US 6,181,909 B2Grant
System and method for computer-based automatic essay scoring
Issue Date:2001-01-30
•7 Claims
•1 Drawing Sheets
Abstract
A method of grading an essay using an automated essay scoring system is provided. The method comprises the automated steps of (a) parsing the essay to produce parsed text, wherein the parsed text is a syntactic representation of the essay, (b) using the parsed text to create a vector of syntactic features derived from the essay, (c) using the parsed text to create a vector of rhetorical features derived from the essay, (d) creating a first score feature derived from the essay, (e) creating a second score feature derived from the essay, and (f) processing the vector of syntactic features, the vector of rhetorical features, the first score feature, and the second score feature to generate a score for the essay. The essay scoring system comprises a Syntactic Feature Analysis program which creates a vector of syntactic features of the electronic essay text, a Rhetorical Feature Analysis program which creates a vector of rhetorical features of the electronic essay text, an EssayContent program which creates a first Essay Score Feature, an ArgContent program which creates a second Essay Score Feature, and a scoring engine which generates a final score for the essay from the vector of syntactic features, the vector of rhetorical features, the first score feature, and the second score feature.
Metadata
Assignee
- Educational Testing Service
Inventors
- Jill C. Burstein
- Lisa Braden-Harder
- Martin S. Chodorow
- Bruce A. Kaplan
- Karen Kukich
- Chi Lu
- Donald A. Rock
- Susanne Wolff
Application Information
Application Number:US 09/120,427
Filing Date:1998-07-22
Priority Date:1997-07-22
Art Unit:7
Classifications
IPC:
G09B 700
Field of Search:
434353434354434118434322434350434363434178382321704 4704 6704 9
Patent Drawings (1 sheets)
Description
CROSS REFERENCE TO RELATED APPLICATIONS
[0002] This application is related to U.S. Provisional Patent Application Ser. No. 60/053,375, filed Jul. 22, 1997, entitled “computer Analysis of Essay Content for Automated Score Prediction,” the contents of which are hereby incorporated by reference in their entirety.
FIELD OF THE INVENTION
[0003] This invention generally relates to the field of computer-based test scoring systems, and more particularly, to automatic essay scoring systems.
BACKGROUND OF THE INVENTION
[0004] For many years, standardized tests have been administered to examinees for various reasons such as for educational testing or for evaluating particular skills. For instance, academic skills tests, e.g., SATs, LSATs, GMATs, etc., are typically administered to a large number of students. Results of these tests are used by colleges, universities and other educational institutions as a factor in determining whether an examinee should be admitted to study at that particular institution. Other standardized testing is carried out to determine whether or not an individual has attained a specified level of knowledge, or mastery, of a given subject. Such testing is referred to as mastery testing, e.g., achievement tests offered to students in a variety of subjects, and the results are used for college credit in such subjects.
[0005] Many of these standardized tests have essay sections. These essay portions of an exam typically require human graders to read the wholly unique essay answers. As one might expect, essay grading requires a significant number of work-hours, especially compared to machine-graded multiple choice questions. Essay questions, however, often provide a more well-rounded assessment of a particular test taker's abilities. It is, therefore, desirable to provide a computer-based automatic scoring system.
[0006] Typically, graders grade essays based on scoring rubrics, i.e., descriptions of essay quality or writing competency at each score level. For example, the scoring guide for a scoring range from 0 to 6 specifically states that a “6” essay “develops ideas cogently, organizes them logically, and connects them with clear transitions.” A human grader simply tries to evaluate the essay based on descriptions in the scoring rubric. This technique, however, is subjective and can lead to inconsistent results. It is, therefore, desirable to provide an automatic scoring system that is accurate, reliable and yields consistent results.
[0007] Literature in the field of discourse analysis points out that lexical (word) and structural (syntactic) features of discourse can be identified (Mann, William C. and Sandra A. Thompson (1988): Rhetorical Structure Theory: Toward a functional theory of text organization, Text 8(3), 243-281) and represented in a machine, for computer-based analysis (Cohen, Robin: A computational theory of the function of clue words in argument understanding, in “Proceedings of 1984 International Computational Linguistics Conference.” California, 251-255 (1984); Hovy, Eduard, Julia Lavid, Elisabeth Maier, Vibhu Nettal and Cecile Paris: Employing Knowledge Resources in a New Text Planner Architecture, in “Aspects of Automated NL Generation,” Dale, Hony, Rosner and Stoch (Eds), Springer-Veriag Lecture Notes in Al no. 587, 57-72 (1992); Hirschberg, Julia and Diane Litman: Empirical Studies on the Disambiguation of Cue Phrases, in “Computational Linguistics” (1993), 501-530 (1993); and Vander Linden, Keith and James H. Martin: Expressing Rhetorical Relations in Instructional, Text: A Case Study in Purpose Relation in “Computational Linguistics” 21(1), 29-57 (1995)).
