AbstractBackgroundLearner profiles detected from digital trace data are typically triangulated with survey data to explain those profiles based on learners' internal conditions (e.g., motivation). However, survey data are often analysed with limited consideration of the interconnected nature of learners' internal conditions.ObjectivesAiming to enable a thorough understanding of trace‐based learner profiles, this paper presents and evaluates a comprehensive approach to analysis of learners' self‐reports, which extends conventional statistical methods with psychological networks analysis.MethodsThe study context is a massive open online course (MOOC) aimed at promoting physical activity (PA) for health. Learners' (N = 497) perceptions related to PA, as well as their self‐efficacy and intentions to increase the level of PA were collected before and after the MOOC, while their interactions with the course were logged as digital traces. Learner profiles derived from trace data were further examined and interpreted through a combined use of conventional statistical methods and psychological networks analysis.Results and ConclusionsThe inclusion of psychological networks in the analysis of learners' self‐reports collected before the start of the MOOC offers better understanding of trace‐based learner profiles, compared to the conventional statistical analysis only. Likewise, the combined use of conventional statistical methods and psychological networks in the analysis of learners' self‐reports before and after the MOOC provided more comprehensive insights about changes in the constructs measured in each learner profile.Major TakeawaysThe combined use of conventional statistical methods and psychological networks presented in this paper sets a path for a comprehensive analysis of survey data. The insights it offers complement the information about learner profiles derived from trace data, thus allowing for a more thorough understanding of learners' course engagement than any individual method or data source would allow.