PhD defence L.Y.J. (Jacqueline ) Wong

The flexibility of open online education, such as Massive Open Online Courses (MOOCs), has offered greater learning opportunities for many. However, very few learners succeed in completing MOOCs. Research suggests that an important factor that may prevent students from completing MOOCs is a lack of self-regulatory learning skills. Self-regulated learning (SRL) is a process in which learners steer their motivation, cognition, metacognition, and behavior towards their goals. The aim of the dissertation is twofold: 1) to examine how SRL can be facilitated in MOOCs and 2) to explore approaches in learning analytics to examine SRL. The first part of the dissertation describes a systematic review and two empirical studies that examined ways to support SRL in MOOCs. The empirical studies investigated the effectiveness of prompting SRL and supporting goal setting and planning across multiple MOOCs. The findings suggest that the characteristics of the MOOC might influence the effectiveness of SRL supports. The second part of the dissertation describes a literature review and an empirical study exploring learning analytics approaches to examine SRL from trace data. The findings point to the potential of learning analytics to provide a deeper understanding of how SRL unfolds over time. The dissertation provides insights into the design and implementation of SRL supports in MOOCs and highlights the challenges and future directions for supporting SRL in MOOCs and using learning analytics to measure and facilitate SRL.

The PhD defences will not take place publicly in the usual way. A live stream link has been provided to candidate. The ceremony will begin exactly at 13:00.