![]() ![]() This can help avoid many issues with teacher scoring. ![]() Two data points are always better than one. Using AES can generate a second score for each essay when it is not appropriate to ask a second teacher to rate the essays. When humans score only for a summative purpose, for instance essays written for a final exam, they score quickly and often focus on superficial features that may be proxies for quality. Thinking first of a summative purpose for a writing assignment-an assessment of student proficiency, AES can help overcome some of the weaknesses associated with human scoring. It is this use of AES that I would recommend to curriculum developers, principals and ELA supervisors-having been in all those roles myself-as they find ways to both manage the increased writing demands of CCSS implementation and as a way to assure better quality scoring of student writing. Now, if the consortia had chosen to use AES to score writing, I would not be wringing my hands though I think the ideal use of AES is in conjunction with human readers. Gewertz’s blog views the question of whether the consortia will use AES as an open one, but everything I read including the test blueprints recently released by PARCC indicates that student writing will be “hand scored”, another odd term, which means humans will read the writing and assign a score point based on a rubric. Similarly based in linguistics, AES algorithms look at multiple text features. Grounded in linguistics, estimating text cohesion looks at over 80 features of text. In fact, AES algorithms are much more comparable to the work on text cohesion done at the University of Memphis, Coh-Metrix ( ). Lexile, Flesch-Kincaid) which only look at a couple text features, one concerning vocabulary and one concerning sentence length. It seems NCTE views automated essay scoring (AES) as very similar to computer estimations of readability (e.g. “Machine scoring” sounds mechanical and mindless which captures the NCTE view nicely, while Gewertz’s phrase “artificial intelligence scoring” suggests the process may in fact be intelligent and clever. The blog title echoes The National Council of Teachers of English phrase “machine scoring” from their position paper, Machine Scoring Fails the Test. And cost is, of course, high on states’ radars as they weigh their continued participation in the two groups. If they decide that humans must score the essays, the expense of the tests soars. ![]() The viability of artificial-intelligence scoring on the common assessments is a powerful cost manager for the two groups of states that are designing tests for the common standards. ![]() The post is entitled English Teachers Group Opposes Machine-Scored Writing and Catherine Gewertz concludes the post with this assessment: Two of these terms pop up in a recent EdWeek Curriculum Matters blog post. One example of the former is the scoring of student writing by a computer algorithm: automated essay scoring, artificial-intelligence scoring, automated essay grading and machine scoring are a sample of the terms. In education we call the same thing by many names, or we use the same term to mean many different things. One of the hallmarks and frustrations of the field of education is the imprecision of the language. ![]()
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