Akbar Momeni rad; Maryam Pourjamshidi; Javad Afshar
Abstract
The purpose of this research was to perform a meta-analysis on the studies conducted in the field of the effect of reverse learning on the engagement and academic motivation of learners. Among the conducted researches, 16 studies were included in the analysis. The studies that were used in this research ...
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The purpose of this research was to perform a meta-analysis on the studies conducted in the field of the effect of reverse learning on the engagement and academic motivation of learners. Among the conducted researches, 16 studies were included in the analysis. The studies that were used in this research were obtained through the website of the Iran Scientific Documentation Center (Thesis Center of Iran), as well as through the websites of Academic Jihad, Mag Iran, Absco, Science Direct, and Sage. , G-Stor, Emerald, Springer and Wiley-Blackwell were collected. After checking the inclusion and exclusion criteria of the studies, the data were analyzed using Meta-analysis software. The findings showed that reverse learning has a significant effect on students' academic motivation (effect size 0.157). Also, reverse learning has a significant effect on the engagement of learners (effect size 0.123). In general, it can be said that reverse learning leads to an increase in students' academic engagement and motivation.
Hossein Zangeneh; Maryam Pourjamshidi; Elahe Velayati; Ebrahim Abolghasemi
Abstract
This qualitative content analysis study, with a deductive approach, aimed at developing a practical framework of content characteristics of e-learning, based on the cognitive load theory. In that, following fundamental questions were first phrased about the characteristics of electronic content: A) “What ...
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This qualitative content analysis study, with a deductive approach, aimed at developing a practical framework of content characteristics of e-learning, based on the cognitive load theory. In that, following fundamental questions were first phrased about the characteristics of electronic content: A) “What are the characteristics of electronic content from intrinsic cognitive load perspective?” B) “What are the characteristics of electronic content from extrinsic cognitive load perspective?,” and C) “What are the characteristics of electronic content from optimal cognitive load perspective?.” Then, the theoretical bases of the cognitive load theory were used to specify the major and minor components of above questions through qualitative content analysis. Finally, practical solutions for the management of cognitive load in design and production of electronic content within a certain framework were developed, based on the findings of this stage and through discussions with specialists. In this study, the statistical population included all articles available for download in Google and Wiley, Ebsco, and Sciencedirect databases. The total number of articles found in the mentioned databases by 2015 was 77 articles, out of which 11 articles were selected for analysis through purposive sampling technique. Finally, the analysis of articles resulted in determination of the characteristics of e-learning content from three perspectives, namely intrinsic cognitive load, extrinsic cognitive load, and optimal cognitive load. The current framework of cognitive load in the analysis of electronic content can help instructional designers in designing better e-learning environments.
