A Fine-Grained Open Learner Model for an Introductory Programming Course

Guiding students to the learning activities that are most appropriate for their current level of knowledge is one of the goals that adaptive educational systems tried to achieve during the last decades. Recently, several attempts have been made to use Open Learner Models (OLM) as a tool for achieving this goal. While the original goal of OLM is to help students reflect about their own learning process, extending OLM with navigation support functionality enables students to take immediate actions towards improving their knowledge. In this work, we attempted to extend the navigation support functionality of OLM by developing a fine-grained OLM that offers student knowledge visualization on both topic and concept levels. The fine-grained OLM enables students to directly explore connections between their knowledge and available learning activities, making an informed decision about their next learning steps. To assess the impact of the new type of OLM, we evaluated several versions of it in a classroom study, while also comparing it with data from our earlier studies that featured a coarse-grained OLM. Our results suggest that the fine-grained OLM considerably impacts student choice of learning activities, making student learning more efficient. We also found that the specific design features of fine-grained OLM could affect students' confidence and persistence while selecting and attempting the learning activities.