Time perspectives as the predictors of online self-regulated learning

Proceedings of ‏The 5th International Academic Conference on Humanities and Social Sciences

Year: 2021

DOI: https://www.doi.org/10.33422/5th.iachss.2021.06.378

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Time perspectives as the predictors of online self-regulated learning

Andrea Barta, Borbála Tamás, Bernadette Gálfi and István Szamosközi



Digital education considerably requires active participation of students in the learning process, the application of self-regulated learning activities for the attainment of successful learning results. The aim of the present study is the investigation of time perspectives as the predictors of online self-regulated learning. In our study 210 Transylvanian students participated, from the Babeș-Bolyai University, Faculty of Psychology and Educational Sciences. Students’ demographic characteristics were recorded, for the assessment of self-regulation the Self-regulated Online Learning Questionnaire – Revised was applied and time perspectives of students’ were measured by the Zimbardo Time Perspective Inventory. A correlational, cross-sectional design was used. On the basis of the results of hierarchical regression, in our first model demographic characteristics explained 5% of the variance for the application of self-regulation activities. In our second model, controlling demographic variables, time perspectives explained an additional 33% of the variance for self-regulation. Self-regulated learning strategies are predicted among demographic characteristics by students’ gender, age and online learning, while out of time perspectives only future orientation proved to be a significant predictor. Females, older students, participants attending online education and higher future orientation apply to a higher degree the self-regulated learning strategies, as males, younger students and participants with lower scores at future orientation.

Keywords: self-regulated strategies, metacognitive activities, time perspectives, future orientation, hierarchical regression.