Abstract: Systems and methods are provided for scoring a non-scripted speech sample. A system includes one or more data processors and one or more computer-readable mediums. The computer-readable mediums are encoded with a non-scripted speech sample data structure, where the non-scripted speech sample data structure includes: a speech sample identifier that identifies a non-scripted speech sample, a content feature extracted from the non-scripted speech sample, and a content-based speech score for the non-scripted speech sample. The computer-readable mediums further include instructions for commanding the one or more data processors to extract the content feature from a set of words automatically recognized in the non-scripted speech sample and to score the non-scripted speech sample by providing the extracted content feature to a scoring model to generate the content-based speech score.

Abstract: A teaching and learning method and system (143, 145, 147, 148, 152) communicates exercises, including URL's of Web pages and questions related thereto. Each exercise is distributed by the teacher's computer (143), synchronously, or downloaded by a student computer (147), asynchronously. An indicator (129) used on the Web page of one computer can be viewed on the same Web page displayed on the other computers. Responses are processed by the response server (145) and by comparison and evaluation logic, and displayed contemporaneously on the teacher's computer, together with scoring information (105, 106) and with URL's (107) associated with links used on each student computer. Scores are awarded automatically, or arbitrarily by clicking on the screen (105) of the teacher's computer. A feedback signal (80) may appear on the student computer with each keystroke to indicate its correctness. Responses selected on the teacher's computer may viewed on the student computers or on a Class Display (171).

Abstract: 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.

Abstract: Computer-implemented systems and methods are provided for assessing non-native spontaneous speech pronunciation. Speech recognition on digitized speech is performed using a non-native acoustic model trained with non-native speech to generate word hypotheses for the digitized speech. Time alignment is performed between the digitized speech and the word hypotheses using a reference acoustic model trained with native-quality speech. Statistics are calculated regarding individual words and phonemes in the word hypotheses based on the alignment. A plurality of features for use in assessing pronunciation of the speech are calculated based on the statistics, an assessment score is calculated based on one or more of the calculated features, and the assessment score is stored in a computer-readable memory.

Abstract: 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.

Abstract: Systems and methods are provided for selecting a proposed test item for inclusion in an examination where a non-multiple choice response to the proposed test item will be automatically scored. A proposed test item is analyzed to generate a proposed test item metric, where the proposed test item is a non-multiple choice test item. The proposed test item metric is provided to a proposed test item scoring model, where the proposed test item scoring model outputs a likelihood score indicative of a likelihood that automated scoring of a response to the proposed test item would be at or above a quality level. The proposed test item is selected for inclusion in the examination based on the likelihood score.

Abstract: Systems and methods are provided for the detection of sentiment in writing. A plurality of texts is received from a larger collection of writing samples with a computer system. A set of seed words from the plurality of texts are labeled as being of positive sentiment or of negative sentiment with the computer system. The set of seed words is expanded in size with the computer system to provide an expanded set of seed words. Intensity values are assigned to words of the expanded set of seed words. Each of the words of the expanded set of seed words is assigned three intensity values: a value corresponding to the strength of the word's association with a positive polarity class, a value corresponding to the strength of the word's association with a negative polarity class, and a value corresponding to the strength of the word's association with a neutral polarity class.

Abstract: Systems and methods are provided for carrying out an examination with a client computer. A client computer is booted using a secondary operating system other than a primary operating system of the client computer that is accessed from a computer readable medium provided by an administrator of an examination only on the day of the examination. A broadcast message is sent from the client computer to check for the existence of a local server and to establish communication with the local server. A client application is received from the local server for presenting the examination at the client computer. Assessment content is received for the examination from the local server. The examination including the assessment content is presented to a user of the client computer with the client application, and user responses are received at the client computer and transmitted to the local server.

Abstract: Computer-implemented systems and methods are provided for assessing non-native spontaneous speech pronunciation. Speech recognition on digitized speech is performed using a non-native acoustic model trained with non-native speech to generate word hypotheses for the digitized speech. Time alignment is performed between the digitized speech and the word hypotheses using a reference acoustic model trained with native-quality speech. Statistics are calculated regarding individual words and phonemes in the word hypotheses based on the alignment. A plurality of features for use in assessing pronunciation of the speech are calculated based on the statistics, an assessment score is calculated based on one or more of the calculated features, and the assessment score is stored in a computer-readable memory.

Abstract: A concept rater module is utilized to automatically grade or score constructed responses based on a model answer. The concept rater module may be configured to accept a model answer as input. The model answer may be used as a grading key by the concept rater module. The concept rater module may be further configured to accept student responses in a file format. The file format may be ASCII text, a formatted word processing (e.g., WORDPERFECT, MICROSOFT WORD, etc.) and the like. The concept rater module may be further configured to process a student response into a canonical representation of the student response. The canonical representation of the student response is compared against the model answer by the concept rater module. From the comparison, a score is generated which represents that student's ability to cover all the key concepts.

