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How undergraduate students learn: uncovering interrelationships between factors that support self-regulated learning and strategy use.

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Title: How undergraduate students learn: uncovering interrelationships between factors that support self-regulated learning and strategy use.
Authors: Hey, Rebecca1 (AUTHOR), McDaniel, Mark2 (AUTHOR), Hodis, Flaviu A.1 (AUTHOR) flaviu.hodis@vuw.ac.nz
Source: Metacognition & Learning. Dec2024, Vol. 19 Issue 3, p743-772. 30p.
Subject Terms: *Self-regulated learning, *Courseware, *Student engagement, *Academic achievement, *Undergraduates
Abstract: Being an effective learner is an important pillar supporting success in higher education and beyond. This research aimed to uncover the extent to which undergraduate students use a set of commonly researched learning strategies, as well as to understand how learning strategy usage relates to key self-regulation factors proposed in influential models of self-regulated learning. Undergraduate students from New Zealand (N = 140) were recruited through course management systems, social media, and campus posters. Data were analysed using correlation and multiple regression. Results show that students reported using more learning strategies that are relatively effective than learning strategies that are somewhat less effective. In addition, effort regulation and student engagement were the most consistent predictors of both learning strategy use and self-reported academic achievement. Building on these findings, this research provides important new insights into the associations between learning strategy usage and pivotal factors that support effective self-regulated learning and academic achievement. As we highlight in the article, these insights have key implications for advancing theory and research on self-regulated learning. [ABSTRACT FROM AUTHOR]
Copyright of Metacognition & Learning is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: <searchLink fieldCode="JN" term="%22Metacognition+%26+Learning%22">Metacognition & Learning</searchLink>. Dec2024, Vol. 19 Issue 3, p743-772. 30p.
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  Data: Being an effective learner is an important pillar supporting success in higher education and beyond. This research aimed to uncover the extent to which undergraduate students use a set of commonly researched learning strategies, as well as to understand how learning strategy usage relates to key self-regulation factors proposed in influential models of self-regulated learning. Undergraduate students from New Zealand (N = 140) were recruited through course management systems, social media, and campus posters. Data were analysed using correlation and multiple regression. Results show that students reported using more learning strategies that are relatively effective than learning strategies that are somewhat less effective. In addition, effort regulation and student engagement were the most consistent predictors of both learning strategy use and self-reported academic achievement. Building on these findings, this research provides important new insights into the associations between learning strategy usage and pivotal factors that support effective self-regulated learning and academic achievement. As we highlight in the article, these insights have key implications for advancing theory and research on self-regulated learning. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Metacognition & Learning is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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              Text: Dec2024
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