Understanding Data Driven Decision Making Practices in Learning Factory Environments
DOI:
https://doi.org/10.33050/itee.v4i2.1096Keywords:
Data Driven, Learning Factory, Learning Analytics, Organizational Culture, Training EffectivenessAbstract
The increasing integration of data science and learning analytics in learning factory environments has created new opportunities to enhance training effectiveness and align educational processes with real industrial needs, yet understanding how decision making practices are enacted in these contexts remains limited. This study aims to explore how stakeholders interpret and utilize data to support instructional, operational, and strategic decisions that influence skill development and adaptive training in learning factories. A qualitative research design was employed through multiple case studies involving semi structured interviews, direct observations, and analysis of institutional documents to capture in depth insights into practices, challenges, and contextual dynamics surrounding data driven decision making. The findings indicate that successful implementation is shaped by factors such as organizational culture, data literacy levels, leadership support, and the availability of integrated information systems, while common challenges include fragmented data sources, limited analytical competencies, and resistance to data informed change; participants reported that collaborative reflection and continuous feedback loops significantly improved training relevance and learner engagement. The study concludes that strengthening governance structures, investing in capacity building, and promoting a culture that values evidence based decision making can enhance both learning outcomes and operational performance in learning factory settings, providing meaningful implications for educators, industry partners, and policymakers seeking to advance sustainable and technology enhanced workforce development.
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Copyright (c) 2026 John Edwards, Reem Ahli, Mohd Faiz Hilmi
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