Behavioral trace data in an online learning environment as indicators of learning engagement in university students - Winter, Marc; Mordel, Julia; Mendzheritskaya, Julia; Biedermann, Daniel; Ciordas-Hertel, George-Petru; Hahnel, Carolin; Bengs, Daniel; Wolter, Ilka; Goldhammer, Frank; Drachsler, Hendrik; Artelt, Cordula; Horz, Holger (Frontiers in Psychology, 2024)
Learning in asynchronous online settings (AOS) is challenging for university students. However, the construct of learning engagement (LE), which is positively linked to online learner motivation, represents a possible lever to identify and reduce challenges within AOS. The idea study addresses the questions of whether LE can be modeled with the sub-dimensions of effort, attention and content interest, and by which behaviors these facets of LE are represented. Learning Analytics provides a fruitful framework to analyze students' learning processes and LE via trace data. This study examines the association between students’ learning behavior (trace data) and self-reported LE. Participants were 764 university students joining attending an AOS. Results show that a model combining multiple indicators can account for a proportion of the variance in students’ LE (highly significant R² between .04 and .13). The identified set of indicators is stable over time supporting the transferability to similar learning contexts. The results of this study can contribute to both research on learning processes in AOS in higher education and the application of learning analytics in university teaching (e.g., modeling automated feedback).