Abstract: Systems and methods are provided for implementing a parallel Expectation Minimization algorithm for generalized latent variable models. Item response data that is based on responses to items from multiple respondents is accessed. The item response data includes data for multiple response variables. The item response data is analyzed using a generalized latent variable model, and the analysis includes an application of a Parallel-E Parallel-M (PEPM) algorithm. In a parallel Expectation step of the PEPM algorithm, the respondents are subdivided into N groups of respondents, and computations for the N groups are performed in parallel using the N processor cores. In a parallel Maximization step of the PEPM algorithm, the response variables are subdivided into N groups of response variables, and computations for the N groups of response variables are performed in parallel using the N processor cores.

Abstract: Systems and methods are provided for scoring a response to a character-by-character highlighting task. A similarity value for the response is calculated by comparing the response to one or more correct responses to the task to determine the similarity or dissimilarity of the response to the one or more correct responses to the task. A threshold similarity value is calculated for the task, where the threshold similarity value is indicative of an amount of similarity or dissimilarity to the one or more correct responses required for the response to be scored at a certain level. The similarity value for the response is compared to the threshold similarity value. A score is assigned at, above, or below the certain level based on the comparison.

Abstract: Disclosed herein is a method of analyzing examinee item response data comprising constructing a diagnosis model for reporting skill profiles of examinees, wherein the diagnosis model comprises at least a variable representing unobserved subpopulations, creating an item design matrix, distributing examinees across the unobserved subpopulations, iteratively estimating values for a plurality of variables within the diagnosis model, and reporting the estimated values to a user.

Abstract: Systems and methods are provided for scoring a response to a character-by-character highlighting task. A similarity value for the response is calculated by comparing the response to one or more correct responses to the task to determine the similarity or dissimilarity of the response to the one or more correct responses to the task. A threshold similarity value is calculated for the task, where the threshold similarity value is indicative of an amount of similarity or dissimilarity to the one or more correct responses required for the response to be scored at a certain level. The similarity value for the response is compared to the threshold similarity value. A score is assigned at, above, or below the certain level based on the comparison.

Abstract: Disclosed herein is a method of analyzing large scale survey results comprising obtaining a sparse data set representing a subset of an original data set comprising a plurality of individuals' responses to a plurality of questions, wherein the sparse data set comprises less than ninety percent of the responses in the original data set; analyzing the sparse data set using a general diagnostic model; and obtaining estimated person parameters using the general diagnostic model.

Abstract: Disclosed herein is a method of analyzing examinee item response data comprising constructing a diagnosis model for reporting skill profiles of examinees, wherein the diagnosis model comprises at least a variable representing unobserved subpopulations, creating an item design matrix, distributing examinees across the unobserved subpopulations, iteratively estimating values for a plurality of variables within the diagnosis model, and reporting the estimated values to a user.