A sketched OODA loop depicting the Observe, Orient, Decide and Act phases.

What is Precision Learning?

In 2015, Gus Evrad at University of Michigan asked “In a 2013 Nature Medicine article, Alla Katsnelson noted a shift in the lexicon of modern clinical medicine; the framework once known as personalized medicine had morphed into precision medicine. Might educators soon be following suit?” Indeed, some educators have followed suit. I define precision learning as an instructional methodology for continuous curriculum-based assessment towards observable outcomes with measurable goals. The “precision” makes it a little buzwordy, however I posit it helps situate what it is in the context of its origins from the precision medicine space. This is not a new buzzword for the learning industry, so much as a contextualization of decades of applied research and development.

Some folks, especially folks who do high stakes compliance training, may be familiar with an approach called stealth assessment; that is one approach to precision learning. OODA and Double-Loop Learning are yet another approaches. These again are not so much teaching methods as they describe patterns of refining learning and doing towards intended outcomes over time with repeated engagement; the idea being learners learn and change behaviors through repeated iterations and progress.

How Does it Work?

Precision learning involves establishing and continuously referencing norms while also systematically evaluating a range of instructional tactics and content that might be employed to attain a desired outcome, let alone a desired set of outcomes. There are a few guiding principles that inform whether a precision learning approach is achievable:

  • Focus/center curricular activities on the learner;
  • Target directly observable behavior(s);
  • Evaluate behaviors by their frequency;
  • Describe the evaluation of learning patterns with a rubric;
  • Analyze environmental conditions systematically that appear to influence behavior.

Seems weird to talk about personalizing the learner experience by first considering learners in the abstract, but from a distance a given learner can certainly be described in a normalized way by a modest set of attributes. Many stakeholders in the learning process share considerable interest an enthusiasm for the capability to compare and contrast learners with different attributes, how these learners engage in curricular and co-curricular activities and task-performance that’s related in work contexts.

Precision infers there is little uncertainty. With precision learning approaches, we want to depend on large populations of the learner audience to enable views of learner attribute composition, facilitator/instructor evaluations, and historical grade/score or even observable skill attainment distributions.

For the products I work with at Elsevier, we use multiple forms of learner engagement to interrogate the working experience of newly graduated nurses. The problems *I* am particularly interested in solving through learning aren’t just “learning” problems but business problems that can be measured and impacted by learning activity, specifically. One use case is encouraging the confidence and feelings of support for new nurses, so a product (Transition to Practice) is designed to do that by

  • Scenario-based learning to reinforce the learner making the correct choices in simulated nursing activities, no matter how many incorrect choices are made in lieu of the correct choice;
  • Shift surveys that evaluate the nurse’s feelings of confidence and support;
  • Journaling that encourages dialogue between a nurse and their preceptors and nursing managers.

We developed a small algorithm, improved from early testing and feedback, that triggers an alert if there are large shifts in how nurses respond to their shift surveys over time, fed also by sentiment analysis of journal entries. Armed with reporting on how people are feeling and triangulated with some evaluation of how nurses demonstrate their command of professional skills in simulated conditions serves as an early warning system for customers of who might need coaching or other supports to build the grit it takes to stay in nursing.

Rather than thinking of precision learning as something that’s fully automated, I think it is compelling to develop smarter tools for people leaders, managers and facilitators to personally engage learners; encouraging learners through the process to take the lead in their own learning. In the case of the above, some clients have shared their employee retention data, monthly, with me — not because I asked or because there was a vendor/customer agreement (there wasn’t for this) but because THEY NOTICED improvements of 7% and 10% nursing (employee) retention cohort-over-cohort in their own data analysis and wanted to share the data in hopes we can make further improvements as we think of them.

Rather than think about personalizing the instruction, or the content, for a given learner, we focus on personalizing the intervention that leads to better self-learning, encouraging resiliency to take on tougher cognitive challenges. By keeping the learner in the center, and making all the learner data available to stakeholders so they are able to determine specific interventions for the learner, is a direct an apt analogue for precision medicine