Corporate training evaluation focuses mainly on measuring retention and skills acquisition. The goal is to determine how successful a learning intervention has been. An evaluation process typically measures positive and tangible results in the workplace—often in the form of behavior change or increased efficiency.
Although this approach isn’t wrong, the best way to improve a learning program is to find out where it could fall short. Predictive learning analytics identify the pain points and gaps by gathering the right data.
Moreover, you can use predictive learning analytics to gain valuable insights into “scrap learning,” or the aspects of learning content that people never actually use in the workplace. In other words, predictive learning analytics focuses on individual learners, predicting whether or not those learners actively apply concepts from training initiatives.
What are the types of learning analytics?
Predictive learning analytics is a tool that leverages data to enhance training outcomes. But before diving into the specifics, it’s important to understand a few broader categories. There are four primary types:
Descriptive
Descriptive analytics focuses on a collection and analysis of historical data to identify trends and patterns. For instance, tracking course completion rates and assessment scores falls under descriptive analytics. This type of analysis helps organizations understand past performance and identify areas for improvement.
Predictive
Predictive analytics forecasts future outcomes based on historical data. Statistical algorithms and machine learning techniques are able to predict learner behavior, performance, and engagement. You might leverage this type to predict which employees will excel in specific training modules or identify who might be at-risk.
Diagnostic
Diagnostic analytics looks under the hood—why did something happen? This analysis digs deeper into the data to uncover the root causes of specific outcomes. If a particular training program has low completion rates, diagnostic analytics can help identify underlying issues, such as content complexity or low learner engagement.
Prescriptive analytics
Prescriptive analytics provides actionable recommendations based on other types of insights. Learning and development leaders can then decide on a new and better course of action to achieve desired goals and outcomes.
Predictive analytics in an LMS
When a learning management system (LMS) is equipped with predictive analytics capabilities, it can completely revolutionize employee training and skills measurement. Such a transformation is the direct result of things like:
- Personalization – Build personalized learning paths to optimize each employee’s development journey, full of relevant and effective training content.
- Enhanced course design – Identify which elements need improvement, and enable continuous optimization of training materials for impactful learning.
- Better resource allotment – Pinpoint high-impact training modules in order to better allocate training budgets and invest in initiatives that deliver the highest ROI.
- Improved compliance training – Simplify compliance training by recognizing employees who need refresher courses or additional support in order to stay compliant with industry regulations.
- Data-driven leadership decisions – Continuously refine training strategies to make sure that they remain aligned with business goals and evolving workforce needs.
Using predictive learning analytics at your organization
Learning specialists constantly search for new tools to boost results related to knowledge retention and skills achievement. Although predictive learning analytics delivers in these areas, success requires thorough planning, ongoing support, and buy-in from leadership.
Business leaders across several industries have already rallied around the benefits of predictive learning analytics for their organizations, thanks to the use of emerging digital transformation technologies. These include data mining, predictive modeling, and machine learning.
When used in combination, these tools identify and measure patterns in learning data and theorize learners’ future behaviors—which has direct applications for e-learning.
Building a predictive learning analytics algorithm
Predictive learning analytics work best if you have a specific goal in mind when you build the algorithm. Your company's LMS is already gathering and storing valuable data, making it a great place to start for delivering comprehensive reports.
Think about predictive learning analytics as a micro-tool, allowing you to glimpse learner behavior in a more granular way.
The key is to stay focused and specific. For example, figure out which employees are most likely to apply skills and which ones aren’t. To accomplish this, you’ll need to look at how learning performance correlates to other outcomes. You might use indicators such as quiz scores, participation in group discussions, or completion times.
Key takeaway: Predictive learning analytics focus on individuals and small pieces of learning content. It’s not a “one-size-fits-all approach.” Instead, learn as you go, and stay mindful that each situation is different. You may be able to recycle part of an analytics algorithm you already built with some modifications.
