Self-Characterization in the Self-Placement Assessment Ecology: Complicating the Stories We Tell about DSP’s Effects and Effectiveness

Abstract

Scholarship on student self-placement (SSP) emphasizes the importance of understanding methods like directed self-placement (DSP) as dynamic assessment ecologies (e.g., Inoue, 2015; Nastal et al., 2022; Wang, 2020), with implications not only for placement but also for how students conceptualize writing and themselves (e.g., Johnson, 2022). What can be learned about SSP’s ecological impacts by more meaningfully attending not just to patterns in students’ placement decisions but also to the qualitative content of their (self-)reflections and (self-)characterizations? Leveraging a dataset of more than 5,000 SSP pathways, we examine a corpus of short-answer survey responses, totaling more than half a million words, in which students wrote about their strengths as writers and what writing tasks they find most challenging. Students’ words help us understand how they see themselves as writers and how they conceive of college writing expectations. Through data analysis, this study found implications for how corpus data can be used to better understand potential tensions between students’ and institutions’ understandings of academic writing in a self-placement ecology.