The shift towards active pedagogies in higher education that emphasize students' engagement in their own learning in and outside of the classroom has increased the ubiquity of online learning and assessment platforms for engaging students in such learning. Online learning requires self-regulated learning, which is a cyclical and temporal process in which students plan, monitor, and control their cognition, motivation, behavior, and affect in pursuit of their learning goals. Help-seeking is a particularly important regulation strategy when learning online, but few researchers have examined the cyclical and temporal nature of help-seeking processes when students learn in an online learning and assessment platform. We conducted a micro-level analysis of the temporal help-seeking behaviors of 488 undergraduates in an online learning and assessment platform to explore how they sought help during learning and identify those who struggled in such a context. This exploratory study includes two levels of analysis, frequency analysis and process mining, to triangulate patterns of help-seeking transitions observed in an online learning and assessment platform and relate those to learning. Results indicated (1) less successful learners demonstrated an increase in incorrect submissions and transitions to and from incorrect submissions, and (2) lower performers used more maladaptive help-seeking strategies during independent learning before classes (e.g., via repetitive use of solutions). The findings demonstrate the benefits of applying multiple learning analytics methods to inform robust interpretations of micro-level self-regulated learning and suggest that such modeling can help prepare interventions that support undergraduate students' effective use of help-seeking when learning online.