<p>The recent surge in the use of learning analytics in education has led to the development of more adaptive and personalized learning environments (APLE). A key feature of APLE is its capability to support learning tailored to various student needs and goals. Although educational studies emphasize goal setting as essential for effective student learning and self-regulation, current empirical research on APLE lacks clarity on how different learning activities (such as text reading and interacting with various task types) vary among students with different learning goals and outcomes, as well as what the specific thresholds and values for these activities are. To address this gap and support further research in APLE, this study aimed to examine how students with different learning outcomes (mastering, passing, and non-passing the course) differ in their learning behaviors and cognitive engagement with course materials, as indicated by their digital trace data obtained from APLE. Conducted within a formal asynchronous distance higher education program, the study grouped students based on their final exam scores and analyzed their digital traces. The findings highlight which aspects of digital trace data correlate effectively with student performance and identify parameters of various indicators that can be useful for guiding students’ behaviors towards desired academic goals. Additionally, the study offers valuable insights by challenging conventional assumptions about the uniform efficacy of different learning tasks (quiz tasks, self-assessment tasks and expert corrected tasks) in assessing student learning progress and outcome. It prompts a discussion about the role of student self-assessment, suggesting that while it is crucial for the self-regulation and learning process, it may not be the best indicator for students’ goal attainment.</p>