Predictive Learning Analytics: 4 Ways To Use It And Decrease Ineffective Learning
How much of your job training program is scrap learning? One study revealed that about 20% of learners never apply their training to their job, and almost 67% of learners try to apply their training but revert to their previous habits. Another study found that 45% of training content is never applied. Scrap learning, or ineffective learning, can be costly to an organization’s bottom line.
For Instructional Designers -and anyone monitoring the Return On Investment of your training program- that’s disturbing news. But if you know the possible causes of ineffective learning, you have a good chance of improving your numbers. Typical causes include:
- Content isn’t relevant to training goals.
- No opportunity to apply new knowledge.
- Learners aren’t in the right training.
- Lack of managerial support.
- Misalignment with goals and priorities.
Predictive Learning Analytics, or else PLA, can help you identify and mitigate these causes to make your online learning programs more effective at changing job behaviors. Predictive Learning Analytics takes Learning Analytics, also known as LA, to the next level. Rather than simply understanding what already happened, Predictive Learning Analytics lets you understand what is likely to happen. It predicts learners’ future success.
Predictive Learning Analytics relies on a collection of techniques that identify and measure patterns in learning data and extrapolate future behaviors. It helps them avoid, for example, applying what they’ve learned based on past trends.
Predictive Learning Analytics is different from other metrics because it focuses on the individual learner, rather than the learning program as a whole. This makes Predictive Learning Analytics uniquely helpful in tackling the problem of ineffective learning. Predictive Learning Analytics allows you to determine who did and did not learn the material, and who is most, or even least, likely to apply the things they learned to their jobs.
How To Use Predictive Learning Analytics
Predictive Learning Analytics works best when it involves each of the stakeholders, such as learners, instructors, managers, and course administrators. Here are some practical ways your company can apply Predictive Learning Analytics to decrease ineffective learning.
1. Empower Learners
The simplest way of reducing ineffective learning is to warn your learners when they’re at risk. Dashboards like Purdue University’s Course Signals uses symbolic traffic lights as a feedback mechanism to let learners know how they’re doing—red for at-risk, green for on-track.
Keeping learners in-the-know as they progress through online training allows them to make the adjustments they need in order to solidify their learning and develop positive habits that will carry over into their daily work tasks.
2. Warn Instructors
Instructor dashboards can identify trends to enable early intervention. For instance, some applications can notify instructors of at-risk learners and make recommendations for intervention. Other tools let you see how individual learners are performing, compared to other learners. You can monitor their status in terms of their predicted success and intervene as necessary.
3. Notify Supervisors
Supervisors may need to know if employees show signs of ineffective learning. You can use Predictive Learning Analytics tools to send notifications to supervisors, so that when learners apply their training to the job, supervisors can monitor their progress and watch for indicators that the learning isn’t being applied.
4. Develop Training Programs
Predictive Learning Analytics can also help drive organizational training policies by helping you to map out a training program for new hires and veteran employees. Using Predictive Learning Analytics data, you can develop an online learning program that progressively trains new employees and provides refresher modules or advanced training as they continue their work at the company.
How To Set Up Predictive Learning Analytics
Successfully using Predictive Learning Analytics in your company [1] requires thoughtful planning and preparation. You’ll need organizational support from executives and other stakeholders, and you’ll probably need to update your policies and procedures to accommodate PLA-related changes. Also, consider what skills and tools you’ll need to make the setup and long-term management successful.
There are many Predictive Learning Analysis tools to choose from, and the ones you invest in will depend on the particular needs of your company. There are three basic approaches you can take:
- Create an in-house solution that’s tailored to your specific purposes for PLA.
Bear in mind that developing your own tool requires a long-term commitment to maintenance, troubleshooting, and continual improvement. Be sure you’re able to invest the resources into this kind of project before you begin. - Purchase an out-of-the-box solution.
These tools are faster to implement than a custom solution, but costs can accumulate when you need to integrate with other applications. Customization is minimal, and security updates can be infrequent. - Invest in a collaborative system.
In some cases, several organizations work together to build a PLA system, but usually, this is limited to academic institutions rather than corporate organizations.
Once you’ve implemented your predictive learning analytics solution, you’ll need a way to intervene. An intervention is any action that is designed to improve outcomes for a learner. An intervention can be passive or proactive and can be automated or manual. In either case, it should have a specific goal and be measurable, so that you can evaluate its effectiveness.
Eliminate Ineffective Learning
Ineffective learning doesn’t have to pull down the Return On Investment of your training programs. Predictive Learning Analytics can be a powerful and effective way to ensure that your learners successfully apply their training to their daily job tasks.
Find out more about Predictive Learning Analytics by watching our on-demand webinar on Predictive Learning Analytics.
References:
[1] The Predictive Learning Analytics Revolution
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