Using Big Data to Chart Workplace Learning during COVID-19

In light of disruptions caused by COVID-19, companies employing frontline workers must balance health and safety with maintaining productivity to survive and thrive in an unpredictable economic climate. This picture is complicated by the need for rapid learning in new, redeployed, and longstanding employees. How is workplace learning impacted by the COVID-19 pandemic and how can it be optimized as the pandemic continues to evolve? To answer these questions, we will combine our expertise in the cognitive neuroscience of memory with mathematical modeling of big data to characterize the role of different learning strategies on uptake of COVID-19 health and safety information as well as company-specific information to help companies maintain operational resilience during the pandemic. We will amplify existing partnerships with Axonify, a leader in online training of frontline workers (e.g., grocers, taxi drivers) in over 150 countries, to determine the ideal combination of learning strategies and content to maximize employee learning and retention.

Intern: 
Julia G. Halilova
Faculty Supervisor: 
Shayna Rosenbaum
Province: 
Ontario
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