According to CEB, scrap learning is a serious problem, making up 45% of learning investments in the average organization. In an effort to minimize this wastage, L&D departments are increasingly turning to sophisticated learning analytics solutions to help them better understand their training data. These efforts have certainly helped organizations make great strides in their understanding of existing training initiatives. However, they are also limited in the sense that they are mostly backward-looking, and tend to emphasize the past instead of the present and future.
Enter Predictive Learning Analytics (PLA), a set of methods and technologies used to model future learner outcomes. By identifying patterns and trends in historical data, organizations can make predictions about how learners are likely to behave in future training programs and on the job.
Let’s take a look at four uses for PLA in your organization:
1. Alerting Instructors to Intervention Opportunities
In any given course, trainees will vary in how quickly and easily they progress through the material. Trainees who lag behind can experience a negative “snowball effect”, where slow progress leads to discouragement and further difficulty in learning advanced concepts.
The best way of dealing with this problem is to provide appropriate coaching and support when trainees start to fall behind. Unfortunately, that is often easier said than done, as it can be hard to know who is struggling with the course material. With PLA, instructors can keep a close eye on trainee progress by comparing specific metrics against what they typically mean for course performance. For instance, consistently low quiz scores combined with a lack of forum participation might indicate a trainee who’s not actively engaged. This will allow instructors to identify opportunities for intervention with those specific trainees.
2. Giving Trainees Appropriate Guidance
Another way to increase the success rate of training is to provide the trainees themselves with direct feedback about their performance. Organizations can use PLA to identify actionable metrics for trainees, and display these metrics in a way that’s appealing and easy to understand. This can help trainees chart the best course for their own learning.
3. Helping Course Designers to Improve Training Programs
Timely individual interventions are not the only way to reduce scrap learning. Sometimes, there can be more fundamental problems with the course itself. By providing a clearer picture of how specific course elements influence learning results, PLA can be a powerful tool for improving course design.
An effective implementation of this is found in the Curtin Challenge platform. Curtin Challenge’s administration dashboards include information on drop-off rates, student ratings of various modules, and weekly participation metrics. Consistent activity drop-off could indicate a lack of engaging material, or an issue with the level of difficulty. In this way, designers were able to monitor learning performance and refine future iterations of the course.
4. Allowing Managers to Make Better Decisions
In a knowledge-based economy, the importance of personnel development extends beyond the L&D department. Managers and administrators often need to understand the process and expected results of training to make better decisions. Depending only on backward-looking analytics is like driving while looking only at the rear view mirror. . . comforting, but dangerous. To increase the effectiveness of your training programs, it is essential to consider the future; and the best way to do that is by implementing predictive learning analytics.