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dc.contributor.authorJames, Katherine
dc.contributor.authorGoldman, Susan R.
dc.contributor.authorKo, Mon-Lin Monica
dc.contributor.authorGreenleaf, Cynthia
dc.contributor.authorBrown, Willard
dc.identifier.citationJames, K., Goldman, S. R., Ko, M. M., Greenleaf, C., & Brown, W. (2014). Multiple-Text Processing in Text-Based Scientific Inquiry. In Joseph L. Polman, Eleni A. Kyza, D. Kevin O'Neill, Iris Tabak, William R. Penuel, A. Susan Jurow, Kevin O'Connor, Tiffany Lee, and Laura D'Amico (Eds.). Learning and Becoming in Practice: The International Conference of the Learning Sciences (ICLS) 2014. Volume 3. Colorado, CO: International Society of the Learning Sciences, pp. 1571-1572.en_US
dc.description.abstractThis study examined multiple--text processing in the context of text--based scientific inquiry for purposes of generating causal models. We used a series of rubrics to examine students' use of the texts, the quality of the causal models they created, and the impact of text use on model quality. Results indicate that multiple text users engaged with the texts in qualitatively different ways and created significantly higher quality models than single text users. Objectives This study examined how students used an intentionally designed text set to generate causal models of the carbon cycle prior to and following an instructional intervention. Using a series of rubrics, we analyzed the pre/post models to determine the extent to which students made use of the texts and the quality of the models that they created. We then examined the relationship between text use and model quality, focusing on the role of multiple-text processes.en_US
dc.publisherBoulder, CO: International Society of the Learning Sciencesen_US
dc.titleMultiple-Text Processing in Text-Based Scientific Inquiryen_US
dc.typeBook chapteren_US
Appears in Collections:ICLS2014

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