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ABSTRACT

Online learning strategies refer to the methods and approaches students use to organize study activities, manage course content, and regulate participation in virtual learning environments. These strategies are relevant to educational technology because they provide measurable indicators that can inform instructional design, learning analytics, adaptive learning systems, and student support in online and blended courses. This study examined the psychometric properties of the online learning strategies scale (OLSS) using a psychometric network analysis approach in Peruvian university students. The sample included 520 university students (389 women = 74.8%; 131 men = 25.2%) aged 18 to 40 years. Descriptive analyses were conducted to evaluate item distributions. The psychometric network structure of the OLSS was then estimated using exploratory graph analysis with bootstrap procedures, followed by assessment of community stability and structural consistency. The results identified a four-community structure corresponding to motivation, self-control, Internet literacy, and Internet anxiety. The network showed high structural stability, with bootstrap replication values close to 1.00 for most items and high structural consistency across communities. These findings support the internal structure and reliability of the OLSS in the studied population. From an educational technology perspective, the OLSS may help instructors and instructional designers identify students requiring motivational, self-regulatory, digital literacy, or affective support in technology-enhanced learning environments.

Keywords: online learning, online learning strategies, educational technology, network analysis, psychometrics, university students