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Building mental models from multiple texts: How readers construct coherence from inconsistent sources
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In: ISSN: 1749-818X ; EISSN: 1749-818X ; Language and Linguistics Compass ; https://hal.archives-ouvertes.fr/hal-03433500 ; Language and Linguistics Compass, Wiley, 2021, 15 (3), ⟨10.1111/lnc3.12409⟩ (2021)
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Supporting Productive Sense-Making in Text-Based Explanatory Modeling
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Abstract:
The current study examined text-based inquiry for purposes of constructing an explanatory model of a biological phenomenon. Ninth-grade students were randomly assigned to one of two conditions of support: graphic organizer present or absent. While reading multiple texts and constructing models, students engaged in think-aloud protocols that were analyzed to shed light on their processing. The hypothesis that performance on both model construction and a learning application task would be higher for students with the graphic organizer was confirmed. Confirming the second hypothesis, analyses of students’ processing indicated that students with the graphic organizer engaged in more elaborative processing of relevant information from the texts than those without the graphic organizer. In the absence of the graphic organizer, processing was dominated by paraphrasing/summarizing of non-model relevant information from the texts. Confirming the third hypothesis, the effects of the graphic organizer on model construction, with respect to the number of elements included, and learning were mediated by elaborative processing of relevant information. These findings indicate that the graphic organizer encouraged deeper forms of processing, implying that it is not the scaffold itself, but rather the productive forms of processing that it encourages that leads to better model construction and learning. This study advances our understanding of the mechanisms that underlie the effects of scaffolds, like the graphic organizer, on successful text-based explanatory modeling. The findings imply that the utility of this and other scaffolds can be evaluated based on the extent to which they support students in identifying and deeply processing model relevant information.
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Keyword:
Science Literacy; Text-Based Explanatory Modeling
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URL: http://hdl.handle.net/10027/22709
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