Open AccessTime Perspectives as The Predictors of Online Self-Regulated Learning
Author(s) -
Andrea Barta,
Borbála Tamás,
Bernadette Gálfi,
István Szamosközi
Publication year - 2021
Publication title -
journal of e-learning research
Language(s) - English
Resource type - Journals
ISSN - 2669-235X
DOI - 10.33422/jelr.v1i2.73
Subject(s) - psychology , self regulated learning , multilevel model , variance (accounting) , regression analysis , computer assisted web interviewing , goal orientation , perspective (graphical) , educational attainment , developmental psychology , social psychology , applied psychology , computer science , statistics , mathematics , accounting , machine learning , artificial intelligence , economics , business , economic growth
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 Babes-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.