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Introducing Gig Translator: Unveiling the hidden skills of gig workers

Could this app make the hidden skills of gig workers more visible?

This is the third post in a Skills Validation Network blog series. Read the first and second.

By Dr. Tara Laughlin, Director of Skills Validation + Development at Education Design Lab

 

Meet DaVante Williams, an Uber driver whose commitment to his teenage passenger’s safety during a snowstorm catapulted him into the spotlight. When I-95 traffic was brought to a standstill, he realized they weren’t going to make it to her destination that night. He spoke to his passenger’s parents, paid for her hotel room, and offered to come back the next morning to finish the trip. (See the full story here). DaVante’s act of kindness didn’t go unnoticed; it went viral online, leading to an unexpected offer: a full-time customer experience job with Alto.

 

DaVante clearly possesses durable skills — such as empathy, initiative, and creative problem solving — in abundance. His story sheds light on a broader challenge within the “gig economy.” While workers like DaVante possess valuable skills, especially durable skills, these talents often remain hidden. Not everyone can rely on viral fame to unveil their hidden potential and access better paying jobs.

Since summer 2023, the Skills Validation Network (SVN) has been tackling this issue head-on. Our mission? To develop tools that uncover and validate the often overlooked durable skills of STARs (individuals Skilled Through Alternative Routes, other than a bachelor’s degree). Our efforts have centered around three key methods:

  • Experience Translation
  • Self-Assertion
  • Skills Demonstration

Among these, the Experience Translation work group is focused on solving this problem for STARs working in the gig economy. We asked ourselves: How might crowdsourcing and the gig economy provide real-world evidence, or data, to inform durable skills validation?

 

 

Might the data in DaVante’s Uber profile have indicated his strong empathy, initiative, and creative problem solving skills?

Many STARs have gig economy experience, accumulating valuable performance data from clients. However, they struggle to leverage this data effectively to identify and showcase their skills to potential employers.

Various platforms facilitate gig work across diverse domains, each collecting real-time performance data from clients. Rather than coming from a single source, this data is crowdsourced, allowing trends to emerge, and increasing its reliability.

But why is this necessary? Consider the statistics: McKinsey reports that 36% of Americans engage in contract, freelance, or temporary work, and Pew Research finds that 16% earn money specifically through a “gig platform.” Research shows that workers who are under 30, Hispanic, and with lower incomes, are most represented in the gig economy. Moreover, a majority of gig platform workers rely on this income for basic needs.

Notably, McKinsey’s survey reveals that nearly two-thirds of primary gig workers would prefer more permanent employment.

Given these realities, the SVN’s Experience Translation group has created a low-fidelity prototype of a tool – Gig Translator – to help gig workers translate their experience into validated, durable skills.

With user permission, Gig Translator would analyze gig workers’ performance data (e.g., number of gigs, star ratings, client reviews) to identify durable skills. STARs like DaVante can then automatically earn credentials showcasing their skills, enabling them to confidently present their abilities to potential employers.

Let’s walk through how Gig Translator would work in practice.

Though theoretical, DaVante’s experience with Gig Translator highlights the untapped potential of gig workers and the innovative solutions emerging to support them. As we continue refining this prototype and exploring new avenues for skill validation, we envision a future where every STAR has the tools they need to thrive in their career.

The Experience Translation group’s next steps involve user research to understand both employer and STAR perspectives on the Gig Translator prototype. Additionally, we’re exploring AI integration to map gig workers’ performance data to the Lab’s durable skills competency framework to inform the award of the credentials.

A massive thank-you to the members of the Experience Translation working group, whose efforts have helped bring this concept to life!

Stay tuned for Part 4 in this blog series, where we’ll share progress of the SVN work group focused on skills demonstration.

We would love to hear from you!

Are you exploring skills validation? Do you have questions, comments, or ideas? Reach out to us at xcredit@eddesignlab.org.