Learning analytics is a multidisciplinary approach to using learning content. While there is no universal agreement on a definition of the term, Wikipedia defines learning analytics as “the measurement, collection, analysis, and reporting of data about learners and their contexts for the purposes of understanding and optimizing learning and the environments in which it occurs.” This is a definition that is often seen in learning literature although details are contested.
Learning analytics (as an activity) is used more often in higher education, possibly due to the lack of a definitive definition among enterprise organizations. Analysis of learning does take place in corporate and government settings, but the results may be less meaningful due to the lack of a definition, the general lack of rigor in measurement, and spotty collection and reporting. In many cases, the activity is driven by the demands of the SCORM standard, and data may be limited to attendance and criterion test scores.
What can you do by using learning analytics
Be those limitations as they may be, learning analytics is (or would like to be) the “Gold Standard” for evaluating training, the basis for understanding the quality of learning operations, and the guide for the development and revision of content and methodology. As a subset of data analysis, learning analytics perfected can serve as:
- A prediction model
- A generic design framework
- The basis for data-driven decision-making to present learning paths or courses of action
- An application of analytics
Learning analytics provides organizations with actionable insights that can be used to optimize training programs and enhance employee development. The process of making this change begins with identifying the actual skills and competencies that employees must have, then identifying skill gaps in the workforce and leadership in the organization.
Learning behavior analysis helps instructional designers and other stakeholders understand how employees engage with training materials. The analysis involves consideration of completion rates, quiz scores, and other classic learning indicators, along with targeted data. It is probable that additional metrics will have to be created to develop the necessary data relating to forecasting future skill needs and talent gaps. The process can result in defining personalized learning pathways through predictive analytics, and this will take time and involvement of subject matter experts, stakeholders, and decision makers.
Data analytics not only benefits organizations but also plays a crucial role in improving learning experiences for employees. Tailored learning paths will result in recommendations for experiences (not necessarily “courses”) based on individual needs, career goals, and proficiency levels. Other actions will be needed in order to:
- Implement real-time feedback and support
- Incorporate elements such as gamified elements
- Support on-the-job performance (including AI-powered chatbots for on-demand assistance)
- Quantify the impact of learning initiatives to ensure a return on investment (financial benefits of improved skills and cost savings through reduced turnover)
- Calculate the financial benefits
- Measure employee engagement and satisfaction
- Link L&D programs to increased employee retention
The future of data-driven L&D
As organizations continue to harness the power of data analytics, the future of L&D looks promising due to:
- Integration of emerging technologies, including artificial intelligence and machine learning for predictive analytics
- Implementation of virtual reality and augmented reality for immersive training experiences
Data and learning analytics are rapidly emerging as cornerstones of modern enterprise L&D. By embracing data-driven strategies, organizations can not only enhance employee skills and performance but also foster engagement and a culture of continuous learning and development. Staying ahead requires more than just keeping up; it demands leveraging the power of data to empower your workforce and drive innovation and success. As the journey towards data-driven L&D continues, organizations that embrace this evolution are poised to thrive.