How predictive learning analytics improves your training programs
Here are seven ways to use predictive learning analytics to deliver successful training programs:
1. Harnessing the potential of xAPI for actionable insights
xAPI is very popular but still has many underutilized functions. This learning technology facilitates communication between various learning tech products. The main advantage is that it helps evaluate learner performance on the job instead of on a test.
xAPI also offers valuable insights into the effectiveness of training programs and can track everything the learner does—from games and mobile apps to tasks that require practical action.
Predictive learning analytics algorithms tap into the massive data collected by xAPI. Then, you can establish a more complete picture of the entire learning experience. These analytics can give accurate predictions of future learner and training program performance. Thus, they help L&D specialists tailor learning materials to fit individual needs.
2. Improving learning program content
Learning success heavily depends on learner engagement. Predictive analytics can offer valuable data about trending topics that are most relevant to modern learners.
Predictive data can also help learning directors pinpoint the best moments to promote certain courses or introduce new learning outcomes. For example, the times of the year and even exact hours when employees are likely to be interested in training may change due to several factors, including workloads and broader corporate priorities.
This information is specific to industries and organizations, and the right predictive algorithms take those factors into account.
3. Reducing scrap learning
“Scrap learning” is knowledge that’s metaphorically discarded once the course is over. If that knowledge isn't applied on the job, it could be a missed learning transfer opportunity. Scrap learning is a waste of resources, especially when nearly every organization aims to maximize ROI.
Research shows that 20 percent of learners use what they learn. However, 65 percent try to apply it but revert back to their old ways. This amounts to a staggering 80-85 percent scrap learning.
Predictive learning analytics identifies the learners who are most likely to apply the knowledge on the job. It also identifies the obstacles that keep others from doing so.
4. Incentivizing the modern learner
Today's learners are self-directed and want to control how they spend time and energy. Offering them immediate and direct feedback about performance is helpful for motivation.
Predictive learning analytics is useful for identifying actionable metrics to share with learners. Learners can use this data to decide the best path for their own learning and time investment.
Other tools, like gamification, make the learning process more competitive and entertaining. Learners have a chance to win awards, gain skills badges, and compare their performance to other learners in real-time.
5. Improving course design
With predictive learning analytics, L&D specialists know when learners complete a course and what they did while taking it. This information predicts when specific learners might experience difficulty or even drop out of a specific e-learning course.
Instructional designers can then find the right solutions to fix training performance issues. They can decide whether the problem is the difficulty level, navigation, or the curriculum itself. Again, the specificity of predictive learning analytics is helpful at eliminating guesswork and making targeted improvements that benefit all learners and participants.
6. Achieving “just-in-time” learning interventions
Not all learners move simultaneously through e-learning courses. Some learners have pre-existing knowledge that gives them a boost, while others need more time to absorb new concepts.
Predictive learning analytics gives L&D specialists the chance to step in and make targeted adjustments. As a result, they can help learners who need it most to prevent disengagement or course abandonment.
Instructors can easily monitor learner progress by comparing specific metrics against general course performance. For example, consistent low quiz scores and lack of discussion could equal low engagement.
Sequence drop-down exercises are great for verifying learners’ understanding of a sequence of events in a task or process. Arranging the items in the right sequence is necessary for the question to be graded as correct. Identifying individuals that struggle to find the right answers allows L&D specialists to offer assistance or incentives more quickly.
7. Making better L&D decisions
When the job market is especially volatile, decisions related to employee development are crucial. These choices often extend beyond the L&D department and have implications for bigger organizational projects or incentives.
Having the right skills can make or break an organization. So, stakeholders need all the data (and actionable insights) to make better decisions that help drive the desired learning and development results.
Bringing it all together
In an age of big data and high-performing LMS platforms, leveraging the power of learning information is critical. Predictive learning analytics effectively looks beyond past performance and enables future results, making it a game-changer for corporate L&D.
Start unlocking the full potential of your workforce with CYPHER. Schedule a demo today.
