ABSTRACT
This article proposes a framework for capability development of adult educators (AEs) in Singapore. Globalisation, demographic changes and digital innovation, accelerated by the Covid- 19 pandemic, have accentuated the importance of adult education and changing role of AEs. The immediate implications of these effects on the professionalism of AEs and the significance of their development trajectories are acute. With an increased focus on the need to ensure AEs are current in both their pedagogic and domain competences, we introduce discussions on the need to develop a conceptual framework to generate a deeper understanding of their knowledge, skills, attitudes, beliefs and practices of AEs. This framework can be used by practitioners as a tool to facilitate professional reflection and development, and by policy makers to support continued improvements to the quality of adult education. Our overall aim is to promote the importance of varied and continued opportunities for the professional development of AEs at policy, organisational and individual levels.
ABSTRACT
Recent reports indicate increased organizational appetite and spend in the energy industry in both the areas of operational risk management training and enablement and in extended reality hardware and software, as part of larger automation and digital transformation initiatives. Furthermore, recent advances in immersive technology, along with more dispersed, asynchronous working conditions due to COVID, have resulted in scalable, immersive simulations that more and more closely resemble real world environments. While recent standards have defined JSON syntax appropriate for tracking and measuring human behavior data in generic learning environments (IEEE P9274.1) and in a manner that more closely approximates human behavior in the workplace, as typically tracked in operational risk management systems, no risk-based ontology has yet been defined that more closely crosswalks and correlates data from simulated environment systems to those in operational environments. Thus, the true efficacy of extended reality-based risk mitigation training cannot be fully measured. In this effort, a risk-based ontology and matrix was constructed in accordance with the xAPI standard syntax and allowable extensions and was utilized to transform a subset of historical data from simulated operational risk-based scenarios from the energy industry. Transformed data from this initial subset closely approximated operational risk reporting data and provided insights into human behavior data in simulated environments that can be easily compared and correlated to existing operational excellence and risk mitigation KPIs. Implications for mapping of additional advanced data from simulated environments in larger, more complex datasets, such as eye tracking and biometrics, were also considered and explored. © 2022 IEEE.