Using big data to chart workplace learning during COVID-19

新冠肺炎疫情引发了一场史无前例的全球混乱，造成了生命和就业的损失。雇佣一线工人的公司必须在健康和安全与保持生产率之间取得平衡，才能在不可预测的经济环境中生存和蓬勃发展。新员工、重新部署员工和老员工需要快速学习，这让情况变得复杂起来。职场学习如何受到新冠肺炎疫情的影响？随着疫情的继续发展，如何对其进行优化？为了回答这些问题，我们将把我们在记忆认知神经科学方面的专业知识与大数据的数学建模结合起来，以表征不同学习策略在吸收新冠肺炎健康安全信息以及公司特定信息方面的作用，以帮助公司在疫情期间保持运营弹性。我们将扩大与Axonify和加拿大公共卫生局的现有合作伙伴关系，Axonify是150多个国家和地区在线培训一线工人(如杂货商、出租车司机)的领先者，加拿大公共卫生局负责指导国家新冠肺炎的健康和经济应对措施。 为了应对新冠肺炎危机，Axonify每天开发一份关于大流行安全、健康和在家工作的最佳实践的培训材料。通过Axonify，我们可以访问280多万员工在疫情期间推出的与新冠肺炎相关的学习数据(例如社交距离)和与新冠肺炎无关的学习数据(例如新产品)，以及在新冠肺炎之前收集的学习数据，这些数据将随着经济的重新开放而收集。我们将根据雇主特征，如公司类型和地理位置，以及员工特征，如聘用日期，对学习策略和培训内容进行建模和统计分析。通过这样做，我们将能够确定学习策略和内容的理想组合，以最大限度地提高员工的学习和留住能力。PHAC将结合我们的发现和新冠肺炎趋势分析数据，为政府战略提供信息，以提高公众对现有保护措施的依从性，并采取新的措施，如佩戴口罩。

The COVID-19 pandemic has led to an unprecedented global disruption, costing lives and jobs. 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, and the Public Health Agency of Canada (PHAC), which is responsible for guiding the national COVID-19 health and economic response. In response to the COVID-19 crisis, Axonify developed daily, bite-sized training material on best practices in pandemic safety, wellness, and working from home. Through Axonify, we have access to learning data in over 2.8 million employees related to COVID-19 (e.g., social distancing) and unrelated to COVID-19 (e.g., new products) rolled out during the pandemic, along with learning data collected prior to COVID-19 and that will be collected as the economy reopens. We will conduct modeling and statistical analyses of learning strategy and training content in the context of employer characteristics, such as company type and geographical location, and employee characteristics, such as date of hire. In doing so, we will be able to determine the ideal combination of learning strategies and content to maximize employee learning and retention. PHAC will use our findings together with COVID-19 trend analysis data to inform government strategies to increase public adherence to existing protective measures and adoption of new measures, such as mask-wearing.