[0008] Previous work in automated essay scoring, such as by Page, E. B. and N. Petersen: The computer moves into essay grading: updating the ancient test. Phi Delta Kappa; March, 561-565 (1995), reports that predicting essay scores using surface feature variables, e.g., the fourth root of the length of an essay, shows correlations as high as 0.78 between a single human rater (grader) score and machine-based scores for a set of PRAXIS essays. Using grammar checker variables in addition to word counts based on essay length yields up to 99% agreement between machine-based scores that match human rater scores within 1 point on a 6-point holistic rubric. These results using grammar checker variables have added value since grammar checker variables may have substantive information about writing competency that might reflect rubric criteria such as, essay is free from errors in mechanics, usage and sentence structure.
SUMMARY OF THE INVENTION
[0009] A method of grading an essay using an automated essay scoring system is provided. The method comprises the steps of (a) parsing the essay to produce parsed text, wherein the parsed text is a syntactic representation of the essay, (b) using the parsed text and discourse-based heuristics to create a vector of syntactic features derived from the essay, (c) using the parsed text to create a vector of rhetorical features derived from the essay, (d) creating a first score feature derived from the essay, (e) creating a second score feature derived from the essay, and (f) processing the vector of syntactic features, the vector of rhetorical features, the first score feature, and the second score feature to generate a score for the essay.
[0010] In a preferred embodiment, the method further comprises the step of (g) creating a predictive feature set for the test question, where the predictive feature set represents a model feature set for the test question covering a complete range of scores of a scoring guide for the test question, wherein in step (f), a scoring formula may be derived from the predictive feature set and the score for the essay may be assigned based on the scoring guide. Preferably, a batch of original essays, which are essays of a known score to a test question, are used in accordance with the model feature of the invention to create the predictive feature set. Creating the predictive feature set in this manner comprises the steps of repeating steps (a) through (f) for the batch of original essays and processing the vector of syntactic features, the vector of rhetorical features, the first score feature, and the second score feature for each original essay using a linear regression to generate the predictive feature set for the test question.
[0011] Preferably, each essay is already in the form of electronic essay text as in the case with on-line essay testing. If this is not the case, however, then the method of the present invention further comprises the step of converting the essay into the form of electronic essay text.
[0012] A computer-based automated essay scoring system for grading an essay also is provided. The essay scoring system comprises a Syntactic Feature Analysis program which creates a vector of syntactic features of the electronic essay text, a Rhetorical Feature Analysis program which creates a vector of rhetorical features of the electronic essay text, an EssayContent program which creates a first Essay Score Feature, an ArgContent program which creates a second Essay Score Feature, and a score generator which generates a final score for the essay from the vector of syntactic features, the vector of rhetorical features, the first score feature, and the second score feature.
[0013] In a preferred embodiment, the essay scoring system further comprising a parser for producing a syntactic representation of each essay for use by the Syntactic Feature Analysis program and the Rhetorical Feature Analysis program. In another preferred embodiment, the essay scoring system further comprising a Stepwise Linear Regression program which generates a predictive feature set representing a model feature set that is predictive of a range of scores for the test question which is provided to the scoring engine for use in assessing the final score for the essay.
BRIEF DESCRIPTION OF THE DRAWING
[0014] The present invention will be better understood, and its numerous objects and advantages will become more apparent, by reference to the following detailed description of the invention when taken in conjunction with the following drawing, of which:
[0015] FIG. 1 is a functional flow diagram for a preferred embodiment of the e-rater system of the present invention.
DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS
[0016] A computer-based system designed to automatically score essay responses is described herein. Solely for illustrative purposes, the following description of the invention focuses on the standardized GMAT Analytical Writing Assessments: (a) Analysis of an Argument (Argument essays) and (b) Analysis of an Issue (Issue essays) item types, examples of which are shown in Appendix A1 and Appendix A2, respectively. The system of the present invention, named e-rater as in Electronic Essay Rater, automatically analyzes several features of an essay and scores the essay based on the features of writing as specified in holistic scoring rubrics (descriptions of essay quality or writing competency at each score level of a 6-point scoring guide used by several standardized exams such as the GMAT, with 6 being the best score).
