Given that 96% of C-suite Fortune 500 CEOs report interest in understanding the investments and impact of their company’s learning and development initiatives, it becomes increasingly urgent for L&D leaders and teams to leverage data when it comes to planning skill-development in the months to come as well as revising the current programs in place. This is where learning analytics comes in as a key component of the ever rising data analytics in the business community.
In the current hybrid working environment, organisations tend to have either an LMS or an LXP in place, and such systems of digital learning collect vast amounts of user data which can be sorted, filtered, and analysed to look for patterns and insights to solve problems.
Learning analytics is this very process of measurement, collection, analysis, and reporting of information concerning training participants in order to understand and optimise the process of learning and the environment where this process takes place.
As organisations plan to increase their budget for learning by 57% according to LinkedIn’s Workplace Learning Report 2021, measuring the ROI and effectiveness of learning programs especially in line with business goals becomes a fundamental course of action.
What are the metrics L&D leaders need to look out for?
There tends to be three main types of statistics provided by your LMS: engagement statistics, performance statistics, and course or site helpdesk. Engagement statistics include site logs, location/IP, course access, and time spent which tells you about the learners’ preferred activities. Performance statistics include gradebook scores, self-assessments, and online learner feedback. Finally, the course or site helpdesk includes the frequently asked questions and issues raised by your learners.
When it comes to learning analytics, it can be broken down into descriptive, diagnostic, predictive and prescriptive analytics.
Descriptive analytics can be used to track engagement, test scores and participation rates. This kind of information will enable L&D professionals to identify patterns that will help them determine which content is useful to employees and which content is creating frustration or confusion.Diagnostic analytics enables them to figure out the dependent elements as well as identify patterns to get insights into a particular problem or opportunity while predictive analytics builds on the findings of descriptive analytics to forecast the future.
But such predictions are only an estimate, and the accuracy highly depends on the quality of data and stability of the associated situations. Nevertheless, this form of learning analytics is particularly useful when it comes to creating Personalised Learner Experiences.
Personalised learning experiences are growing more popular due to their ability to increase the speed, efficiency, effectiveness of employee training while balancing diverse learning styles and needs. Finally, prescriptive analytics help L&D professionals to strategically plan for training interventions and increase its value and impact.
How do we translate data into action-oriented strategies?
We now know the metrics we need to keep a watch for and we also know that these metrics serve to increase the effectiveness of L&D programs for the future as well as the present. Having an LXP dashboard in place then becomes an important tool to navigate the learning landscape from skilling strategies to creating a burning impact. L&D leaders are aware of the increased urgency to align L&D programs with business goals while implementing KPIs for effective measurement and deriving meaning from the data.
But the question remains: How do we translate our learnings into effective strategies? Here are some answers we have found.
Using learning analytics in combination with qualitative metrics:
Employee feedback is one of the best ways to get a true understanding of employee learning experiences and perceptions. Accordingly, spaces have to be provided for them to be heard. When organisations value the opinion of individual employees and want to analyse their experiences, conducting post-rollout interviews, or sending questionnaires to employees that target their experiences will support in devising strategies to improve the engagement and effectiveness of L&D programs.
Emphasising on measuring behavioural impact of the learner:
This level of evaluation has to do with measuring knowledge application and is often done by monitoring employees on the job after the training takes place to see if they apply what they have learned while working.However, learning analytics can also be used to measure knowledge application from online simulations where employees work through life-like scenarios to practice applying knowledge. This is an effective way to let employees practice their new skills and knowledge without risk, while at the same time gathering data about the effectiveness of training with a wider lens.
Engaging in best practices for KPI setting of L&D programs:
L&D analytical processes should be planned with a focus on the employee life cycle in the organisation. Further, the indicators need to be adapted not only to the organisation’s goals but also the stage at which the L&D department is at the moment. This is where Disprz’s Learning Maturity Survey becomes helpful. Essential indicators such as the impact of the training on individual performance, employee engagement, team effectiveness, and facilitation of business processes also need to be accounted for.
Organisations are all set on making investments in creating career paths and challenging opportunities for employees, developing leaders and succession planning.
To back these investments, learning analytics not only helps understand the ROI of the current L&D programs but also identifies critical points of improvement. As most important business decisions today tend to be made on the basis of detailed data analysis, a learning solution that enables organisations to meticulously analyse learning outcomes will not only improve current employee success, but also build a strong learning culture for the future.