Semantic Web-Enabled Interventions to Support Workplace Learning

To keep pace with today’s rapidly growing knowledge-driven society, continuous learning in workplaces and being able to self-regulate one’s learning processes have become essential. In this dissertation, I propose a set of interventions, developed using Semantic-Web technologies, to scaffold self-regulated learning (SRL) processes in workplaces. I integrate social embeddedness elements with harmonization components in the functionalities provided by these interventions to accentuate the social and contextual dimensions of workplace learning. To measure users’ SRL processes, I developed a trace-based protocol which captures users’ low-level trace data on the fly and translates them into higher level SRL events, contingencies and graphs of users’ learning actions. Findings of this research suggest that elements from both social and organizational aspects of a workplace should be integrated into the design and development of interventions which aim to support users’ SRL processes in that environment. Users’ perceived usefulness of the interventions show that they do consider the social context of their organization when planning their learning goals; yet, they prefer to know clearly what competences their organization expects them to achieve. Analysis of users’ trace data, on the other hand, indicates a relative balance between users’ reliance on both social and organizational contexts. The Social Wave intervention, which brought users updates from their social context, was the most central one during their learning actions, also the strongest determinant of users’ engagement in SRL processes. The next most central intervention included the one that informed users about how various learning resources were used by their colleagues, along with the two interventions providing users with the organizational context of their workplace. This theoretically-grounded understanding can guide researchers in intervention planning and development for workplace settings. Also, the trace-based methodology developed within this work takes promising steps toward adopting new methodological approaches in investigating SRL, and offers new ways to achieve insight into factors that promote knowledge workers’ use of self-regulatory processes. Future research can gain substantially by applying social analytics on users’ trace data collected using trace methodologies, merged with other quantitative and qualitative means for gathering data about users’ SRL beliefs and processes.