Act, Inc.

Richard D Meisner

Bradley Bolender

Daniel Shaw

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.

System and methodology for computer-facilitated development of reading comprehension test items through passage mapping

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.

Automatic item generation for passage-based assessment

A computer implemented method for evaluating examination responses includes receiving, with an examination evaluation server, an examination response, tokenizing the examination response into one or more n-word segments, extracting, from the response, a set of construct-relevant features indicating one or more scoring metrics, generating a set of scoring rules, and generating a score estimate as a function of one or more scoring rules from the set of scoring rules and one or more of the scoring metrics.

Application of high-dimensional linguistic and semantic feature vectors in automated scoring of examination responses

A computer implemented method for identifying and removing similar examination items in a digital examination includes obtaining, with an examination evaluation server, a digital examination with a plurality of examination items, tokenizing a first examination item of the plurality of examination items into a first set of n-grams, tokenizing a second examination item of the plurality of examination items into a second set of n-grams, generating a first item vector from the first set of n-grams, generating a second item vector from the second set of n-grams, generating multiple item congruence values for corresponding similarity dimensions by comparing the first item vector with the second item vector, and identifying that the first and second examination items are a potential enemy item pair if one or more of the item congruence values exceed corresponding threshold values.

Automated determination of degree of item similarity in the generation of digitized examinations

A computer implemented method of examination scoring includes receiving, with an examination evaluation server, a language corpus with a plurality of corpus words, deriving, for each corpus word, a corpus sematic vector within a multi-dimensional semantic vector space, receiving first and second examination responses, tokenizing each examination response into a plurality of response words, identifying a set of matching response words from the plurality of response words from each examination response, identifying a set of semantic vectors corresponding to the matching response words for each examination response, and comparing the semantic vectors from the first examination response with the semantic vectors from the second examination response to derive a response semantic similarity value for use as an input for automated scoring.

Application of semantic vectors in automated scoring of examination responses

A computer implemented method for detecting linguistic uniqueness includes receiving, with an examination evaluation server, a list of word n-grams and corresponding frequency values indicating the frequency at which each word n-gram appears in a language corpus, receiving an examination response with one or more sentences, tokenizing the sentences into a plurality of words or phrases, combining contiguous words or phrases into n-word segments, and identifying, for each n-word segment that matches a matching word n-gram from the list, the frequency value corresponding to the matching word n-gram.

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Iowa City, IA, United States of America

Richard D Meisner