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.

This thesis designs, implements and evaluates a board game using Ozobots to address gender stereotypes in STEM education among elementary students. It reviews gender-sensitive strategies, assesses robotics tools for inclusivity, and develops an engaging board game to promote equal participation and interest in STEM fields for both boys and girls.

Pupils playtest two board games that are identical in structure but differ in theme. Each version addresses a distinct gender stereotype. The process records engagement, collaboration, and individual perceptions through structured surveys and classroom observations. The collected data will provide insights into pupils’ confidence and participation levels, guiding refinements to enhance inclusivity.