Exploring Learning Analytics: How is it affecting corporate learning?

Posted on : January 16th, 2015
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With changing technologies, the learning industry is changing its expectations from e-learning as well.  While until a few years ago e-learning was simply making learning available and accessible to learners, today learning professionals are keen on uplifting the user experience and want more out of the learning experience. One of the main concerns for L&D managers today is to make sure that the learning provided is aligned to the needs and requirements of the learners. This is reflected in the industry-wide interest in Big Data and adopting newer standards like Tin Can API that can help make sense of the data to churn useful inputs on learner experience. The trend of studying learner analytics to create adaptive learning is taking the learning industry by storm and the possibilities are endless. We had explored some of the opportunities and challenges of adaptive e-learning in a previous post. With more and more organizations open to the merits of utilizing the power of Big Data to create better learning for their employees, here are some ways that Learning Analytics is affecting corporate learning:

  • Measuring Efficacy: Studying learner analytics, learning managers can ascertain how well an e-course impacts the learner. With e-learning, it has always been possible to monitor and store learner responses within the course. That helps in generating reports on how the learner performed on the various quizzes or tests within a course. But with advanced learner analytics technology, it is now possible to record how much time a learner spends on a page, how many external links he or she clicks within the course, how many times does he or she refer a course. This gives a comprehensive report on the efficacy of the course – which emerges from different experiences within the e-course and not just the result of a quiz or test which measures retention.program-cpmpletion-report
  • Taking remedial measures: From the exhaustive learner data, it is also possible to identify if an e-course or a portion of an e-course is not working very well. In the previous scenario, learning managers had to wait for learner feedback to come in — which is mostly at the end of a course. By then, changing or refining the course becomes often a tedious or even impossible task, without going back to the drawing board. But with newer learning technologies, it is possible to track the learners’ progress as he or she takes up an e-course. This data comes quickly and using learning analytics, it can be revealed if learners are taking too much time over a portion or just skipping it due to disinterest. The sections of the e-course can then be quickly changed to create ‘adaptive’ learning for the new-age learner.
  • Comprehensive summation of learning experiences: With newer learning software like Tin Can API, it is possible to monitor and manage a variety of learning experiences – both online and offline. This includes modern ways of learning – mobile learning, game-based learning, simulations and virtual worlds, social and collaborative learning as well as experiential learning. This gives us a true and wider picture of the learners’ experiences.
  • Identifying successful learning activities and patterns:  From the true picture of the learner experiences, it is possible to identify learning activities that are successful for the majority of learners and patterns that lead to the success of the learner in the long run. L&D professionals can then provide learning which aligns to these patterns and can steer their learners towards them to ensure that the entire learner group learns well and goes on to perform better in their area of work.  For instance, an identified successful pattern in social learning can be the number of posts or contribution a learner makes in a week. With the clear indication that learners who are more active on the social learning platform are performing better at work as well, more learners can be encouraged to follow the same path to ensure success of the whole group – within the platform and at work, where it matters the most.
  • Non-learning related benefits: The learner data does not just reside on the learning platform. Newer technologies ensure that comprehensive learner data can be transferred to other systems within the organization, for instance the HRMS. This helps in creating better compensation packages and takes supportive measures for employee retention.

Before announcing the obvious – that learning analytics benefits learners, managers and the organization alike, it is important to consider that this is not a completely fool-proof system. Along with the voluminous data churned, there will be emerging patterns which may not have much relevance to learning preferences or impact of learning. For instance, learning analytics may reveal that most learners with English as their native language perform better in a course. This could lead some to speculate on the efficacy of the chosen language of delivery, but the truth may be that the highlighted group has the best access to the course. If the accessibility of the course is increased and other learner groups also have the same advantage, they too will start accessing the course more and perform just as well. Correlation between emerging trends has to be scrutinized before making changes in the learning objective or the course itself.

So, while learning analytics makes a lot of sense, it has to be utilized judiciously to bring actual benefits on the table. As newer learning norms and standards make their presence felt, it is a time marked with change – a definitive change to create learner-centric training and provide learning that is required, learning that helps. If you want to explore more about learning analytics, please write to us at [email protected] or fill the following form:

Arunima Majumdar

Arunima is the Marketing Head at G-Cube. She loves exploring and blogging about innovations in training & learning for the new-age corporate sector.

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