Open Learner Models for Self-Regulated Learning: Exploring the Effects of Social Comparison and Granularit

Open Learner Models (OLM) show the learner the internal model that the computer-based adaptive or tutoring system maintains. In the context of Self-Regulated Learning, where the learner is able to make decisions about what to learn and how to learn, OLM bring a wide variety of supporting features, ranging from metacognitive support, to navigational support, to engagement with the learning content. In prior work using OLM which featured social comparison features (OSLM), I have discovered interesting effects from these systems, regarding engagement with the system, encompassing considerable variations across different studies.;My thesis deepens the understanding of OLM and OSLM by a series of studies in which I evaluate different versions of Mastery Grids, incorporating features that were designed to match different motivational profiles, which are grounded in theories of Self-Regulated Learning and Learning Motivation. A large classroom study with more than 300 active students was conducted to deepen the exploration of the social comparison features in terms of engagement and navigation within the system. The results of this study confirmed the positive effects of the social comparison features and also brought insights into why certain students are influenced, based on their motivational orientations and prior-knowledge. A second large classroom study expanded the exploration by deploying the Rich-OLM, an extension of Mastery Grids featuring coarse- and fine-grained information about the learner model, which was designed to help students navigate the content contained in the system. Results showed that students exposed to the fine-grained components took comparatively less time navigating the interface with higher rates of attempting content that they had opened. Results also raised concerns about increasing the complexity of the interface by integrating fine-grained visualization and social comparison features.;My work contributes to the understanding of the effects of Open Learner Models and additional features that provide social comparison and detailed information. It also contributes bringing learning motivation aspects into the understanding of Open Learner Models. Learning motivation in the context of self-regulated learning, provides a valuable theoretical basis to study how different students react and use learning tools.