Corporate training evaluation focuses mainly on information retention and skills acquisition. The goal is to determine how successful a learning intervention has been. This involves measuring the positive results in the workplace – usually in the form of behavior change or increased efficiency.
The approach isn’t wrong. However, the best way to improve a learning program is to find out where it falls short. Predictive learning analytics can help identify the pain points and gaps by gathering the right data.
Moreover, you can use it to get valuable insights into scrap learning. That is, the parts of the learning content people never use in the workplace. In other words, predictive learning analytics focuses on individual learners. It predicts whether or not learners apply what the training programs teach.
Using predictive learning analytics in your organization
Learning specialists are constantly looking for new tools to boost their programs’ results. Predictive learning analytics promises to deliver this. But it takes a lot of planning and ongoing support from all stakeholders. You’ll likely need to modify some of the company procedures to accommodate predictive learning analytics. So, leadership buy-in must be established from the start.
It's also a good practice to choose a high-stakes learning program as your pilot project. Maybe even try to build an in-house predictive learning analytics solution before purchasing a ready-made one. The latter may prove rather costly. More so, it might not always match your organization's needs, especially when you aim to minimize scrap learning.
Business leaders across several industries have already rallied around the benefits of predictive learning analytics for their organizations. This type of analytics leverage digital transformation technologies. These include data mining, predictive modeling, and machine learning. Hence, they identify and measure patterns in learning data and theorize learners’ future behaviors. This applies to e-learning as well.
Building a predictive learning analytics algorithm
Predictive learning analytics work best if you have a specific goal when you begin building the algorithm. Your company's LMS is already gathering and storing valuable data.This is great for delivering comprehensive reports.
Predictive learning analytics are more of a micro-tool. They aim to give you a glimpse into learner behavior after completing a module or an entire unit. The key is to stay focused and not generalize. For example, you want to figure out which employees are likely to apply skills and which aren’t. Therefore, you’ll need to look at whatever low performance has meant for previous courses. The indicators could be quiz scores, participation in group discussions, completion times.
Predictive learning analytics focus on individuals and smaller pieces of learning content. Hence, they are not a "one-size-fits-all" recipe. It's good to learn as you go along but be mindful that each situation is different. You may be able to recycle part of the algorithm you already built. However, you'll also have to customize it. What you can keep is the predictive modeling methodology.
Ways in which predictive learning analytics can improve your training programs
Predictive learning analytics stands out as it brings insights for decision-making. The goal is to have simple and intuitive dashboards to make business projection easier. 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 interoperability 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. It offers valuable insights into the effectiveness of training programs. xAPI can track everything the learner does. From games and mobile apps to job tasks that require putting learning into practice.
Predictive learning analytics algorithms can tap into the massive data collected by xAPI. Then, it can provide a complete picture of the whole 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. Improved learning program campaigns
The success of a course depends on learner engagement. The idea of training has to be appealing to them. Predictive learning analytics can offer valuable data about trending topics and those declining in popularity. Also, they can determine the best moments to promote courses. For example, the times of the year and even exact hours when employees are likely to be interested in training.
This information is specific to industries and organizations. It’s important to know that predictive learning analytics can gather granular data. Thus, it will help you decide when and what to promote to learners.
3. Reducing scrap learning
Scrap learning is the knowledge that’s metaphorically discarded once the course is over. If that knowledge isn't applied on the job, we can consider it a missed learning transfer opportunity. With every organization looking to maximize training ROI, scrap learning is a waste of resources.
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 can identify 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 learner
Today's learners are self-directed and want to control how they spend their time and energy. Offering them immediate and direct feedback about their performance is excellent for their motivation. Predictive learning analytics can identify actionable metrics you can share with learners.
The key is to present them in a manner that is appealing and easy to understand. Learners can then decide on the best path for their own learning. Also, they will feel genuinely involved in their development.
You can do this by presenting a graph of how past learners have improved performance on the job. Furthermore, when learners tend to get demotivated, you can include leaderboards showing where they rank. Then, highlight (in percentages, time, or number of units) how far they have until they finish a course or learning path. Thankfully, you don’t have to create these for each learner, as your learning management system can already do this for you through easy-to-understand learner analytics.
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 data helps predict when specific learners will have difficulty moving forward or even drop out of the course.
Instructional designers can then find the right solutions to fix training performance issues. They can decide whether the problem is the difficulty level or the navigation. Once more, the specificity of predictive learning analytics insights is enormously helpful. It doesn't leave room for guessing. You don’t have to wait for learners to finish a course to improve the course design.
6. Just-in-time learning interventions
Not all learners move simultaneously through e-learning courses. Some are more knowledgeable or quicker at grasping a specific concept, while others may struggle. Predictive learning analytics give L&D specialists the possibility to step in. Thus, they can help learners who need it. So, that they don’t get demotivated and drop out of the course.
Instructors can easily monitor learner progress by comparing specific metrics against what they generally mean for course performance. For example, consistently scoring low on quizzes and drop-down exercises indicate 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.
7. Making better L&D decisions
The job market is especially volatile at the moment. And decisions related to employee development extend beyond the L&D department. 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 results.
In the age of big data and high-performing LMSs, it’s a waste not to leverage the power of information. Predictive learning analytics is an effective way of looking at past performance. This allows to improve future results, and it can be a real game-changer for corporate L&D. Predictive learning analytics is an enormously valuable resource to have. It enables instructors step in and offer just-in-time interventions to incentivizing learners and helps stakeholders make informed decisions.