Here’s the reality—today’s world is driven by data, and corporate training is no exception. It empowers organizations to make informed and relevant L&D decisions. But what happens in the absence of data? L&D decisions are based on educated guesses, hunches, opinions, and past patterns. Do you think these decisions guarantee effective training or create the desired business impact? Probably not! Here’s where learning analytics works like the lens through which organizations can view and plan better changes at the course or strategy level.
While you can find different definitions of learning analytics across the web, here’s one you can rely on.
There are 3 major elements in this definition:
It is important to note that learning analytics will only bear fruits if there is action.
Getting started with learning analytics can be challenging. For instance, it can be overwhelming to figure out where to start, coordinating with different functions such as IT, ensuring expertise in eLearning, Instructional Design, LMS, analytics, and so forth. Nevertheless, any effort will not be in vain since leveraging learning analytics in Learning and Development comes with many benefits.
Let’s review the benefits from the perspective of 4 different types of learning analytics. We’ll start from the basic level and move toward the sophisticated ones. Note that the more complex an analysis, the more insights it garners.
Descriptive analytics will give you answers to questions about what happened.
For instance, a retailer will learn about the average monthly sales and for a healthcare provider, the number of patients admitted in a week. Similarly, with eLearning, you can find the number of course enrollments, pass percentages, assessment scores, and so on.
Descriptive analytics collate data from multiple sources to give insights about past performance. This data can be used to make relevant decisions that will impact future training programs.
For example, if the data shows increasing dropout rates, you might take steps to improve the training content or switch to an engaging learning strategy. These discoveries allow you to enhance training programs and even eliminate courses that are wasting the organization’s money and resources.
However, descriptive analytics is limited to indicating that something has occurred, without explaining why. If your organization is looking for in-depth insights, you can combine descriptive analytics with other types.
Diagnostic analytics can be used to drill down and ask questions about why something happened.
You can figure out the dependent elements as well as identify patterns to get insights into a particular problem or opportunity. For instance, data from diagnostic analytics might show that an eLearning course on customer service experienced low completion rates among senior executives while new hires found it effective. Further diagnosis found that the course content was too basic for the senior executives, suggesting the organization needs to roll out an advanced level of customer service course for them.
In a way, the deeper analysis highlighted the need to cater to the specific needs of learners and offer a more personalized learning experience. This would help ensure the training program is not redundant while impacting all learners’ performance positively.
As the name suggests, predictive analytics tells what is likely to happen.
It builds on the findings of existing data to forecast the future. However, it is important to remember that predictions are just an estimate, and the accuracy highly depends on the quality of data and stability of the associated situations. Hence, it is important to analyze the data carefully.
Predictive analytics can help identify the probable difficulties learners might face during a learning experience. This allows L&D managers to create opportunities that provide early intervention and targeted support. Moreover, predictive analytics can be used to enhance the quality of training and raise the engagement ratio.
For instance, let’s say data from a post-course survey revealed that some learners did not prefer accessing the eLearning program from a desktop. Since most of them are hard-pressed for time and often on-the-go, they prefer accessing the training anytime, anywhere on their mobile devices. In this case, learner profiles and predictive analytics can help you zero-in on and offer solutions in microlearning formats that meet individual needs.
The purpose of prescriptive analytics is to find solutions to questions about what should be done.
Simply put, apart from finding solutions to what will happen, it should help in understanding why it will happen. More so, prescriptive analytics can help you strategically plan for training interventions.
Let’s take this example, there is a curriculum of eLearning courses that need to be rolled out to employees in the manufacturing industry. Learner surveys on courses conducted in the past revealed 2 aspects. The courses excel theoretically; however, it would be beneficial if learners could learn how to transfer or apply this learning to their work.
In this scenario, simulations can be progressively delivered to help learners apply the learning in a simulated environment. This, in turn, would increase the impact and value of the training program.
Again, today’s world is driven by data. Learning analytics offer decision-makers deeper insights into how corporate training programs are aligned with organizational goals and individual learning needs. There is a tremendous opportunity for L&D leaders, and their stakeholders, to make data-driven decisions and, most importantly, to use learning analytics. If your organization hasn’t started using learning analytics to enhance the quality as well as the ROI of your training programs, it’s time to give learning analytics a serious thought. For comprehensive insights on getting started with learning analytics, join this webinar and download the free eBook Leveraging Learning Analytics To Maximize Training Effectiveness - Practical Insights And Ideas.