[0017] The present system automatically rates essays using features that reflect the 6-point holistic rubrics used by human raters to assign scores to essay responses. E-rater is completely automated so that it can be quickly moved into an operationally-ready mode and uses rubric-based features to evaluate essay responses, such as rhetorical structure, vocabulary and syntactic features.
[0018] E-rater uses a hybrid feature methodology. It incorporates several variables that are derived statistically, or extracted through Natural Language Processing (NLP) techniques. As described in this specification, e-rater uses four sets of critical feature variables to build the final linear regression model used for predicting scores, referred to as predictor variables. All predictor variables and counts of predictor variables are automatically generated by several independent computer programs. For argument and issue essay types, all relevant information about the variables are introduced into a stepwise linear regression in order to evaluate the predictive variables, i.e., the variables that account for most of the variation between essays at different score intervals. Variables included in e-rater's final score prediction model for argument and issue essays are: (a) structural features, (b) rhetorical structure analyses, (c) content vector analyses, and (d) content vector analyses by argument (argument vector analyses). A conceptual rationale and a description of how each variable is generated is described below.
[0019] A. Structural Features
[0020] The scoring guides for both argument and issue essays indicate that one feature used to rate an essay is “syntactic variety.” Syntactic structures in essays can be identified using NLP techniques. In the present invention, all sentences in the essay responses are parsed. The parser takes a sentence string as input and returns a syntactically analyzed version of a sentence, as illustrated in Table 1. Examination of syntactic structures in an essay response yields information about the “syntactic variety” in the essay. For example, information about what types of clauses or verb types can reveal information about “syntactic variety.” In Table 1, DEC is a declarative sentence, NP is a Noun phrase, AJP is an adjective phrase, ADJ is an adjective, NOUN is a noun, PP is a prepositional phrase, PREP is a preposition, INFCL is an infinitive clause, DETP is a determiner phrase, and CHAR is a character.
[0021] A program for examining syntactic structure was run on approximately 1,300 essays. The program counted the number of complement clauses, subordinate clauses, infinitive clauses, relative clauses and the subjunctive modal auxiliary verbs such as would, could, should, might and may, for each sentence in an essay. A linear regression analysis then selected the variables in Table 2 as predictive variables for the final score prediction model. By using these predictive variables, a vector of syntactic counts (42 in FIG. 1) for each essay is generated and is used by e-rater is the final scoring.
[0022] B. Rhetorical Structure Analysis
[0023] In both argument and issue essays, the scoring guides indicate that an essay will receive a score based on the examinee's demonstration of a well-developed essay. For the argument essay, the scoring guide states specifically that a “6” essay “develops ideas cogently, organizes them logically, and connects them with clear transitions.” For the issue essay, a “6” essay “develops a position on the issue with insightful reasons . . . ” and the essay “is clearly well-organized.”
[0024] Language in holistic scoring guides, such as “cogent”, “logical,” “insightful,” and “well-organized” have “fuzzy” meaning because they are based on imprecise observation. Methods of “fuzzy logic” can be used to automatically assign these kinds of “fuzzy” classifications to essays. This part of the present invention identifies the organization of an essay through automated analysis of the rhetorical (argument) structure of the essay.
[0025] The linguistic literature about rhetorical structure (Cohen (1984), Hovy et al. (1992), Hirschberg and Litman (1993), and Vander Linden and Martin (1995)) point out that rhetorical (or discourse) structure can be characterized by words, terms and syntactic structures. For instance, words and terms that provide “clues” about where a new argument starts, or how it is being developed are discussed in the literature as “clue words.”
[0026] Conjunctive relations from Quirk, Randolph, Sidney Greenbaum, Geoffrey Leech, and Jan Svartik: A Comprehensive Grammar of the English Language, Longman, N.Y. (1985) including terms such as, “In summary” and “In conclusion,” are considered to be clue words and are classified as conjuncts used for summarizing. Clue words such as “perhaps,” and “possibly” are considered to be “belief” words used by a writer to express a belief in developing an argument in the essay. Words like “this” and “these” may often be used to flag that the writer has not changed topics (Sidner, Candace: 1986, Focusing in the Comprehension of Definite Anaphora, in “Readings in Natural Language Processing,” Barbara Grosz, Karen Sparck Jones, and Bonnie Lynn Webber (Eds.), Morgan Kaufmann Publishers, Los Altos, Calif., 363-394). It also was observed that in certain discourse contexts, structures such as infinitive clauses (INFCL) mark the beginning of a new argument, e.g., “To experimentally support their argument, Big Boards(INFCL) would have to do two things.”
