Machine Scoring of Student Essays: Truth and Consequences

Published on Jan 1, 2006
Patricia Freitag Ericsson2
Estimated H-index: 2
Richard Haswell2
Estimated H-index: 2
The current trend toward machine-scoring of student work, Ericsson and Haswell argue, has created an emerging issue with implications for higher education across the disciplines, but with particular importance for those in English departments and in administration. The academic community has been silent on the issuesome would say excluded from itwhile the commercial entities who develop essay-scoring software have been very active. Machine Scoring of Student Essays is the first volume to seriously consider the educational mechanisms and consequences of this trend, and it offers important discussions from some of the leading scholars in writing assessment.Reading and evaluating student writing is a time-consuming process, yet it is a vital part of both student placement and coursework at post-secondary institutions. In recent years, commercial computer-evaluation programs have been developed to score student essays in both of these contexts. Two-year colleges have been especially drawn to these programs, but four-year institutions are moving to them as well, because of the cost-savings they promise. Unfortunately, to a large extent, the programs have been written, and institutions are installing them, without attention to their instructional validity or adequacy.Since the education software companies are moving so rapidly into what they perceive as a promising new market, a wider discussion of machine-scoring is vital if scholars hope to influence development and-or implementation of the programs being created. What is needed, then, is a critical resource to help teachers and administrators evaluate programs they might be considering, and to more fully envision the instructional consequences of adopting them. And this is the resource that Ericsson and Haswell are providing here.
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  • Citations (45)
#1David Bartholomae (University of Pittsburgh)H-Index: 11
#1David S. Kaufer (CMU: Carnegie Mellon University)H-Index: 18
#2Cheryl Geisler (RPI: Rensselaer Polytechnic Institute)H-Index: 10
Last.Pantelis Vlachos (CMU: Carnegie Mellon University)H-Index: 6
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Cited By45
#1Simon Skov Fougt (Metropolitan University College)H-Index: 1
#2Amanda Siebert-Evenstone (UW: University of Wisconsin-Madison)H-Index: 2
Last.Morten Misfeldt (AAU: Aalborg University)H-Index: 8
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#1Simon Knight (UTS: University of Technology, Sydney)H-Index: 8
#2Simon Buckingham Shum (UTS: University of Technology, Sydney)H-Index: 37
Last.Xiaolong Wang (UTS: University of Technology, Sydney)H-Index: 2
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