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Challenging new views on familiar plotlines: A discussion of the use of XML in the development of a scholarly tool for literary pedagogy.

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Title: Challenging new views on familiar plotlines: A discussion of the use of XML in the development of a scholarly tool for literary pedagogy.
Authors: Brown, Monica1, Dobson, Teresa2, Grue, Dustin1, Ruecker, Stan3
Source: Literary & Linguistic Computing. Jun2013, Vol. 28 Issue 2, p199-208. 10p. 2 Color Photographs, 1 Diagram.
Subjects: XML (Extensible Markup Language), Text Encoding Initiative (Document type definition), Tags (Metadata), Education equipment, Visualization
Abstract: This article describes PlotVisML, a simple, flexible XML schema for encoding literary narratives that was developed by an interdisciplinary team of researchers in literary studies, interface design, computing studies, and education as part of a research project on reading, writing, and teaching complex literary narrative. PlotVisML is a simple, adaptable schema consisting of five key elements: , , and (tags for marking up narrative events), and and (tags for encoding narrative objects). Fictional narratives that have been marked up using PlotVisML can be visualized in PlotVis, a digital scholarly tool that allows users to model and interact with literary narratives in three dimensions. Both PlotVis, an interactive visualization tool, and PlotVisML, our custom XML schema for encoding literary narratives, were designed to permit challenging new views on familiar plotlines and, more importantly, to depart from conventional ways of modeling narrative in literary instruction. In discussing the process of developing PlotVisML, we contribute to the ongoing discussion of text encoding as a form of close reading (e.g., Liepert, 2009). [ABSTRACT FROM AUTHOR]
Copyright of Literary & Linguistic Computing is the property of Oxford University Press / USA 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.)
Database: Engineering Source
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  Data: Challenging new views on familiar plotlines: A discussion of the use of XML in the development of a scholarly tool for literary pedagogy.
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  Data: <searchLink fieldCode="JN" term="%22Literary+%26+Linguistic+Computing%22">Literary & Linguistic Computing</searchLink>. Jun2013, Vol. 28 Issue 2, p199-208. 10p. 2 Color Photographs, 1 Diagram.
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  Data: This article describes PlotVisML, a simple, flexible XML schema for encoding literary narratives that was developed by an interdisciplinary team of researchers in literary studies, interface design, computing studies, and education as part of a research project on reading, writing, and teaching complex literary narrative. PlotVisML is a simple, adaptable schema consisting of five key elements: <action>, <dialogue>, and <narration> (tags for marking up narrative events), and <character> and <object> (tags for encoding narrative objects). Fictional narratives that have been marked up using PlotVisML can be visualized in PlotVis, a digital scholarly tool that allows users to model and interact with literary narratives in three dimensions. Both PlotVis, an interactive visualization tool, and PlotVisML, our custom XML schema for encoding literary narratives, were designed to permit challenging new views on familiar plotlines and, more importantly, to depart from conventional ways of modeling narrative in literary instruction. In discussing the process of developing PlotVisML, we contribute to the ongoing discussion of text encoding as a form of close reading (e.g., Liepert, 2009). [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Literary & Linguistic Computing is the property of Oxford University Press / USA 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: Jun2013
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