In the evolving landscape of educational technology, Self-Regulated Learning (SRL) emerges as a pivotal factor for student success. This is especially important in Blended Learning (BL) environments, where students must regulate their learning in both online and traditional in-person activities. This thesis advances the understanding of self-regulated learning in BL settings through a holistic approach. It utilizes Learning Analytics (LA) techniques and, more especially, the latest advancements in the use of Sequence Analysis (SA) to analyze students' behavior captured through trace data and self-reported measures.

Two primary objectives are addressed: (1) to study how students' SRL manifests across time and how it is related to students’ internal conditions and performance, and (2) to evaluate the impact of LA interventions on how students’ learning strategies manifest over time. To address these objectives, this thesis follows a quantitative approach using observational data collected from three quasi-experiments conducted in three courses in Higher Education. All courses followed a BL design and were part of the same institution. This thesis combines the generalizability of variable-centered analytical methods with person-centered approaches for detailed subgroup insights in order to capture the full complexity of the three courses.

For the first objective, this thesis explores how students’ self-reported measures and prior achievements are related to their learning strategies and whether the collected trace data are a mediator between their internal conditions and their course performance. For the second objective, this thesis studies the causal effect of an intervention in the temporal dynamics of students' behavior captured through trace data.

The research uncovers nuanced insights into how SRL manifests across various BL courses, the predictive power of students’ strategies on academic performance, and the transformative impact of LA interventions on learning behaviors. The results underscore the importance of providing a nuanced understanding of SRL in BL environments and demonstrate the benefits of leveraging LA and internal conditions to understand students’ SRL.

This research makes four contributions by synthesizing findings from the three courses: (1) it proposes a holistic analytical framework for analyzing self-regulated learning in blended learning environments; (2) it provides insights into the effect of external conditions on students' behavior; (3) it offers insights into the mediating effect of behavioral trace data on the relationship between students' internal conditions and their academic performance; and (4) it evaluates the effect of learning analytics interventions on students' learning strategies.

Altogether, this thesis contributes with a holistic perspective of SRL in BL, providing more nuanced insights into student learning processes and strategies over time.