Khosravi, HassanDemartini, GianlucaLahza, Hatim2024-12-042024https://hdl.handle.net/20.500.14154/74010The adoption of educational technologies in modern educational systems has significantly advanced the field of learning sciences. This shift, particularly evident within digital learning environments, has enriched pedagogical strategies and redefined educational evaluation methodologies by leveraging sophisticated developments in learning analytics. Despite these advancements, a notable gap persists in effectively applying learning theories within digital environments and in the design of learning analytics. This shortfall partly stems from the ongoing development of empirical evidence supporting these theories and a prevalent reliance on software engineering and data science perspectives, which may not fully integrate learning theory insights. To this end, this thesis addresses this gap by proposing two triadic relationships among learning theories, educational technologies, and learning analytics. The overarching aim is to leverage these relationships to enhance learning understanding and learning optimisation. First, I leverage the dyadic relationship between learning theory and educational technologies to influence the development of the third actor of the triad, learning analytics, to enhance learning understanding. I demonstrate the application of this relationship through two approaches using two authentic educational platforms with real-life course data. The first approach, LA-exam, uses an e-exam platform and Self-Regulated Learning (SRL) theory to develop analytics for e-exams on two levels: student level and item level. The second approach, LA-sourcing, uses a learnersourcing platform and SRL theory to develop analytics about student tactics and strategies when engaging with the platform activities. Second, I leverage the dyadic relationship between learning theory and learning analytics to inform the design choices of the third actor of the triad, educational technologies, to enhance learning optimisation. I demonstrate the application of this relationship through two approaches that report the results of randomised controlled experiments conducted on a learnersourcing platform. The first approach, ET-create, uses a set of learning analytics and SRL theory to inform the design choices of SRL scaffolding strategies for content creation. The second approach, ET- review, uses a set of learning analytics and SRL and scripting theories to inform the design choices of scaffolding strategies for peer review.196enlearning analyticsAI in educationscripting theoryself-regulated learningassessmente-examlearnersourcingpeer feedbackmachine learninglearning scaffoldsThesis