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Grading papers is a “tedious, repetitive, and time-consuming” undertaking, one that invites the sort of sympathies one might receive upon the “death of a pet” (Baker, 2014, p. 36). Perhaps, though, the only thing more distasteful for an English professor than having to grade hundreds of essays each semester is having to hand the job over to the likes of a mindless software program. Automated Essay Evaluation (AEE), the process of scoring essays by computer, was developed in the 1960s but has mostly aroused suspicion and derogation from the composition community. The parable of the blindfolded villagers describing the same elephant from different parts of its body serves as an apt metaphor for how rhetorical choices screen in or screen out different facts about AEE. Each research camp reports on a different facet of AEE, but it is possible for these multiple realities to describe the same beast. This dissertation seeks to: (1) describe the rhetorical contours of arguments for and against AEE, both currently and historically, exposing their limitations and motivations; (2) explore through a small study how differential frames of data analysis affect interpretations of AEE’s utility, namely how error analysis adds an emphasis on individual students rather than student-aggregates; and (3) sketch ways that attention to errors is instructive in understanding the pitfalls of both human and machine scoring of essays. Because human scoring—despite its drawbacks—is still the superior method, I end by suggesting that AEE would be best used when the economic choice is between machine scoring and feedback, or no scoring and feedback at all. The context of online learning in globally disadvantaged populations is one such example.

Automated Essay Evaluation and the Computational Paradigm: Machine Scoring Enters the Classroom