[0027] One part of the present invention is an automated argument partitioning and annotation program (APA). APA outputs a file for each essay after it is partitioned into argument units. In addition, APA outputs a second file in which each sentence in an essay is annotated with word, term or structure classifications that denote argument structure.
[0028] A specialized dictionary (lexicon) is used by APA to identify relevant clue words and terms. The lexicon used by e-rater is displayed in Appendix B1.
[0029] APA's heuristics select the dictionary-based clue words, terms, and non-lexical structures. Descriptions of the rules used by APA appear in Appendix B2. The rules that APA uses to partition and annotate arguments specify syntactic structure and the syntactic contexts in which clue words contribute to argument structure. APA uses parsed essays to identify syntactic structures in essays. Essays have been syntactically parsed and each sentence in the essay has a syntactic analysis. Table 3 illustrates original essay text and the text output by APA with corresponding argument partitioning annotations, where wording in the argument-unit annotations has been revised for comprehensibility.
[0030] Table 4 shows the rhetorical variables used by e-rater for predicting scores. By using these predictive variables, a vector of rhetorical feature counts (54 in FIG. 1) for each essay is generated and used by e-rater in final scoring.
[0031] C. Content Vector Analysis
[0032] The scoring rubric suggests that certain ideas are expected in an essay by stating that the essay “effectively supports the main points of the critique” for argument essays and “explores ideas and develops a position on the issue with insightful reasons and/or persuasive examples” for the issue essays. Content vector (CV) analysis is a statistical weighting technique used to identify relationships between words and documents. With regard to the approximate specifications in the rubric about essay content, CV analysis can be used to identify vocabulary (or content words) in essays that appear to contribute to essay score.
[0033] Assigning one of six scores to a GMAT essay is a standard type of classification problem. Statistical approaches to classification define each class (score) by the distribution of characteristics found in labeled training examples. Then, each test essay is analyzed, and its distribution is compared to that of the known classes. The class which best matches the test essay is selected.
[0034] For text, the characteristics may be physical (the number or length of words, sentences, paragraphs, or documents), lexical (the particular words that occur), syntactic (the form, complexity, or variety of constructions), rhetorical (the number or type of arguments), logical (the propositional structure of the sentences), or a combination of these.
[0035] Standard CV analysis characterizes each text document (essay) at the lexical (word) level. The document is transformed into a list of word-frequency pairs, where frequency is simply the number of times that the word appeared in the document. This list constitutes a vector which represents the lexical content of the document. Morphological analysis can optionally be used to combine the counts of inflectionally-related forms so that “walks,” “walked,” and “walking” all contribute to the frequency of their stem, “walk.” In this way, a degree of generalization is realized across morphological variants. To represent a whole class of documents, such as a score level for a set of essays, the documents in the class are concatenated and a single vector is generated to represent the class.
[0036] CV analysis refines this basic approach by assigning a weight to each word in the vector based on the word's salience. Salience is determined by the relative frequency of the word in the document (or class) and by the inverse of its frequency over all documents. For example, “the” may be very frequent in a given document, but its salience will be low because it appears in all documents. If the word “pterodactyl” appears even a few times in a document, it will likely have high salience because there are relatively few documents that contain this word.
[0037] A test essay is compared to a class by computing a cosine correlation between their weighted vectors. The cosine value is determined by the following equation:
[0038] cos=Σ(ai* bi)/sqrt(Σ(ai2)*Σ(bi2))
[0039] where aiis the frequency of word “i” in document “a” and biis the frequency of word “i” in document “b.” The larger the value of the correlation, the closer the test essay is to the class. The class which is closest to the test essay is selected and designated “Essay Score Feature A” (22 in FIG. 1). These steps are summarized below.
[0040] Vector construction for each document (or class):
[0041] Extract words from document (or combined documents)
[0042] Apply morphological analysis (optional)
[0043] Construct frequency vector
[0044] Assign weights to words to form weighted vector
[0045] Compute cosine correlation between test essay vector and the vector of each class
[0046] Select class with highest correlation
[0047] As discussed in the next section, CV analysis can also be applied to units of text smaller than essays. For example, it can be used to evaluate the rhetorical arguments within an essay. In this case, each argument is treated like a mini-document and is compared to the classes independently of the other arguments. The result is a vector of classes (scores), one for each argument in the essay.
