Everything you need to know about learning analytics

Learning analytics is a growing field in the learning and development (L&D) industry.

Many L&D managers are using learning analytics to encourage the development of specialised learning programmes that are effective and meet the needs of the learners. 

Here’s everything you need to know about learning analytics in corporate training.

What is learning analytics?

Learning analytics is a learning technique that involves the measurement, collection, analysis and reporting of data about learners and their educational background. This is to help improve the learning experience and make it more effective, and to improve feedback. 

Learning analytics is used across a wide range of settings, however, in this article, we’re going to focus on its impact and use to upskill employees in a corporate training setting.

How can learning analytics be used in eLearning design?

Learning analytics can be used to enhance the eLearning experience for learners and instructors alike. Some of the benefits of implementing learning analytics into eLearning are:

Improved learning outcomes

Collecting and analysing data around learners’ progress can allow instructors to identify key areas where learners may need additional support. By stepping in, instructors can help learners with their development and get them to where they need to be. 

This is great for employees who are undergoing corporate training, as they may need additional support to get the best out of their learning experience. 

Better instructional design

Collecting data on learners can allow course designers to develop an improved instructional design for learning courses. This will give learners an improved learning experience where they’re getting the most out of their course. 

Learning analytics allows course developers to see which areas to improve upon and ultimately deliver courses that will have the right content, hold attention and interest, and be engaging.

Personalised learning

The best learning experience differs from person to person as individuals take information on board in different ways; some prefer to learn in a visual manner, others find text easier to follow. With learning analytics, course developers can curate each learning experience to meet the needs of each learner, allowing a more engaging and personalised learning experience.

Increased engagement

Learning analytics in eLearning allows learners to receive real-time feedback. The fast-paced nature of feedback responses keeps learners engaged, decreases wait time, and maintains their motivation levels to succeed in their training programme. 
 

How can learning analytics be used during corporate training?

Assess trainee performance 

Collecting data can help course instructors and L&D professionals establish the performance of their learners. This provides them with a clear insight of the progress of their learners and enables them to assess if their learners are on track or not. 

Provides an early warning on trainee performance 

If any anomalies are detected, they can be addressed early so that learners are getting the correct support and guidance. Alternatively, tutors can see if learners are on track to achieve learning goals earlier than expected. Learners who are on track can assess if they feel it is worth aiming to achieve beyond their expected score. 

Evaluating effectiveness of each course

Digital learning designers can use learning analytics to determine if learners got the most out of their learning experience and if the modules are a success and are effective enough to invest in.

What are the different methods of learning analytics?

There are a few different methods of learning analytics that can be used to collect and analyse data throughout the learning process, to ensure trainee success in learning. 

The main 4 types of data analysis can be broken down as:

• Descriptive analytics: Provides a description of what has occurred.
• Diagnostic analytics: Seeks to identify the cause of an issue.
• Predictive analytics: Predicts future outcomes.
• Prescriptive analytics: Offers recommendations for future action.

Here is a more in-depth explanation of the above methods of learning analytics, including an extra method:

Descriptive analytics

Descriptive analytics focuses on what has already occurred. This is a great stepping stone to establish learning outcomes and assess how you want the learner to proceed. A good example of descriptive analysis is past scores and grades from previous training programmes. 

Diagnostic analytics 

This type of analysis helps us determine the root causes behind any outcome or anomaly. If it is a negative outcome, we may seek to determine the root cause so we can avoid it. Alternatively, if the outcome is positive, it may be possible to adopt this best practice in other courses. 

An example of this is a high-class average score caused by a particular tutor during a training programme. In this instance we would keep the tutor as they have proven success rates which is better for the business. 

Predictive analytics

Data can tell us a lot about what may occur in the future. Predictive analysis aims to forecast future outcomes in learning. An example would be, predicting which set of learners may do better than another, based on which instructor has a better class average in the past. 

Prescriptive analytics

Prescriptive analysis is important for targeting the individual needs of each learner. Prescriptive analysis provides recommendations to achieve a desired outcome, so each learner has their learning needs met. You can use this data to tailor your training programme to meet learner needs.

Cognitive analytics

Cognitive analysis uses technology and algorithms to analyse and organise big data sets and provide solutions to any queries inputted into the system. 

If you’re an L&D professional looking to specialise in Digital Learning, enrol on our Digital Learning Design course today to progress your career.