{"title":"The association between online self-regulated learning and E-learning acceptance among medical sciences students during the COVID-19 pandemic","authors":"M. Kamali, M. Bagheri-Nesami","doi":"10.4103/jnms.jnms_97_22","DOIUrl":null,"url":null,"abstract":"Context: Self-regulated learning is a process by which learners choose goals for themselves and then try to regulate, control and manage their cognition, motivation, and behavior. The COVID-19 pandemic faced students to numerous educational challenges. Rapid transition of the traditional classroom to the virtual environment affected E-learning acceptance of the students in the age of the COVID-19 pandemic. Aim: The present study aimed to determine the relationship between online self-regulated learning and E-learning acceptance among Mazandaran University of medical sciences during the COVID-19 pandemic. Settings and Design: This descriptive-analytical study was conducted on 234 Mazandaran University of medical sciences students. Materials and Methods: The nonprobability quota sampling method was used for data collection. Inclusion criterion was experience E-learning at least one semester in the age of COVID-19 pandemic. Internship medical sciences students were excluded. The online questionnaire consisted of three parts: Sociodemographic questionnaire, online self-regulated learning and E-learning acceptance. Statistical Analysis Used: Descriptive statistics, one-way ANOVA, Pearson test, and univariate and multivariate linear regression model were utilized. Results: According to the univariate linear regression model, E-learning acceptance explored 19.8% variance of the online self-regulated learning. The multivariate linear regression showed age, gender, marital status, medical students, another job and E-learning acceptance explored 47.1% variance of the online self-regulated learning. Conclusion: The results showed that E-learning acceptance was correlated with online self-regulated learning. The faculty members and university managers can use strategies to enhance the E-learning acceptance to improve online self-regulated learning and facilitate barriers in the age of mandatory online learning.","PeriodicalId":42130,"journal":{"name":"Journal of Nursing and Midwifery Sciences","volume":"9 1","pages":"219 - 223"},"PeriodicalIF":0.5000,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Nursing and Midwifery Sciences","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4103/jnms.jnms_97_22","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"NURSING","Score":null,"Total":0}
引用次数: 5
Abstract
Context: Self-regulated learning is a process by which learners choose goals for themselves and then try to regulate, control and manage their cognition, motivation, and behavior. The COVID-19 pandemic faced students to numerous educational challenges. Rapid transition of the traditional classroom to the virtual environment affected E-learning acceptance of the students in the age of the COVID-19 pandemic. Aim: The present study aimed to determine the relationship between online self-regulated learning and E-learning acceptance among Mazandaran University of medical sciences during the COVID-19 pandemic. Settings and Design: This descriptive-analytical study was conducted on 234 Mazandaran University of medical sciences students. Materials and Methods: The nonprobability quota sampling method was used for data collection. Inclusion criterion was experience E-learning at least one semester in the age of COVID-19 pandemic. Internship medical sciences students were excluded. The online questionnaire consisted of three parts: Sociodemographic questionnaire, online self-regulated learning and E-learning acceptance. Statistical Analysis Used: Descriptive statistics, one-way ANOVA, Pearson test, and univariate and multivariate linear regression model were utilized. Results: According to the univariate linear regression model, E-learning acceptance explored 19.8% variance of the online self-regulated learning. The multivariate linear regression showed age, gender, marital status, medical students, another job and E-learning acceptance explored 47.1% variance of the online self-regulated learning. Conclusion: The results showed that E-learning acceptance was correlated with online self-regulated learning. The faculty members and university managers can use strategies to enhance the E-learning acceptance to improve online self-regulated learning and facilitate barriers in the age of mandatory online learning.