Assessment of Inference Types (AIT): A New Test for Measuring How Well Students Make Inferences.

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Title: Assessment of Inference Types (AIT): A New Test for Measuring How Well Students Make Inferences.
Authors: Morrison, Timothy G. (AUTHOR), Wilcox, Brad (AUTHOR), Sudweeks, Richard R. (AUTHOR), Bird, Lauren (AUTHOR), Murdoch, Erica (AUTHOR), Bursey, Hannah (AUTHOR), Helvey, McKenzie (AUTHOR)
Source: Reading Psychology. Jul/Aug2022, Vol. 43 Issue 5/6, p293-316. 24p.
Subjects: Inference (Logic), Common Core State Standards, Children's drawings, Literacy programs
Abstract: The authors of the Common Core State Standards and publishers of literacy programs focus on an essential aspect of comprehension, the process of drawing inferences. An inference refers to any piece of information that an author does not include in text but expects readers to use to make meaning. Four common inference types are anaphoric, background knowledge, predictive, and retrospective. To date, there have been group-administered test for teachers to use to evaluate children's abilities to draw inferences, but none focuses on these inference types. The purpose of this study was to create a reliable instrument, and the Assessment of Inference Types (AIT) was the result. It includes four passages, two narrative and two informational. Each passage has eight items, two of each inference type. Unlike most assessments, it measures students' abilities to make inferences moment-by-moment during the act of reading, instead of before or after. The AIT was administered to a 492 fifth graders and was shown to have a.902 reliability coefficient. Armed with results of this test, teachers can identify what specific inferencing processes children have mastered and ones with which they are struggling, and plan instruction accordingly. [ABSTRACT FROM AUTHOR]
Copyright of Reading Psychology is the property of Taylor & Francis Ltd 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: Assessment of Inference Types (AIT): A New Test for Measuring How Well Students Make Inferences.
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  Data: The authors of the Common Core State Standards and publishers of literacy programs focus on an essential aspect of comprehension, the process of drawing inferences. An inference refers to any piece of information that an author does not include in text but expects readers to use to make meaning. Four common inference types are anaphoric, background knowledge, predictive, and retrospective. To date, there have been group-administered test for teachers to use to evaluate children's abilities to draw inferences, but none focuses on these inference types. The purpose of this study was to create a reliable instrument, and the Assessment of Inference Types (AIT) was the result. It includes four passages, two narrative and two informational. Each passage has eight items, two of each inference type. Unlike most assessments, it measures students' abilities to make inferences moment-by-moment during the act of reading, instead of before or after. The AIT was administered to a 492 fifth graders and was shown to have a.902 reliability coefficient. Armed with results of this test, teachers can identify what specific inferencing processes children have mastered and ones with which they are struggling, and plan instruction accordingly. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Reading Psychology is the property of Taylor & Francis Ltd 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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        Value: 10.1080/02702711.2022.2094042
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      – Code: eng
        Text: English
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      – SubjectFull: Children's drawings
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              Text: Jul/Aug2022
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