Abstract
Select any industry and you will find scores of articles detailing how the SARS-CoV-2 pandemic fostered an explosion of online learning over the past two years. Within social distancing guidelines, organizations were able to meet many challenges of the learning and performance space. At the same time, training budgets have shrunk to offset pandemic headwinds as organizations rally to survive. Focus has shifted instead toward digital transformation of big data, to unify, manage, and visualize the flow of structured business data and, thus, improve overall efficiency and outcomes. While impressive innovations have emerged to focus learning and performance (L&P) on what is most pertinent, L&P content science lags woefully behind data science. Scalability of L&P content remains largely elusive. This paper outlines a vision for an L&P science to foster a cogent digital transformation on par with modern data science.
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Acknowledgments
I am deeply indebted to Diane (DiBernadino) Parker, Charlie Sullivan, and Amile Samarakoon, for paving the way toward an L&P ecosystem during my tenure at Travelport (https://www.travelport.com/). Without the lead staff members for each component of Travelport’s L&P ecosystem—especially R. Mark Moore, Lori Guillory, Jack Denion, Ines Hedenus, Sandra Yanez, and Laurenne McAteer—this vision of an L&P science would not have been possible. I would also like to thank TJ Bramblett, founder of Van Bram Marketing (vanbram.co), for the opportunity to gain insights around big data science.
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Dickelman, G.J. (2023). Learning and Performance Science for Digital Transformation. In: Guralnick, D., Auer, M.E., Poce, A. (eds) Innovative Approaches to Technology-Enhanced Learning for the Workplace and Higher Education. TLIC 2022. Lecture Notes in Networks and Systems, vol 581. Springer, Cham. https://doi.org/10.1007/978-3-031-21569-8_12
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