We continue to stress on metrics that provide evaluation of learning devised against a set of learning outcomes. We need to rethink with a focus on value as compared to learning as a key target.
Experts characterize ‘Learning Analytics’ as the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimizing learning and the environments in which it occurs.
The interest in learning analytics continues to flare as more tools and techniques emerge and our shared understanding of the usefulness of analytics continues to develop. It plays a key role in better evaluation of learning’s effectiveness, improving learning delivery methods, and most of all, better understanding of learning’s impact on business results to establish the real business value of organizational learning.
Research shows that on average only one in five organizations currently use learning-related big data and learning analytics. However, it is apparent that this proportion is likely to grow as another two out of the five have plans to implement learning-related big data capabilities within the coming year or a longer timeframe.
From maggot to butterfly…
Along its path of evolution, we can see that learning analytics has developed from an administrator and learning manager focused set of data files to becoming more learner focused with supporting dashboards and other dynamic interfaces. Technological developments certainly have served as catalysts to move towards ‘Big Data’, to manage its volume, velocity, variety, veracity, variability, and complexity.
Now learning analytics is steadily budding into a line and business aligned system, providing intelligence for making better-informed learning and performance decisions.
The ‘crystal ball’ of learning…
Now we are able to collect vast amounts of information in our learning management systems and enterprise resource planning systems.
We can apply learning analytics to this information using a blend of dispositional and performance indicators to deliver more accurate prognostic capabilities to learners and enterprises alike, to proactively recognize danger signs before threats to learning success materialize.
But not quite a ‘magic wand’…
The degree to which learning analytics will offer high value to our enterprises will depend on the degree to which we as learning and performance professionals keep the promise of providing personalized and optimized experiences for learning and performance support.
Learning analytics is a means to the end, not the end in itself. If we are able to use learning analytics based on sound analytical frameworks and findings, it has the potential to provide learning stakeholders, including learners themselves, the power of better-informed decision-making and process optimization to improve learning and positively influence business outcomes. This framework will depend on the development of models built on high-value variables that make predictions at levels of confidence that make the interventions meaningful.
Adjusting our roles…
As learning and performance professionals, we need to understand our business needs and align analytics processes to setup feedback loops for continuous improvement. We need to layer learning analytics over the organizational goals we are seeking to support and the programs, initiatives, and processes we create to ensure sustained business impact.
We, as learning and performance professionals need a change in mindset to accept the reality of the present-day state of learning analytics and create the business case for this change.