Data mining and analytics in the context of workplace learning

New chapter published providing an overview on data mining and analytics in the context of workplace learning, while critically reflecting the related benefits and affordances for organisations and its stakeholders.

Ifenthaler, D. (2022). Data mining and analytics in the context of workplace learning: benefits and affordances. In M. Goller, E. Kyndt, S. Paloniemi, & C. Damsa (Eds.), Methods for researching professional learning and development. Challenges, applications, and empirical illustrations (pp. 313–327). Springer. https://doi.org/10.1007/978-3-031-08518-5_14 

Technology-based innovations in workplace learning have significantly altered both the scale and resolution of measurements for supporting learning processes in organisations. The increased availability of vast and highly varied amounts of data from settings in the context of workplace learning is overwhelming. This chapter outlines standards in data mining with a specific focus on data from workplace learning. Further, different data mining and analytics methodologies, such as Support Vector Machines or Decision Trees, are presented. An emphasis is shifted to the understanding of learning analytics which are a socio-technical data-mining and analytic practice in educational contexts. The chapter closes with an outlook on how data mining and analytics may provide benefits for future workplace learning scenarios.

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