[0048] E-rater uses a CV analysis computer program which automatically predicts scores for both argument and issue essays. The scores assigned by the CV analysis program are used as a predictor variable for the set of argument essays.
[0049] D. Argument-Content Vector Analysis
[0050] An important goal of this invention is to be able to predict essay scores based on “what the writer says.” CV analysis, as it is used above, identifies word associations over the essay as a whole. It looks at words randomly in the essay. Although this tells the reader something about possible essay content, it is important to capture words in a more structured way, so that topic may be identified using closely clustered word groupings.
[0051] The scoring rubric specifies that relevant essay content (or relevant words used in an essay) should be well organized and should address relevant content. Therefore, a revised version of the content vector analysis program was implemented and run on the “argument partitioned” training essays for argument and issue essays.
[0052] Another content similarity measure, ArgContent, is computed separately for each argument in the test essay and is based on the kind of term weighting used in information retrieval. For this purpose, the word frequency vectors for the six score categories, described above, are converted to vectors of word weights. The weight for word “i” in score category “s” is:
[0053] wi,s=(freqi,s/max_freqs)*log(n_essaystotal/n_essaysi)
[0054] where freqi,sis the frequency of word “i” in category “s,” max_freqsis the frequency of the most frequent word in category “s” (after a stop list of words has been removed), n_essaystotalis the total number of training essays across all six categories, and n_essaysiis the number of training essays containing word “i.”
[0055] The first part of the weight formula represents the prominence of word “i” in the score category, and the second part is the log of the word's inverse document frequency (IDF). For each argument “a” in the test essay, a vector of word weights is also constructed. The weight for word “i” in argument “a” is:
[0056] wi,a=(freqi,a/max_freqa)*log(n_essaystotal/n_essaysi)
[0057] where freqi,ais the frequency of word “i” in argument “a,” and max_freqais the frequency of the most frequency word in “a” (once again, after a stop list of words has been removed). Each argument (as it has been partitioned) is evaluated by computing cosine correlations between its weighted vector and those of the six score categories, and the most similar category is assigned to the argument. As a result of this analysis, e-rater has a set of scores (one per argument) for each test essay. The final score is then calculated as an adjusted mean of the set of scores, represented as ArgContent:
[0058] ArgContent=((arg_scores+n_args)/(n_args+1)
[0059] This final score output is designated “Essay Score Feature B” (62 in FIG. 1).
[0060] E. The e-rater System Overview
[0061] FIG. 1 shows a functional flow diagram for a preferred embodiment of the e-rater system of the present invention. The first step in automatically scoring an essay is creating a model feature set, i.e., a model feature set used to predict scores at each score point of the scoring rubric. The system starts with a batch of approximately 250-300 original electronic essay text responses (essays already having a known score). Each original electronic essay text is evaluated by EssayContent to perform Content Vector Analysis (as described in Section C above) and to generate “Essay Score Feature A” and is also parsed by the parser 30 to produce a “syntactic” representation of each essay response, denoted as parsed essay text 32.
[0062] Syntactic Feature Analysis 40 (program clause.c) then processes the parsed essay text 32 to extract syntactic information (as described above in Section A entitled “Structural Features”) and creates a vector of syntactic feature counts 42 for each syntatic feature considered by e-rater. Rhetorical Feature Analysis 50 (program gmat.c) also processes the parsed essay text 32 (as described above in Section B entitled “Rhetorical Structure Analysis”) to generate annotated text 52, which includes a vector of rhetorical feature counts 54 and text partitioned into independent arguments 56. This argument partitioned text 56 is then evaluated by ArgContent to perform Argument-Content Vector Analysis (Section D above) to produce “Essay Score Feature B” 62.
[0063] The vector of syntactic features 42, the vector of rhetorical features 54, Essay Score Feature A 22, and Essay Score Feature B 62 are then fed (depicted by the phantom arrows) into a stepwise linear regression 70, from which a “weighted” predictive feature set 72 is generated for each test question using the batch of sample data. The set of weighted predictive features define the model feature set for each test question.
[0064] The steps just described above up to the linear regression 70 are then performed for a score to be predicted for each actual essay response. The vector of syntactic features 42, the vector of rhetorical features 54, Essay Score Feature A 22, and Essay Score Feature B 62 for each response are then fed (depicted by the solid arrows) into the score calculation program 80 associated with the model answer for the test question with which the essay is associated and a Final Score 90 between 0 and 6 is generated.
