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Rudner, Lawrence - Gagne, Phill Source: It is not surprising that extended-response items, typically short essays, are now an integral part of most large-scale assessments.
Extended response items provide an opportunity for students to demonstrate a wide range of skills and knowledge, including higher order thinking skills such as synthesis and analysis.
Yet assessing students' writing is one of the most expensive and time-consuming activities for assessment programs. Prompts need to be designed, rubrics created, multiple raters need to be trained, and then the extended responses need to be scored, typically by multiple raters.
With different people evaluating different essays, interrater reliability becomes an additional concern in the writing assessment process. Even with rigorous training, differences in the background, training, and experience of the raters can lead to subtle but important differences in grading.
Computers and artificial intelligence have been proposed as tools to facilitate the evaluation of student essays. In theory, computer scoring can be faster, reduce costs, increase accuracy and eliminate concerns about rater consistency and fatigue.
Latent Semantic Analysis (LSA), (3) Electronic Essay Rater (e -rater), (4) Bayesian Essay Test Scoring sYstem BETSY), and others. PEG grades essays based on the quality of writing. The other systems included in this category are the Bayesian Essay Test Scoring sYstem (BETSY), PEG, LightSIDE and Schema Extract Analysis and Report (SEAR). The LSA systems do more than a simple analysis of co-occurring terms. Those who are interested in pursuing essay scoring may be interested in the Bayesian Essay Test Scoring sYstem (BETSY), being developed by the author based on the naive Bayes text classification literature (e.g., McCallum and Nigam, ). Free software is available for research use.
Further, the computer can quickly re-score materials should the scoring rubric be redefined. This articles describes the three most prominent approaches to essay scoring.
Descriptions of these approaches can be found at the web sites listed at the end of this article and in Whittington and Hunt and Wresch Page uses a regression model with surface features of the text document length, word length, and punctuation as the independent variables and the essay score as the dependent variable.
Landauer's approach is a factor-analytic model of word co-occurrences which emphasizes essay content. Burstein uses a regression model with content features as the independent variables. The underlying theory is that there are intrinsic qualities to a person's writing style called trins that need to be measured, analogous to true scores in measurement theory.
PEG uses approximations of these variables, called proxes, to measure these underlying traits.
Specific attributes of writing style, such as average word length, number of semicolons, and word rarity are examples of proxes that can be measured directly by PEG to generate a grade. For a given sample of essays, human raters grade a large number of essays toand determine values for up to 30 proxes.
The grades are then entered as the criterion variable in a regression equation with all of the proxes as predictors, and beta weights are computed for each predictor. For the remaining unscored essays, the values of the proxes are found, and those values are then weighted by the betas from the initial analysis to calculate a score for the essay.
Page has over 30 years of research consistently showing exceptionally high correlations. In one study, Page analyzed samples of and senior essays from the and National Assessment of Educational Progress using responses to a question about a recreation opportunity: With 20 variables, PEG reached multiple Rs as high as.
The underlying idea is to identify which of several calibration documents are most similar to the new document based on the most specific i.Assessor (IEA), Bayesian Essay Test Scoring System (BETSY), and Vantage Learning’s IntelliMetric.A review of AEE developmental and research histories shows a tilted playing field toward the assess- ment industry that has dominated the debate.
Bayesian probability is an interpretation of the concept of probability, in which, instead of frequency or propensity of some phenomenon, probability is interpreted as reasonable expectation representing a state of knowledge or as quantification of a personal belief.
Two Bayesian models for text classification from the information science field were extended and applied to student produced essays. Both models were calibrated using essays with two score points.
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Those who are interested in pursuing essay scoring may be interested in the Bayesian Essay Test Scoring s Ystem (BETSY), being developed by the author based on .
Bayesian Essay Test Scoring sYstem, developed by Larkey in , is based on naive Bayesian model.
It is the only open-source AES system, but has not been put into practical use yet. It is the only open-source AES system, but has not been put into practical use yet.