Chapter 4: Rethinking Validity and Reliability in the Age of Convergence
In “Chapter 4,” Penrod explains how networked writing redefines traditional psychometric writing terminology and assessment practices and instruments. To highlight principle arguments from Penrod’s chapter, I have presented them in a Question and Answer (Q&A) format below--a succinct, common rhetorical strategy for emphasizing key points in an online format.
Q: How does the notion of “convergence” impact traditional notions of psychometric assessment?
A: Penrod argues that convergence demands a reconceptualization of traditional psychometric writing terminology (e.g. reliability and validity). She explains that poststructuralism and postmodernism have brought about “social constructivism” and other qualitative research methods far removed from psychometric writing assessment models (p. 93). Consequently, she asserts that networked writing favors qualitative approaches to teaching writing.
Q: More specifically, what qualitative pedagogical approaches and instruments can compositionists use in assessing e-texts?
A: Penrod asserts that “deep assessment” is a qualitative assessment approach in which both instructors and students collect numerous writing “artifacts” (e.g. texts, data, comments, interviews, notes, etc.) for assessment (pp. 98-99). Penrod provides strategies for qualitative “deep assessment” (pp. 101-102). Finally, she argues that William L. Smith's “placement” concept of “adequacy” (in which compositionists “simply measure whether the writing is acceptable for the situation”) can be adopted for assessing the multimodal, context-dependent literacies of networked writing (p. 112).
Penrod argues that some composition teachers are currently using electronic portfolios in qualitative writing assessment—the “first foray into qualitative methods” (97). In addition, Penrod praises qualitative online writing databases and archival systems, such as the Online Learning Record (OLR) at the University of Texas at Austin and the TOPIC/ICON program at Texas Tech University.