Effects of perceived instructional support and self-regulated learning support on students' learning behavior in an online learning environment

Effects of perceived instructional support and self-regulated learning support on students' learning behavior in an online learning environment

Článek WoS

Learning Analytics (LA) has advanced significantly in recent years; however, its findings often suffer from limited generalizability and transferability due to reliance on data from a small number of courses. Course design variability is a critical factor influencing students' learning behavior in online learning environments (OLEs). This study examines how differences in instructional design, specifically in self-regulated learning (SRL) support, predict the intensity and regularity of students' learning behavior in OLEs. Using qualitative content analysis, we developed the SRL-S coding scheme to systematically assess the extent to which course design supports specific SRL processes. The coding scheme was validated by three independent researchers and applied to 76 courses. Multilevel modeling analysis confirmed substantial variability in student learning behavior across courses. Higher SRL support was associated with more frequent course visits (beta = .46, p < .001), greater regularity of visits (beta = -.31, p < .001), and increased total time spent in a course (beta = .45, p < .001). In contrast, perceived instructional support from the teacher significantly influenced total time spent (beta = .12, p = .004) but not the visit frequency (p = .324) or regularity (p = .951). Moreover, SRL support significantly predicted perceived instructional support (beta = .17, p = .002). Mediation analysis revealed a small but significant indirect effect of SRL support on total time spent (beta = .021, p = .012), while no mediation was found for visit frequency (p = .360) or regularity (p = .836). These findings highlight the pivotal role of structured SRL support in shaping student engagement. Our approach contextualizes LA indicators by accounting for course design differences and suggests that the SRL-S coding scheme could serve as a research-based guideline for enhancing SRL support in online courses.

Learning Analytics (LA) has advanced significantly in recent years; however, its findings often suffer from limited generalizability and transferability due to reliance on data from a small number of courses. Course design variability is a critical factor influencing students' learning behavior in online learning environments (OLEs). This study examines how differences in instructional design, specifically in self-regulated learning (SRL) support, predict the intensity and regularity of students' learning behavior in OLEs. Using qualitative content analysis, we developed the SRL-S coding scheme to systematically assess the extent to which course design supports specific SRL processes. The coding scheme was validated by three independent researchers and applied to 76 courses. Multilevel modeling analysis confirmed substantial variability in student learning behavior across courses. Higher SRL support was associated with more frequent course visits (beta = .46, p < .001), greater regularity of visits (beta = -.31, p < .001), and increased total time spent in a course (beta = .45, p < .001). In contrast, perceived instructional support from the teacher significantly influenced total time spent (beta = .12, p = .004) but not the visit frequency (p = .324) or regularity (p = .951). Moreover, SRL support significantly predicted perceived instructional support (beta = .17, p = .002). Mediation analysis revealed a small but significant indirect effect of SRL support on total time spent (beta = .021, p = .012), while no mediation was found for visit frequency (p = .360) or regularity (p = .836). These findings highlight the pivotal role of structured SRL support in shaping student engagement. Our approach contextualizes LA indicators by accounting for course design differences and suggests that the SRL-S coding scheme could serve as a research-based guideline for enhancing SRL support in online courses.

Learning Analytics (LA), Self-Regulated Learning (SRL), instructional design, online learning environment (OLE), SRL-S coding scheme, course design variability, student learning behavior, multilevel modeling, frequency and regularity of visits, perception of teacher support, mediation analysis, student engagement, generalizability of results, and qualitative content analysis

Learning Analytics (LA), Self-Regulated Learning (SRL), instructional design, online learning environment (OLE), SRL-S coding scheme, course design variability, student learning behavior, multilevel modeling, frequency and regularity of visits, perception of teacher support, mediation analysis, student engagement, generalizability of results, and qualitative content analysis

JUŘÍK, V.; JUHANAK, L.; BRUCKNEROVA, K.; JARINA, J.; ZOUNEK, J.

2026

17.09.2025

PLoS One

20

9

Spojené státy americké

19

https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0332296