We are all living in a data-driven world. From music streaming service to GPS, to personalised news feeds—data is a convenient helping hand to improve our lives. When it comes to education and learning, data is just as valuable.
The data learners leave behind in their digital footprint can offer incredible insights into their learning experience. However, without analysis, data can be, well, kind of meaningless. Enter learning analytics—a digital strategy transforming the eLearning and broader education sector. But what exactly is learning analytics and why is it important? Read on for our complete guide to learning analytics and its impact on the learner experience.
What is learning analytics?
Learning analytics measures, collects, and analyses data about learners to improve learning outcomes and the learning environment. Although learning analytics is a relatively young digital strategy and technology, it is set to burst into the mainstream. According to the 2019 Horizon Report by EDUCAUSE, learning analytics is expected to achieve widespread adoption in the coming years.
EDUCASE estimates that 35 per cent of institutions are planning or currently expanding course-levelling predictive learning analytics. Over 50 per cent of institutions are planning or expanding predictive analysis for student success.
The EDUCASE report identified a growing number of studies showing the use of learning analytics to help identify students at risk of failure and intervene to positively influence measures of student success including retention.
Why is learning analytics important?
Learning analytics is an important tool for both learners and education providers. Data about learners can shape the student experience, by measuring performance and supporting development. Learning analytics can help educators improve the quality of their teaching and develop best-practice methods.
At an institutional and organisational level is can provide a blueprint strategy to create best business outcomes and help direct where you should invest in training and upskilling of employees.
What are the types of learning analytics?
Learning analytics is broadly broken up into four categories with each having strengths that can be leveraged in your organisation.
- Descriptive analytics looks at the ‘what’ of past behaviour. For example, descriptive analytics can measure the number of enrolments in a course, assessment scores, or student feedback. Comparing this historical data with new data offers a clever way to see whether positive changes has occurred within a learning organisation.
- Diagnostic analytics will tell you why something happened. You might use it to understand the reason behind a trend of poor completion of staff training, for example. Diagnostic analysis is also useful to analyse the ‘why’ behind descriptive analytics trends and how to address them.
- Predictive analytics helps you predict what might happen in the future. It uses existing data to forecast an estimate around things like predicted compliance rates, or even the likelihood of a learner experiencing difficulties completing training. In this way, predictive analytics can help organisations make data-driven decisions to inform strategy.
- Prescriptive analytics gives you the answers about what should be done next. It uses predictive analytics data to make informed choices, by calling on things like machine learning and algorithms. Prescriptive analytics can make suggestions around how to boost course enrolments or improve learner engagement.
How can learning analytics be used to drive learner engagement?
In a post-pandemic world, eLearning is increasingly popular choice to upskill teams working remotely or across different locations. One of the perceived challenges or concerns from clients exploring the eLearning sector is decreased learner engagement compared to traditional face-to-face delivery.
Learning analytics are the ideal tool to give a data-driven insight, and allows you to make timely and agile changes to course content. Learning analytics have been successfully used in higher education to help universities measure and address student wellbeing.
What are the benefits of learning analytics?
In its infancy, learning analytics was used by teachers to identify students with learning difficulties (and still is), so they can offer more personalised support and improve learning outcomes. Learning analytics can tell educators if their learners have low attendance, or fail to submit assessments on time.
Armed with these insights, facilitators can offer additional advice and support to their learners, decreasing risk of non-completion and improving learner engagement and retention rates.
Learning analytics allows educators to personalise learning to suit an individual or organisation. If a learner feels disconnected from course content, learning analytics provides a warning to revise or review existing content and improve for future learners. Facilitators and trainers now have an evidence-based reason to respond and adapt course content to benefit their learners.
Talk to the eLearning experts
Data insights are frequently evolving to inform best practice eLearning solutions. Choose to work with an LMS partner who stays on top of the latest trends and eLearning insights. Our dedicated team of Learning and Development professionals can help you design unique learning solutions that prioritise your learners’ needs and motivations.
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