The Critical Role of Learning Analytics in Education and Development

Decision-makers can better understand how corporate training programs match company goals and individual learning needs through learning analytics.

FREMONT, CA: The reality is that today's world is data-driven, and corporate training is no exception. It enables firms to make educated and pertinent learning and development decisions. However, what happens when there is no data? Decisions about L&D are made based on educated guesses, hunches, views, and historical patterns. Do users believe that these choices ensure successful training or the desired business impact? Most likely not! This is where learning analytics shines like a prism through which enterprises may view and plan for a course or strategy-level changes more effectively.

This definition contains three critical components:

Data

The fundamental asset that enables analytical understanding

Analysis

It entails conducting research and imbuing data with intelligence using algorithms

Action

Taking action toward informing judgments, utilizing acquired insights, and achieving the objective enhance students' performance.

It is critical to keep in mind that learning analytics will bear fruit only if there is action.

It can be challenging to get started with learning analytics. For example, it can be hard to determine where to begin, working with many functions such as IT and guaranteeing expertise in eLearning, Instructional Design, LMS, and analytics. Nonetheless, any work will not be useless, as integrating learning analytics in Learning and Development has several advantages.

Consider the advantages from the perspective of some distinct forms of learning analytics:

Descriptive Analytics

Descriptive analytics can provide users with information about what occurred.

For example, a merchant will learn the average monthly sales, while a healthcare facility will learn the average weekly patient admissions. Similarly, with eLearning, users may track course enrollments, pass rates, and assessment scores.

Descriptive analytics assemble data from a variety of sources to provide context for prior performance. This information can be utilized to make informed decisions about future training programs.

For instance, if data indicates that dropout rates are growing, users might improve the training material or implement a more engaging learning technique. These insights enable users to better training programs and perhaps eliminate courses that are a waste of money and resources for the firm.

However, descriptive analytics is confined to reporting that something occurred without explaining why it happened. If a firm requires more detailed insights, users can mix descriptive and other types of analytics.

Diagnostics Analytical

Diagnostic analytics can be used to delve deeper and elicit information about how something occurred.

Users can deduce dependent aspects and patterns to get insight into a specific problem or opportunity. For instance, diagnostic analytics data may reveal that a customer service eLearning course had low completion rates among senior executives but was effective with new hires. Further diagnosis revealed that the course content was too rudimentary for the top executives, indicating that the firm should offer them advanced customer service.

In some ways, the deeper examination emphasized the importance of catering to learners' unique needs and providing a more personalized learning experience. This would help guarantee that the training program is not repetitive while also positively improving the performance of all learners.