The research, led by Abdo Hasan AL-Qadri of Xi’an Eurasia University together with colleagues at institutions in Saudi Arabia and Algeria, set out to build and test a structural model of student engagement in university-level online EFL learning. Rather than asking a single question about whether students like online classes, the team mapped the relationships among three psychological constructs: self-regulation, which encompasses the ability to plan, monitor, and evaluate one’s own learning; online learning pathways, which describe how students participate across different components of online instruction; and student engagement itself, the behavioral and emotional investment learners make in their studies. The goal was to determine not just whether these factors correlate, but how they connect in a coherent causal architecture.

To do this, the researchers collected data from 625 undergraduate EFL students enrolled at three public universities in Algeria. The choice of setting is significant. Algerian higher education operates under substantial institutional and infrastructural constraints, and the country’s universities were pushed rapidly into digital delivery during the COVID-19 pandemic, a transition that previous scholarship has described as emergency remote teaching rather than carefully designed online learning. Studying engagement in such a context provides a demanding test of the model: if self-regulation predicts engagement where connectivity is patchy and digital pedagogy is still maturing, the relationship is unlikely to be an artifact of privileged, well-resourced learning environments.

Methodologically, the study followed a rigorous two-stage analytical sequence. First, the team administered validated questionnaires measuring self-regulation, online learning pathways, and student engagement, then subjected the responses to exploratory and confirmatory factor analyses. Exploratory factor analysis allows the underlying structure of a questionnaire to emerge from the data itself, revealing which items cluster together as dimensions of a single construct. Confirmatory factor analysis then tests whether that structure holds when imposed on the data as a fixed hypothesis, a crucial safeguard against mistaking statistical noise for genuine psychological dimensions. Only after the measurement structure was established did the researchers proceed to structural equation modelling, the technique that allows simultaneous testing of direct and indirect pathways among multiple constructs.

The results paint a clear picture. Self-regulation was positively associated with student engagement through both direct and indirect routes. In other words, students who are better at setting goals, tracking their progress, and reflecting on their strategies invest more effort and attention in their online English courses, even before any other factor is considered. But the indirect pathway is where the model becomes genuinely interesting. Self-regulation was also strongly related to online learning pathways, the patterns of participation students adopt across the various components of their digital courses, and those pathways were in turn positively associated with engagement outcomes. Statistical testing confirmed a significant indirect association: self-regulated learners navigate online learning more effectively, and that effective navigation feeds engagement.

Think of it as a chain of psychological transmission. A student who plans a study schedule, monitors comprehension while watching a recorded lecture, and evaluates performance after an online quiz is not merely accumulating good habits in isolation. That self-regulatory machinery shapes how the student engages with the architecture of online learning itself, which platforms to use, which activities to prioritize, how to distribute effort across asynchronous and interactive components. The model suggests that this structured participation is a conduit through which self-regulation translates into sustained engagement. Students who lack that machinery, by implication, may flounder not because they cannot learn English online, but because they never establish the participation patterns that make online learning cohere.

The explanatory power of the model adds weight to these conclusions. The structural equation model accounted for a substantial proportion of the variance in both online learning pathways and student engagement, indicating that the constructs the researchers chose capture much of what actually matters in these settings. In educational research, where dozens of competing variables, from family background to institutional quality, can dilute any single model’s reach, explaining a large share of engagement through self-regulation and participation pathways is a notable result. It suggests that in constrained online EFL environments, the learner’s internal regulatory capacity and the participation patterns it produces are among the dominant determinants of engagement.

These findings align with a growing body of international evidence. Systematic reviews of self-regulated learning strategies in blended and online higher education have repeatedly found that students who deploy planning, monitoring, and evaluation strategies achieve better outcomes and persist longer. Research on massive open online courses has shown that self-regulation predicts retention, and studies of EFL learners in contexts from Iran to China have linked self-regulatory skill to motivation, engagement with feedback, and language performance. What the Algerian study adds is an integrated structural model that positions online learning pathways as a mediating mechanism, giving researchers and instructors a more precise account of how self-regulation exerts its influence rather than simply documenting that it does.

The practical implications are considerable. If self-regulation drives engagement partly through the pathways students take through online learning, then interventions should target both. Universities operating under infrastructural constraints, and indeed any institution delivering online instruction, could incorporate explicit training in self-regulatory strategies, goal setting, self-monitoring, and reflective evaluation, into language curricula rather than assuming students arrive with these skills. Course designers, meanwhile, could structure online platforms so that productive participation pathways are visible and easy to follow, reducing the navigational burden on students whose regulatory capacities are still developing. The study’s authors suggest that learners’ capacity to plan, monitor, and evaluate their learning is closely linked to engagement in online EFL contexts, particularly participation across different components of online learning, a formulation that points directly at course architecture as a lever for improvement.

There are also broader lessons for the post-pandemic university. The mass shift to online instruction did not merely relocate classrooms; it redistributed cognitive labor from instructors to learners. Students in Algiers, like students everywhere, were suddenly responsible for managing their own academic activity in environments that offered fewer external supports. This study provides empirical evidence that in such systems, the psychological resources students bring to that task matter enormously, and that the way those resources shape digital participation is measurable, modelable, and potentially trainable. As online and blended learning becomes a permanent fixture of higher education rather than an emergency measure, understanding the mechanics of engagement, who stays invested, who drifts away, and why, may prove to be one of the most consequential questions educational science can answer. This research offers a rigorous first draft of that answer, drawn from a setting where the stakes for getting it right are especially high.

Subject of Research: A structural equation model of self-regulation, online learning pathways, and student engagement in online EFL learning among Algerian university students

Article Title: A structural model of student engagement in online EFL learning: self-regulation and online learning pathways

Article References: AL-Qadri, A. H., Al-khresheh, M. H., Abdelhadi, A., Saraa, N., Zhang, J., Alruwaili, S. F., & Almayez, M. (2026). A structural model of student engagement in online EFL learning: self-regulation and online learning pathways. SN Social Sciences, 6(10), Article 529. https://doi.org/10.1007/s43545-026-01794-x

Image Credits: AI Generated

DOI: 10.1007/s43545-026-01794-x

Keywords: self-regulation, student engagement, online learning, EFL, higher education, structural equation modelling, Algeria, online learning pathways, language learning, educational psychology, structural, model