Educational Testing Service

Mo Zhang

Paul Douglas Deane

Jiangang Hao

Chen Li

Michael Flor

Yigal Attali

Gary Feng

Brent Bridgeman

Randy E Bennett

Shelby J Haberman

David Michael Williamson

Amanda Roth

Hongwen Guo

Peter Van Rijn

Jing Chen

Theresa Richter

Andre Alexander Rupp

Michael E Wagner

Eowyn Winchester

Yoav Bergner

Nathan Lederer

Ick Kyu Choi

A method comprising accessing a first data structure that is associated with a first product prepared by a student and that includes first process data associated with a process performed by the student in generating the first product, analyzing the first data structure to generate a first characterization score based on the first product and the first process data, accessing a second data structure that is associated with a second product prepared by the student and that includes second process data associated with a process performed by the student in generating the second product, analyzing the second data structure to generate a second characterization score based on the second product and the second process data, and calculating a skill level change metric based on the first characterization score and the second characterization score indicating a change in ability level of the student over a course of the scenario-based assessment.

Platform for characterizing performance on a scenario-based assessment to school differences in curriculum

Biometric keystroke measure data derived from a computer-implemented long form examination taken by an examinee is received. Features are the extracted from the biometric keystroke measure data for the examinee. A similarity value is then determined, using one or more of a direct distance approach or a machine learning approach, for the extracted features relative to features extracted from biometric keystroke measure data derived from each of a plurality of other examinees while taking the long form examination. At least one of the determined similarity values is then identified having a value above a pre-defined threshold. The pre-defined threshold indicates a likelihood of the examinee being the same as one of the other examinees. Data can then be provided that characterizes the identification. Related apparatus, systems, techniques and articles are also described.

Behavior-based electronic essay assessment fraud detection

Systems and methods for defining and using an optimal burst boundary threshold to assess the reliability of a manual/automatic writing score are presented. Keystroke data, including inter-key interval data, such as inter-word interval data, may be gathered from writings. Clustering analyses may be performed on the inter-key interval data to determine an optimal number of bursts for the writings. An optimal burst boundary may be determined from the optimal number of bursts. Other burst-related measures and statistics, including the average and maximum burst lengths, may be determined from the writings based on the optimal burst boundary threshold. A score may be received for each of the writings. A validation indication metric may be generated for each of the writings based on the received score and the optimal burst boundary threshold. The resulting measures and statistics may be used or applied in different ways and provide personalized feedback as learning analytics.

Defining personalized writing burst measures of translation using keystroke logs

Systems and methods are provided for automatically scoring a response and statistically revaluating whether it can be considered as aberrant. In one embodiment, a constructed response is evaluated via a pre-screening stage and a post-hoc screening stage. The pre-screening stage attempts to determine whether the constructed response is aberrant based on a variety of aberration metrics and criteria. If the constructed response is deemed not to be aberrant, then the post-hoc screening stage attempts to predict a discrepancy between what score an automated scoring system would assigned and what score a human rater would assign to the response. If the discrepancy is sufficiently low, then the constructed response may be scored by an automated scoring engine. On the other hand, if the constructed response failed to pass either of the two stages, then a flag may be raised to indicate that additional human review may be needed.

Systems and methods for treatment of aberrant responses

Systems and methods are provided for providing feedback on a user's writing behavior in generating a constructed response. An electronic process log for the constructed response is received. The electronic process log is processed to generate a vector having a predetermined number of elements. The vector comprises information related to the user's actions in generating the constructed response and includes (i) data indicating types of actions performed by the user in generating the constructed response, (ii) time points associated with the actions, and (iii) locations associated with the actions, each location indicating whether an associated action occurred within a word, between words, within a phrase, between phrases, within a sentence, between sentences, within a paragraph, or between paragraph. The vector is compared to one or more vectors associated with other constructed responses, and feedback is generated based on the comparison.

Generating scores and feedback for writing assessment and instruction using electronic process logs

Systems and methods are provided for scoring a constructed response generated by a user and providing information on the user's writing behavior. A constructed response and associated electronic process log are received. The constructed response is processed to generate first feature values representative of aspects of the constructed response. The electronic process log is processed to generate second feature values related to the user's actions in generating the constructed response. A score for the constructed response is generated using the processing system by applying a computer scoring model to the first and second feature values. A rule of a rule engine that is satisfied is identified, the rule being satisfied when one or more feature values of the second feature values meet a condition associated with the rule. Information on the user's actions in generating the constructed response is provided based on the satisfied rule.

Systems and methods are described for generating a scoring model for responses. A computer-implemented method of calibrating a scoring model using a processing system for scoring examinee responses includes accessing a plurality of training responses for training the scoring model. The plurality of training responses are analyzed to derive values of multiple features (variables) of the training responses. The scoring model is trained based on the values of the multiple features of the training responses and one or more external measures of proficiency for each individual associated with a training response utilized in the training. The one or more external measures are not derived from the training responses. Based on the training, a weight for each of the multiple features is determined. The scoring model is calibrated to include the weights for at least some of the features for scoring examinee responses.

Systems and methods for generating automated evaluation models

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Plainsboro, NJ, United States of America

Mo Zhang