Abstract: A Consolidated Online Assessment System (COLA System) that creates, manipulates, and distributes an objected-oriented paradigm that represents the scoring and related activities as a unified and integrated family of loosely coupled objects, most notably a “Case” representing a state-machine that replaces the constructed response from the test taker as the unit of work. The Scoring Model and associated Properties that encapsulate the business rules associated with what actions are appropriate or required for a unit of work and the Responses to the unit of work which represent the test-taker-contributed content (e.g., essays or other text-based responses, audio responses, digitized video responses, scanned images, diagrams, lessons plans, etc.) and ties that content to its creator are linked to a Distinct Scorable Unit (DSU) which represents a tree-based mechanism that connects and provides inheritability for the other primary system objects.

Abstract: A method, system and computer-readable carrier for using a discrimination index to select test items from an item bank for a test are disclosed. At least one parameter may be identified for each of a plurality of test items in an item bank. A first test item may be selected from the item bank based on at least the parameter for the test item. Each unselected test item may be evaluated to determine whether one or more constraints would be satisfied if the test item were selected. A next test item may be selected from the unselected test items that satisfy the one or more first constraints based on at least the parameter for each test item. The Evaluation and test item selection processes may be repeated until one or more second constraints are satisfied.

Abstract: A method, system and processor-readable storage medium for evaluating vocabulary similarity are disclosed. A generic rate may be determined for each word in a plurality of first responses. Each first response may respond to one of a plurality of first prompts. At least one first response may respond to each of the first prompts. A specific rate may be determined for each word in a plurality of second responses, which each respond to a second prompt. A target response may be received that is associated with the second prompt and has a plurality of words. A vocabulary similarity index may be computed for the target response based on one or more generic rates and on or more specific rates. A determination of whether the target response is off-topic may be made based on the vocabulary similarity index for the target response.

Abstract: Systems and methods are provided for adapting a text, for example, for generating a text appropriate for the proficiency level of a language learner, are described. An original text is received and at least one portion of the original text to adapt is identified, where the adapted text will facilitate reader comprehension. An adaptation to be applied to the at least one portion of the original text is determined. The at least one portion of the original text is analyzed based on the adaptation, the adaptation is applied to the at least one portion of the original text, and the adapted text is provided.

Abstract: A computer-based test creation system and method for generating test items. The test creation process includes creating items in electronic form and allowing access by a plurality of reviewers via a network. The reviewers may review the item for content, fairness and format, and collaborate with each other before indicating that the item is acceptable for inclusion in a test. After the administration of the items to test takers, item performance may be evaluated on-line by reviewing electronic feedback from the administrations and creating electronic evaluation reports. Since the items are created in an electronic form, the items may further include new forms of stimuli such as graphics and multimedia.

Abstract: A method and system for automatically generating a scoring model for scoring a speech sample are disclosed. One or more training speech samples are received in response to a prompt. One or more speech features are determined for each of the training speech samples. A scoring model is then generated based on the speech features. At least one of the training speech samples may be a high entropy speech sample. An evaluation speech sample is received and a score is assigned to the evaluation speech sample using the scoring model. The evaluation speech sample may be a high entropy speech sample.

Abstract: A computerized method and system for creating test items by generating variants from a test item model, comprising the steps of creating a new test item model by identifying elements of an initial test item or test item model to be variabilized, variabilizing the elements thereby creating test item variables, indicating values the variables can assume, defining the variables, and generating test item variants utilizing a simultaneous constraint solver. The initial test item can be a pre-existing test item or test item model, a newly created test item or even a conceptual template in the mind of the test item creator. The generated test item variants are displayed to the test item creator. The test item creator can store and forward acceptable test item variants for later use as test items. Test item models can be stored for later use in generating new test item variants.

Abstract: A method and system for customizing an automated essay scoring system are disclosed. Relative weights may be assigned to a plurality of features for a plurality of benchmark essay responses. Based on the relative weights, automated scores may be determined for one or more first essay responses to a first prompt. The automated scores may be scaled based on one or more scoring standards for the first prompt. A scaled automated score may be assigned for a second essay response to the first prompt based on the scaled automated scores for the one or more first essay responses.

Abstract: Systems and methods are provided for determining a difficulty level of a text. A determination is made as to a number of cohesive devices present in a text. A further determination is made as to a number of cohesive devices expected in the text. A cohesiveness metric is calculated based on the number of cohesive devices present in the text and the number of cohesive devices expected in the text, where the cohesiveness metric is used to identify a difficulty level of the text.

Abstract: A method and system for determining text coherence in an essay is disclosed. A method of evaluating the coherence of an essay includes receiving an essay having one or more discourse elements and text segments. The one or more discourse elements are annotated either manually or automatically. A text segment vector is generated for each text segment in a discourse element using sparse random indexing vectors. The method or system then identifies one or more essay dimensions and measures the semantic similarity of each text segment based on the essay dimensions. Finally, a coherence level is assigned to the essay based on the measured semantic similarities.