An Architecture for Workplace Learning Analytics (WLA) to Support Lifelong Learning in Sustainable Smart Organisations

Alyssa Whale and Brenda Scholtz ()
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Alyssa Whale: Department of Computing Sciences, Nelson Mandela University, Gqeberha 6001, South Africa
Brenda Scholtz: Department of Computing Sciences, Nelson Mandela University, Gqeberha 6001, South Africa

Sustainability, 2024, vol. 16, issue 9, 1-19

Abstract: An environment that supports lifelong learning contributes to the sustainability of the organisations in a Smart City, their stakeholders and ultimately, the city itself. Workplace Learning Analytics (WLA) can provide an organisation’s employees with the opportunity for lifelong learning in the workplace to enhance their skills and knowledge in their current and/or future roles. It uses the data generated by Learning Experience Platforms (LXPs) and other learning platforms to provide support for data-driven decision-making to gain a competitive edge. One of the components of successful and sustainable WLA is a layered architecture. The purpose of this paper is to present the design of a WLA architecture that can be used in organisations to impact successful WLA. This architecture was developed from the results of evaluating four potentially relevant architectures for WLA. The evaluation consisted of three phases. In the first phase, the architectures were evaluated using the DeLone and McLean Model of Information Systems Success. In the second phase, a real-world case of an organisation that provides lifelong learning opportunities to its stakeholders was used to validate the findings of the first phase. The proposed Layered Architecture for WLA was further validated in the third phase where a focus group discussion was held with participants from the real-world context. The architecture can provide valuable guidance to both practitioners and researchers to improve the success of WLA.

Keywords: workplace learning analytics; WLA; lifelong learning; WLA architecture; sustainability; smart city organisations (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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