Abstract: The present disclosure provides systems and methods for providing interface rendering of passages for test item generation. Methods and systems can utilize natural language processing (NLP) and machine learning (ML) models to analyze passages that were provided in historical testing environments (e.g., first passage), determine one or more similar search strings that are also found in a current passage (e.g., second passage), and provide the first and second passages to a user interface of a user device. On the display, the search string in the historical and current passages may be highlighted at the user interface to identify the similarities.

Abstract: A computer-implemented method for automatic item generation in an digital examination that includes obtaining a first string of text, the first string of text being organized based on a linguistic context, selecting a first textual term from the first string of text, identifying a first set of linguistic characteristics corresponding to the first textual term, obtaining a second textual term exhibiting at least one linguistic characteristic that is different from any linguistic characteristic of the first set of linguistic characteristics, and generating a second string of text by replacing the first textual term in the first string of text with the second textual term.

Abstract: A comprehensive assessment is generated from sources of information. A base stimulus on a broad topic is received, wherein the base stimulus consists of components in the sources of information. Shorter stimuli are generated by selecting components from the base stimulus. The base stimulus may contain content for any type of subject, wherein the content is any combination of text, numbers, tables, charts, figures, audio clips, video clips, etc. The components for each sub-stimulus can be selected and rearranged based on examinee's performance and ability.

Abstract: Systems and methods for enhanced monitoring of learning progressions include obtaining a first set of examinations and a first set of responses corresponding to the first set of examinations, a first set of examination assessments, training a machine-learning multidimensional scoring model based on the first set of examinations, the first set of responses, and the first set of examination assessments, generating a confusion matrix based on the first set of examination assessments, determining a performance assessment value from the confusion matrix, and determining that the multidimensional scoring model has been sufficiently trained if the performance assessment value meets or exceeds a selected threshold value.

Abstract: A computer-implemented method for automatic item generation in an digital examination that includes obtaining a first string of text, the first string of text being organized based on a linguistic context, selecting a first textual term from the first string of text, identifying a first set of linguistic characteristics corresponding to the first textual term, obtaining a second textual term exhibiting at least one linguistic characteristic that is different from any linguistic characteristic of the first set of linguistic characteristics, and generating a second string of text by replacing the first textual term in the first string of text with the second textual term.