Klein, Sonja2025-08-202025-08-202025-08-20https://hdl.handle.net/2077/89391Purpose: This thesis, conducted with the i-MASTER project, explores how student-facing learning analytics dashboards (LADs) can be designed to support students’ sensemaking in relation to their learning, through contextualizing learning data. Specifically, the designs of student-facing LADs in a simulation-based maritime communication case were explored. Addressing a gap in pedagogically grounded learning analytics research, the study contributes to educational design-based learning analytics as well as maritime communication research. It further provides tangible prototype designs of contextualized LAD features. Theory: The theoretical foundation for this thesis builds on human-centered learning analytics, which emphasizes student agency and the importance of pedagogically informed design. To conceptualize these core commitments, Educational Data Storytelling is applied as a design and analytical framework to contextualize learning analytics through narrative elements. Method: A participatory design-based research design was employed. First, unstructured classroom observations and design workshops with maritime instructors were conducted to uncover key features of the simulation-based learning design. Based on these insights, a digital prototype of a multi-modal learning analytics dashboard, including contextualized elements, was developed. The prototype was evaluated through an expert review involving learning science and educational technology researchers. Results: The findings suggest that a narrative learning analytics dashboard design aligns well with the existing pedagogical structure of simulation-based maritime education and training, making educational data storytelling a promising approach for this field. Observed feedback practices emerged as a crucial source for contextualizing learning analytics, though integrating domain-specific regulations requires a more mindful approach. The expert evaluation highlighted the pedagogical value of self-assessment features, detailed explanations, and delayed data access, as these elements promote student reflection and promise to improve students’ sensemaking in relation to their learning.engMultimodal Learning Analytics, Human-centered Learning Analytics, Contextualized Learning Analytics, Educational Data Storytelling, Feedback, Maritime Education and Training, Explanatory Dashboards, Sensemaking, Maritime EnglishText