Sparks – formerly known as ‘Reach’ – is a tool that uses learning data to automate adaptive messaging, feedback and support. Its main aim is to enhance the learning experience through the delivery of recommended actions, support and content at the right time.

I’m a huge fan of prototyping, the feedback you gain early on really helps shape your product. True to form, Sparks has gone through a few prototyping phases, each one valuable in shaping what the product has become today. It’s also had a few names including Quire, Reach and now Sparks, but the central principle has remained constant: finding ways to help people as they learn and develop.

How Does Sparks Work?

Sparks consumes data from connected applications and utilizes that data in two ways.

Firstly, it can be used to trigger events. When incoming data meets given criteria this triggers a sequence of steps.

Secondly, it uses data to generate user personas. These personas then control the type of messaging /  content that should be sent to people and when. It’s important to note that personas are dynamic and constantly changing based on data being analyzed, ensuring nudge remain contextually relevant.

Nudge Theory

Sparks is based around the principle of Nudge Theory; the notion that small, meaningful, easy interventions can help learners outcomes. Delivering advice when it is most meaningful and making resources easy to access can result in more people taking action which, in turn, can enhance their learning experience.

xAPI and Learning Locker®

If you’re interested in xAPI and using Learning Locker, Sparks is an official Learning Locker app making it easy to get started triggering events based on data within xAPI statements.

If you like to learn more about Sparks or wish to try it out for yourself don’t hesitate to get in touch!