Robotic

Personalized learning experiences are pivotal in enhancing student engagement and academic success. This thesis investigates the development of dynamic learner profiles within Artemis, an open-source Learning Management System (LMS). By leveraging data analytics and machine learning techniques, we aim to generate comprehensive learner profiles that capture individual learning behaviors, preferences, and progress. These profiles will enable tailored educational experiences, providing insights for both educators and learners. The study covers the methodology for data collection and analysis, the design and implementation of the profiling system, and the impact of personalized learning on student outcomes. This research offers valuable contributions to the field of adaptive learning technologies and the advancement of personalized education.

Abstract TBA

Competency-based education gives students transparency and flexibility in their learning process. The usage can enhance motivation, especially in university settings where instructors address students with diverse backgrounds and prior knowledge together. Therefore, Artemis contains the adaptive learning system Atlas to support university teaching. To provide the best experience possible to students, it is essential to understand how students currently interact with the system integrated into Artemis and its impacts on their learning process.