Federal workforce automation requires employees to complete digital curriculum, but not everyone passes with flying colors. Personalized learning can help workers overcome their individual roadblocks.
Automated data feeds create a data sandbox for analysis. Unsupervised learning generates persona segments for analysis.
To accomplish this research, the team experimented with several popular methods for what’s known as “unsupervised learning,” using automated processes to find patterns existing within a data set.
In smaller classroom settings with a handful of students, instructors can conduct this same kind of process intuitively based on their experience with the classroom. Shy students sit in the back row, so you ask them questions to draw them out, and you try not to let the eager achievers in the front of the class dominate the discussion. These analytic methods shine in complex environments with hundreds of variables and thousands of learners—patterns can be identified on a scale that humans cannot accomplish on their own.
What do I do with these personas?
Importantly, the methods described here can be applied today to real-world problems, such as how to more rapidly develop the technical skill sets required for an agency’s digital transformation. Identifying and labeling personas through analytics are just the first steps towards actionable insights. Once you have defined the learner personas that exist in your data, they can provide immediate value to a number of stakeholders.
Here are just a few example use cases for applying personas:
- Learner scaffolding—Detecting common characteristics of students who quit early, like those taking online data science programs, can reveal patterns and help predict who is going struggle. Intervention can then occur earlier with more targeted learning opportunities, such as remedial programming skills or even recommending a different course.
- At-risk students—The use of predictive analytics has started to take hold in the higher education community, based in part on the dramatic improvements to retention at Georgia State University. Personas can be used to identify patterns and trends of students who drop out, then predict which students will end up in the at-risk category before it is too late.
- Rapid advancement—On the other end of the spectrum, you may find a significant number of learners that are completing the course rapidly with excellent scores.
- Anomaly detection—Unsupervised learning methods are also excellent for identifying “rare patterns” in the data, which can be useful if there is a suspicion that learners are skirting the rules.
While many of the methods fueling the recent advances in artificial intelligence and machine learning have been around for decades, they have never been so scalable, efficient, and accessible. Capabilities such as robotic process automation can be taught quickly and easily online and can dramatically reduce the time needed to collect and process data.
These capabilities are being applied to all manner of government challenges, such as a recent case study on contract closeout demonstrated. Thus, federal workforce automation is within close reach.
Tools like learner personas provide immediate benefits and form the foundation for complex analytic techniques and performance enhancement. Personas demonstrate one such use case for achieving personalized learning at scale, but there are many others. Your agency’s data may hold all kinds of hidden gems to facilitate employee training.