In a data-driven world, anything can be data. As the techniques and scale of data analysis advance, the need for a response from rhetoric and composition grows ever more pronounced. It is increasingly possible to examine thousands of documents and peer-review comments, labor-hours, and citation networks in composition courses and beyond. Composition and Big Data brings together a range of scholars, teachers, and administrators already working with big-data methods and datasets to kickstart a collective reckoning with the role that algorithmic and computational approaches can, or should, play in research and teaching in the field. Their work takes place in various contexts, including programmatic assessment, first-year pedagogy, stylistics, and learning transfer across the curriculum. From ethical reflections to database design, from corpus linguistics to quantitative autoethnography, these chapters implement and interpret the drive toward data in diverse ways.
| Acknowledgments |
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| Introduction: Reasons to Engage Composition through Big Data |
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3 | (19) |
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SECTION ONE DATA IN STUDENTS' HANDS |
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1 Learning to Read Again: Introducing Undergraduates to Critical Distant Reading, Machine Analysis, and Data in Humanities Writing |
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22 | (13) |
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2 A Corpus of First-Year Composition: Exploring Stylistic Complexity in Student Writing |
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35 | (17) |
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3 Expanding Our Repertoire: Corpus Analysis and the Moves of Synthesis |
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52 | (14) |
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SECTION TWO DATA ACROSS CONTEXTS |
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4 Localizing Big Data: Using Computational Methodologies to Support Programmatic Assessment |
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66 | (19) |
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5 Big Data as Mirror: Writing Analytics and Assessing Assignment Genres |
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85 | (16) |
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6 Peer Review in First-Year Composition and STEM Courses: A Large-Scale Corpus Analysis of Key Writing Terms |
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101 | (24) |
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7 Moving from Categories to Continuums: How Corpus Analysis Tools Reveal Disciplinary Tension in Context |
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125 | (13) |
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SECTION THREE DATA AND THE DISCIPLINE |
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8 From 1993 to 2017: Exploring "A Giant Cache of (Disciplinary) Lore" on WPA-L |
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138 | (21) |
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9 Composing the Archives with Big Data: A Case Study in Building a Collaboratively Authored Metadata Information Infrastructure |
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159 | (20) |
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10 Big-Time Disciplinarity: Measuring Professional Consequences in Candles and Clocks |
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179 | (28) |
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11 The Boutique Is Open: Data for Writing Studies |
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207 | (5) |
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SECTION FOUR DEALING WITH DATA'S COMPLICATIONS |
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12 Ethics, the IRBs, and Big Data Research: Toward Disciplinary Datasets in Composition |
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212 | (18) |
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13 Ethics in Big Data Composition Research: Cybersecurity and Algorithmic Accountability as Best Practices |
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230 | (15) |
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14 Data Do Not Speak for Themselves: Interpretation and Model Selection in Unsupervised Automated Text Analysis |
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245 | (17) |
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15 "Unsupervised Learning": Reflections on a First Foray into Data-Driven Argument |
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262 | (17) |
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16 Making Do: Working with Missing and Broken Data |
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279 | (12) |
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| Contributors |
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291 | (5) |
| Index |
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296 | |
Amanda Licastro is the emerging & digital literacy instructional designer at the University of Pennsylvania. Her research explores the intersection of technology and writing, including book history, dystopian literature, and digital humanities, with a focus on multimodal composition and extended reality. Benjamin Miller is assistant professor of composition in the English Department at the University of Pittsburgh, focusing on digital research and pedagogy. He is the author of the poetry collection Without Compass.
Lisainfo e-raamatute kohta