Sixth graders' selection and integration when writing from multiple online texts.

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Title: Sixth graders' selection and integration when writing from multiple online texts.
Authors: Kullberg, Nina1 (AUTHOR), Kiili, Carita2 (AUTHOR) carita.kiili@tuni.fi, Bråten, Ivar3 (AUTHOR), González-Ibáñez, Roberto4 (AUTHOR), Leppänen, Paavo H. T.1 (AUTHOR)
Source: Instructional Science. Feb2023, Vol. 51 Issue 1, p39-64. 26p.
Subject Terms: *Reading comprehension, Multiple regression analysis, Electronic publications
Abstract: This study examined students' ability to select relevant ideas from multiple online texts and integrate those ideas in their written products. Students (N = 162) used a web-based platform to complete an online inquiry task in which they read three texts presenting different perspectives on computer gaming and wrote an article for a school magazine on the issue based on these texts. Students selected two snippets from each text during reading and wrote their article with the selected snippets available. The selected snippets were scored according to their relevance for completing the task, and the written products were scored according to their integration quality. The results showed that most students performed well on the selection task. However, nearly half of the written products were characterized by poor integration quality. The hierarchical multiple regression analysis showed that students' selection of relevant ideas from the texts contributed to their integration of information across texts over and above both reading fluency and reading comprehension skills. The study provides new evidence on the relationship between selection and integration when younger students work with multiple texts, and both theoretical and educational implications of these findings are discussed. [ABSTRACT FROM AUTHOR]
Copyright of Instructional Science 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: Sixth graders' selection and integration when writing from multiple online texts.
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  Data: This study examined students' ability to select relevant ideas from multiple online texts and integrate those ideas in their written products. Students (N = 162) used a web-based platform to complete an online inquiry task in which they read three texts presenting different perspectives on computer gaming and wrote an article for a school magazine on the issue based on these texts. Students selected two snippets from each text during reading and wrote their article with the selected snippets available. The selected snippets were scored according to their relevance for completing the task, and the written products were scored according to their integration quality. The results showed that most students performed well on the selection task. However, nearly half of the written products were characterized by poor integration quality. The hierarchical multiple regression analysis showed that students' selection of relevant ideas from the texts contributed to their integration of information across texts over and above both reading fluency and reading comprehension skills. The study provides new evidence on the relationship between selection and integration when younger students work with multiple texts, and both theoretical and educational implications of these findings are discussed. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Instructional Science 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: Feb2023
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