement, personalized learning experiences, effective e-learning programs, increased retention, behavior prediction … and the list goes on. Evaluating learners’ experiences on a consistent basis helps L&D teams improve learner engagement by creating or tweaking training programs that are specifically designed for the learner. This personalization results in increased knowledge transfer and application.

Through learning analytics, L&D can also pinpoint problems that are hampering learners’ performance and provide then with the necessary tools or resources to perform better. Analytics turns learners’ data into actionable insights, which may also include improving the learning culture, improving process efficiency and leadership development, and more.

How Can You Leverage Learning Analytics?

Many approaches to learning analytics are based on the Kirkpatrick model of evaluation, which uses four levels of evaluation:

  1. Learner reaction: the learners’ feedback on the training
  2. Learning: the learners’ acquisition of knowledge and skills as a result of the training
  3. Behavior: the learners’ application of new knowledge and skills on the job
  4. Results: the impact of learners’ improved performance on business outcomes

Although it is easy to track levels one and two, tracking the higher levels can be a constant struggle. However, they are what really give purpose and value to the entire practice. Level 3 involves both pre- and post-training measurement of learners’ behavior. This measurement can be, but is not necessarily, a reflection of whether participants actually learned the subject material. Level 4 requires both pre- and post-event measurement of the training objective, such as reduced cost, improved quality and efficiency, increased productivity, employee retention, increased sales, and higher morale. This measurement is where learning analytics plays a vital role. An LMS equipped with the right analytics and reporting tools will provide the L&D team with the right data, custom reporting capabilities through graphical charts and tables, easier navigation and management, and more.

An LMS equipped with a reporting and analysis features is capable of tracking learners’ performance, engagement and knowledge retention. In its raw form, these data may not mean much, but when seen through an analytical lens, they reveals valuable insights that L&D teams can use to customize training programs. With the fast-changing digital landscape, many LMSs and analytics tools are now also employing artificial intelligence (AI) for the interpretation, analysis and application of captured data.

Now Is the Time for Learning Analytics.

Learning analytics is not some distant reality but a valuable solution for every organization. In a marketplace flooded with evolving technology, there is no denying that the learning industry will continue leveraging analytics on a larger scale to understand learner behaviors and adapt training accordingly.

With every department in today’s organizations facing pressure to show its value, L&D stands to benefit more from learning analytics. L&D departments can back their budget with analytics, thus adding more credibility to their work. Considering the money spent on L&D programs, it is only fair that organizations seek to find value in them. L&D leaders need to be proactive and leverage learning analytics to justify their requirements and remain competitive.