Educational Testing Service

Vyacheslav Andreyev

Jill C Burstein

Daniel Marcu

Martin S Chodorow

Claudia Leacock

Yigal Attali

Chi Lu

Systems and methods for building a model for use in grading an essay are provided. A plurality of human graded essays are evaluated using a processor to generate a set of features. A score category is determined for each of the plurality of human graded essays. A weight is produced using a processor for each feature based on the score category for each of the plurality of human graded essays and the set of features. A model is generated using a processor based on the weights for the set of features for evaluating an essay. The set of features includes n features, where the first k features have optimized weights, and the last n&#x2212;k features have fixed predetermined weights.

Automatic essay scoring system

Systems and methods for building a trait model for essay evaluation are provided. At least one evaluated essay is received. A plurality of features pertaining to one or more traits from the at least one evaluated essay are identified and extracted. The one or more traits comprise writing errors, discourse, or vocabulary usage. A plurality of vector files based upon the plurality of features are created. A trait model for essay evaluation based upon the plurality of vector files is built. The trait model is evaluated, where the evaluation includes mapping features of a new essay to the trait model by navigating a multi-branched decision tree. At each branch of the decision tree, a value associated with the features of the new essay is used to determine how to proceed through the trait model.

Automated essay scoring

Systems and methods for creating a mathematical model for use in identifying discourse elements are described. A plurality of first essays relating to a particular subject are received, where each first essay is in an electronic format. Annotations for each first essay are received, where each annotation identifies at least one discourse element. Features are identified with a processor, where each feature is exhibited by at least one identified discourse element. Empirical frequencies are computed with a processor, where each empirical frequency relates to the presence of a feature with respect to the identified discourse elements across the plurality of first essays. Each empirical frequency is associated with the related identified discourse element with a processor. The empirical frequencies are utilized to select discourse elements in at least one second essay.

Methods for automated essay analysis

A method of grading an essay using an automated essay scoring system is provided. The method comprises the steps of deriving a set of predetermined features from the essay, wherein the predetermined feature set comprises one or more features that are independent from the test prompt, scoring the feature set with a scoring equation, wherein a multiple regression analysis with graded essay data produces weights for the scoring equation, generating a raw score for the essay; and processing the raw score for the essay into a score category based on an adaptive cutoff algorithm. Also provided is a method of generating a model in which to grade essays, wherein the data used to generate the model is independent from the test prompt or essay topic.

An essay is analyzed automatically by accepting the essay and determining whether each of a predetermined set of features is present or absent in each sentence of the essay. For each sentence in the essay a probability that the sentence is a member of a certain discourse element category is calculated. The probability is based on the determinations of whether each feature in the set of features is present or absent. Furthermore, based on the calculated probabilities, a sentence is chosen as the choice for the discourse element category.

Growing community of inventors

Ewing, NJ, United States of America

Vyacheslav Andreyev