Learning Analytics & its Utility in the Post Covid World

What really defines today's modern business ecosystems and their successes or failures is the availability and management of data (anything and everything) we all are using to make strategic choices and decisions at work. The information collected, analysed, inferred and stored for the future has led to the fastest growing period in the history of mankind. One of the areas where data is being used extensively today is Talent Development or Learning & Development which leads us to the sophisticated discipline of Learning Analytics (no sarcasm intended).

Started nearly a decade ago as a means to understanding a learners’ behaviours and patterns of using e-learning platforms and m-learning applications in the organisations, it plays a huge role in upgrading the e-learning/m-learning ecosystem and developmental pathways and supports upskilling by customising a learning journey as per the need of the hour for the learners, the learning practitioners of course/ pathways/ journey, the technology platform providers and the organizations indeed. 

The effective use of this discipline lies in the approach we choose... These approaches may be dependent on the stage of the learning agenda the organization is up to. The four most utilitarian approaches to nudge on the efficiency of Learning Analytics can be categorised as:

• Descriptive  -   Used to explain the existing outcomes of a certain ongoing learning journey. Ex. Adoption Rate, Engagement Rate, Engagement Rate, Completion Rate and more
• Diagnostic -    Used to understand gaps and evaluate options/ solutions as course corrective measures to improve the learning experience for a learner. Ex. Improving the Assessment Strategy, Change of Content, Increasing the number of quiz attempts looking at trial rates and success rates and more 
• Predictive    -  Used to map out a practical future situation of an in-hand task. Ex. Suggesting at-risk learners, Planning extra courses/ modules based on future needs, Interests gathered from the discussion boards
• Prescriptive - Used mainly for simulating the possibilities of future strategies for both learner and a learning practitioner. Ex. Learner getting suggestions on possible next set of courses one should take up. 

The plummeting physical human interactions due to the ongoing pandemic of Covid-19 is foreseeably skyrocketing the use of e-learning avenues and hence as practitioners, the learning analytics utility at work is in the attempt to rapidly adapt to business requirements. The more time we spend online, the more data trails are available for organizations  & learning practitioners to leverage the true potential of data and harvest our learning agenda for good. The constant monitoring of our experience of online learning or engagement platforms and our performance metrics provides organisations with an incentivised exposure into what will lure us towards them. This has in turn increased the quality offered to the learners in the learning experiences. 

Modern Technology has made the discipline of learning analytics a fast-paced facilitator of implemented knowledge. It is highly equipped to provide a quality output from the information available. The ongoing pandemic demanded an instant change in our ritualistic routines and restructuring viable platforms which are eased by Learning Analytics. It lives up to its promise of being a meticulous discipline that provides a high value and efficient output leading to an exponential heightening of pre-existing domains. 

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