Self-Regulated Learning Strategies as Predictors of Perceived Learning Gains among Undergraduate Students in Ethiopian Universities.

Autor: Tadesse, Tefera, Asmamaw, Aemero, Getachew, Kinde, Ferede, Bekalu, Melese, Wudu, Siebeck, Matthias, Fischer, Martin R.
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Zdroj: Education Sciences; Jul2022, Vol. 12 Issue 7, pN.PAG-N.PAG, 17p
Abstrakt: Despite increasing focus on the importance of self–regulated learning for undergraduate students in universities in recent years, very little is known about its specific features in universities in developing countries, in general, and Ethiopia, in particular. This study examined the relationships of self-regulated learning strategies (SRLSs) with perceived learning and further assessed the relationships within the SRLS components in Ethiopian public universities. For this, the authors adopted Pintrich's self-regulation theory as a guiding framework and used structural equation modeling (SEM) analysis. The sample used in the analysis pooled survey data from three randomly selected public universities and included volunteer undergraduate students having a major in Business and Economics and Engineering and Technology fields (n = 1142; male = 700 and female = 442), with mean age = 21.98 and SD = 2.50. The results indicated that the student SRLS and perceived learning gains scores were average values in terms of the magnitude of those measured variables. A two–step hierarchical regression analysis showed that the five components of SRLS that emerged from SEM analysis significantly predicted students' perceived learning over and above the control variables (ΔR2 ≥ 0.38 and 39%) for the total samples. Moreover, the regression results showed that greater predictions were observed for the help–seeking component (0.35 ≤ β ≥ 0.47) than others, significantly positively predicting the perceived learning for the total samples. Overall, the findings of this study indicate that the SRLSs are relevant mechanisms to aid student success in higher education. The implications of the study are highlighted. [ABSTRACT FROM AUTHOR]
Databáze: Complementary Index