2026/04/06 by Mihwa Lee, Björn Rudzewitz, Luise von Keyserlingk +6
Arts and Humanities · Social Sciences · Psychology · #EFL/ESL Teaching and Learning #Educational Tools and Methods #Second Language Acquisition and Learning
paper · doi:10.1016/j.lindif.2026.102914
Understanding how students engage in learning is crucial for identifying individual learning paths and delivering personalized support in formative assessment practices. We analyzed trace data from an intelligent computer-assisted language learning (ICALL) system to examine English as a Foreign Language (EFL) learners’ behavioral engagement in extracurricular assignments ( N = 201). Results from a structural equation model (SEM) indicated that behavioral engagement indicators related to time management, time investment, help-seeking, and learning reflection predicted learning outcomes, while task attempts were negatively associated with outcomes. Motivation showed a small positive effect on outcomes via time spent on reflection on assignments after the deadline. Latent profile analysis (LPA) identified four profiles that reflected the SEM findings: “early engagers” and “deep engagers” demonstrated better outcomes than “disengagers” and “minimal engagers”. The findings offer theoretical insights into the role of engagement in learning and provide pedagogical foundations for enhancing formative assessment practices.