Book Review: Composition and Big Data by Amanda Licastro and Benjamin Miller

Abstract

Composition and Big Data, edited by Amanda Licastro and Benjamin Miller, offers the discipline a collection of 16 chapters that explain and discuss big data methods in composition studies. The editors use the book to advocate for “work that combines qualitative and quantitative methods, recognizing that data doesn’t speak for itself, but must be spoken into and from, based on deep disciplinary knowledge” (p. 8). They attempt to not only broaden but deepen disciplinary methodological knowledge and provide carefully situated critiques of big data methods. This approach allows Licastro and Miller to emphasize exciting technological tools and how to use those tools while they productively problematize what these tools do. The book is divided into four sections: “Data in Students’ Hands,” “Data Across Contexts,” “Data and the Discipline,” and “Dealing With Data’s Complications.” Although each chapter follows the theme in its section, the book’s pagination does not indicate the separation between sections, which can make thematic similarities harder to track. Section 1, “Data in Students’ Hands,” is the smallest, containing only three chapters. In Chapter 1, Trevor Hoag and Nicole Emmelhainz describe teaching undergraduate students to use “machinic collaboration” (p. 25) to assist in metacognitive textual analysis. They showcase an assignment asking students to use distant reading to make new interpretive connections. In Chapter 2, Chris Holcomb and Duncan A. Buell write about creating a first-year composition (FYC) corpus and what such a corpus reveals about the complexity of student writing. And in Chapter 3, Alexis Teagarden Book Review