STEM students in LMS-supported courses often face self-regulation challenges and avoid seeking help online despite high motivation. Existing learning analytics (LA) interventions frequently fail to address these issues, as they are not grounded in students’ behavioral and psychological barriers. This paper presents a sequential mixed-methods study (N=106, n=14) investigating self-regulation, motivation, and support behavior alongside academic stress areas (self-study, exam preparation, group work, instructor interaction) across four STEM courses. Findings reveal a disconnect between strategic knowledge and implementation, driven by procrastination and fear of appearing incompetent in public forums. Based on these results, we derive six design principles for LA-based digital nudging interventions, such as structured re-entry points after inactivity and just-in-time reminders keyed to temporal risk. Each principle is aligned with specific LMS log data and behavioral mechanisms. The resulting framework translates empirically identified student challenges into log-data-based design requirements for scalable and ethically grounded nudging interventions in LMS.