[0065] It will be appreciated by those skilled in the art that the foregoing has set forth the presently preferred embodiment of the invention and an illustrative embodiment of the invention, but that numerous alternative embodiments are possible without departing from the novel teachings of the invention. All such modifications are intended to be included within the scope of the appended claims.
APPENDIX A1: ANALYSIS OF AN ARGUMENT ITEM
ANALYSIS OF AN ARGUMENT
Time—30 minutes
[0066] Directions: In this section you will be asked to write a critique of the argument presented below. You are not being asked to present your own views on the subject.
[0067] Read the argument and the instructions that follow it, and then make any notes in your test booklet that will help you plan your response. Begin writing your response on the separate answer document. Make sure that you use the answer document that goes with this writing task.
[0068] The following is from a campaign by Big Boards, Inc., to convince companies in River City that their sales will increase if they use Big Boards billboards for advertising their locally manufactured products.
NOTES
[0069] Use the space below or on the facing page to plan your response. Any writing on these pages will not be evaluated.
STOP
[0070] YOU FINISH BEFORE TIME IS CALLED, YOU MAY CHECK YOUR WORK ON THIS SECTION ONLY. DO NOT TURN TO ANY OTHER SECTION IN THE TEST.
APPENDIX A2: ANALYSIS OF AN ISSUE ITEM
ANALYSIS OF AN ISSUE
Time—30 minutes
[0071] Directions: In this section you will need to analyze the issue presented below and explain your views on it The question has no “correct” answer. Instead, you should consider various perspectives as you develop your own position on the issue.
[0072] Read the statement and the instructions that follow it, and then make any notes in your test booklet that will help you plan your response. Begin writing your response on the separate answer document. Make sure that you use the answer document that goes with this writing task.
NOTES
[0073] Use the space below or on the facing page to plan your response. Any writing on these pages will not be evaluated.
STOP
[0074] IF YOU FINISH BEFORE TIME IS CALLED, YOU MAY CHECK YOUR WORK ON THIS SECTION ONLY. DO NOT TURN TO ANY OTHER SECTION IN THE TEST.
Claims
We claim:
1. A method of grading an essay using an automated essay scoring system, the essay being a response to a test question and in the form of electronic essay text, the method comprising the steps of:
(a) parsing the essay to produce parsed text, wherein the parsed text is a syntactic representation of the essay;
(b) using the parsed text to create a vector of syntactic features derived from the essay;
(c) using the parsed text to create a vector of rhetorical features derived from the essay;
(d) creating a first score feature derived from the essay;
(e) creating a second score feature derived from the essay; and
(f) processing the vector of syntactic features, the vector of rhetorical features, the first score feature, and the second score feature to generate a score for the essay.
2. The method of claim 1 further comprising the step of:
(g) creating a predictive feature set for the test question, where the predictive feature set represents a model feature set that is predictive of a range of scores for the test question,
wherein in step (f), a scoring formula is derived from the predictive feature set and the score for the essay is assigned based on the scoring guide.
3. The method of claim 2, wherein there is a batch of original essays which are essays of a known score to the test question and in the form of original electronic essay texts, and wherein step (g) of creating a predictive feature set comprises the steps of repeating steps (a) through (f) for the batch of original essays and processing the vector of syntactic features, the vector of rhetorical features, the first score feature, and the second score feature for each original essay using a linear regression to generate a predictive feature set for the test question.
4. The method of claim 1, wherein the scoring guide ranges from 0 to 6 based on holistic scoring rubrics.
5. A computer-based automated essay scoring system for grading an essay, the essay being a response to a test question, the scoring system comprising:
a Syntactic Feature Analysis program which creates a vector of syntactic features of the electronic essay text;
a Rhetorical Feature Analysis program which creates a vector of rhetorical features of the electronic essay text;
an EssayContent program which creates a first Essay Score Feature;
an ArgContent program which creates a second Essay Score Feature; and
a scoring engine which generates a final score for the essay from the vector of syntactic features, the vector of rhetorical features, the first score feature, and the second score feature.
6. The essay scoring system of claim 5 further comprising:
a parser for producing a syntactic representation of each essay for use by the Syntactic Feature Analysis program and the Rhetorical Feature Analysis program.
7. The essay scoring system of claim 6 further comprising:
a Stepwise Linear Regression program which generates a predictive feature set representing a model feature set that is predictive of a range of scores for the test question which is provided to the scoring engine for use in assessing the final score for the essay.
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