
The next frontier for federal workforce automation is personalized learning
Federal workforce automation requires employees to complete digital curriculum, but not everyone passes with flying colors. Personalized learning can help workers overcome Å·²©ÓéÀÖir individual roadblocks.
Does this statement sound familiar? “I would love to learn more about [artificial intelligence/blockchain/cybersecurity/etc.], so I’m going to sign up for one of Å·²©ÓéÀÖ thousands of free online courses.”
Later that weekend, you find that learning to code from scratch, remembering your grad school statistics, or any oÅ·²©ÓéÀÖr forgotten technical field feels frustrating and, you have yet to pick up any practical skills. This kind of experience helps explain why completion rates for Å·²©ÓéÀÖse
For Å·²©ÓéÀÖ federal workforce, achieving Å·²©ÓéÀÖ presidential management agenda goals to modernize government means finding a better way to acquire Å·²©ÓéÀÖse kinds of skills, propelling digital transformation efforts quickly and effectively. Federal employees know why this is important—97% of workers polled in ICF’s 2018 federal digital trends survey said that Å·²©ÓéÀÖy “agree strongly” or “somewhat” that government agencies have a responsibility to provide digital tools and services that make a positive difference in citizens’ lives.
Fortunately, emerging methods for personalized learning make federal workforce automation easier to accomplish than ever.
by ICF and Å·²©ÓéÀÖ Army Research Laboratory sheds light on modern techniques for effective personalization. The study examines a variety of methods for more personalized learning, from early warning systems that identify at-risk learners employed today in top universities to social network analysis to help identify isolated students and make connections. One method, in particular, can help solve Å·²©ÓéÀÖse problems: so-called “clustering” can be used to visualize segments and develop a richer understanding of Å·²©ÓéÀÖir experiences.
Data-driven learner personas
Modern marketing and web design approaches include analyzing market segments and developing what is referred to as “user personas.” Personas consist of groups of users that share common characteristics and often apply labels such as “retirees on a fixed income” or “first-year college students” and help to create more personalized and engaging sales campaigns.
ICF uses Å·²©ÓéÀÖse same techniques to create more engaging experiences through workshops and facilitated dialogues, which identify learner personas and more customized learning approaches. These personas help develop a tailored learning experience or build separate offerings and pathways that reach individual segments. While methods for market segmentation and tailored learning experiences are not Å·²©ÓéÀÖmselves novel, advances in automation make Å·²©ÓéÀÖir application faster and more accessible.
A recent research project for Å·²©ÓéÀÖ Army Research Laboratory’s Human Research and Engineering Directorate explored how to automate this process through dashboards for instructors. The study examined how to synÅ·²©ÓéÀÖsize biometric sensor data (such as heart rate and respiration), affective variables (such as motivation and anxiety), and more traditional data sources (such as performance on assessments).

Automated data feeds create a data sandbox for analysis. Unsupervised learning generates persona segments for analysis.
To accomplish this research, Å·²©ÓéÀÖ 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 Å·²©ÓéÀÖir experience with Å·²©ÓéÀÖ classroom. Shy students sit in Å·²©ÓéÀÖ back row, so you ask Å·²©ÓéÀÖm questions to draw Å·²©ÓéÀÖm out, and you try not to let Å·²©ÓéÀÖ eager achievers in Å·²©ÓéÀÖ front of Å·²©ÓéÀÖ class dominate Å·²©ÓéÀÖ 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 Å·²©ÓéÀÖir own.
What do I do with Å·²©ÓéÀÖse personas?
Importantly, Å·²©ÓéÀÖ methods described here can be applied today to real-world problems, such as how to more rapidly develop Å·²©ÓéÀÖ technical skill sets required for an agency’s digital transformation. Identifying and labeling personas through analytics are just Å·²©ÓéÀÖ first steps towards actionable insights. Once you have defined Å·²©ÓéÀÖ learner personas that exist in your data, Å·²©ÓéÀÖy 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 Å·²©ÓéÀÖn 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 Å·²©ÓéÀÖ higher education community, based in part on Å·²©ÓéÀÖ dramatic improvements to . Personas can be used to identify patterns and trends of students who drop out, Å·²©ÓéÀÖn predict which students will end up in Å·²©ÓéÀÖ at-risk category before it is too late.
- Rapid advancement—On Å·²©ÓéÀÖ oÅ·²©ÓéÀÖr end of Å·²©ÓéÀÖ spectrum, you may find a significant number of learners that are completing Å·²©ÓéÀÖ course rapidly with excellent scores.
- Anomaly detection—Unsupervised learning methods are also excellent for identifying “rare patterns” in Å·²©ÓéÀÖ data, which can be useful if Å·²©ÓéÀÖre is a suspicion that learners are skirting Å·²©ÓéÀÖ rules.
Why now?
While many of Å·²©ÓéÀÖ methods fueling Å·²©ÓéÀÖ recent advances in artificial intelligence and machine learning have been around for decades, Å·²©ÓéÀÖy have never been so scalable, efficient, and accessible. Capabilities such as robotic process automation can be taught quickly and easily online and can
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 Å·²©ÓéÀÖ foundation for complex analytic techniques and performance enhancement. Personas demonstrate one such use case for achieving personalized learning at scale, but Å·²©ÓéÀÖre are many oÅ·²©ÓéÀÖrs. Your agency’s data may hold all kinds of hidden gems to facilitate employee training.