Explaining trace‐based learner profiles with self‐reports: The added value of psychological networks.

Background: Learner 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. Objectives: Aiming 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. Methods: The 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 Conclusions: The 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 Takeaways: The 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. Lay Description: What is already known about this topic: Researchers have made extensive use of data from online learning platforms—often referred to as learning trace data—to understand how learners engage in online learning activities.Surveys are often used to gather information about learners' motivation, perceptions, and other internal factors, to complement the insight gleaned from learning traces and thus create a more complete picture of the learning behaviour.When analysing survey data, the focus is usually on individual factors without considering how different factors are connected and affect one another.Psychological networks analysis is a novel analytic approach to studying complex phenomena both in psychology and education. What this paper adds: The paper uses psychological networks as a method for examining mutual relations among learners' internal factors (e.g., motivation, perceptions) measured through self‐reports before and after a course.By combining conventional statistical analysis of self‐reports with psychological networks analysis, the paper develops a comprehensive picture of learners' internal factors measured through self‐reports, one that accounts for both individual and interconnected nature of those factors.This comprehensive approach to information extraction from surveys allows for better understanding of trace‐based learner profiles, that is, profiles mined from data about learners' interaction with online learning activities. Implications for practice and/or policy: The presented method paves a way for a comprehensive analysis of the data collected from learners through surveys.It helps us gain a better understanding of learners' engagement with online learning activities.It can advance the evaluation of digital educational programs that aim to encourage changes in health‐related behaviour. [ABSTRACT FROM AUTHOR]