Parallel computing for data analysis using generalized latent variable models

A system comprising:

• a processing system including N processor cores, N being greater than one;
• and
• one or more non-transitory computer-readable storage mediums in communication with the processing system and encoded with instructions for commanding the processing system to execute steps comprising:
• accessing response data that is based on responses to test questions from multiple test-takers, the response data including data for multiple response variables;
• and
• analyzing the response data using a generalized latent variable model, the analysis including an application of a parallel Expectation parallel Maximization (PEPM) algorithm,
• wherein a parallel Expectation step of the PEPM algorithm includes:
• subdividing the test-takers into N groups of test-takers, and
• performing computations for the N groups in parallel using the N processor cores, data for each of the N groups being processed by a respective core of the N processor cores, wherein the computations include, for each of the multiple test-takers, calculating the posterior of the latent variable given responses;
• and
• wherein a parallel Maximization step of the PEPM algorithm includes:
• subdividing the response variables into N groups of response variables, and
• performing computations for the N groups of response variables in parallel using the N processor cores, data for each of the N groups being processed by a respective core of the N processor cores, wherein the computations include, for each response variable of the multiple response variables, updating parameters associated with the response variable,
• wherein results of the PEPM algorithm are utilized to provide a score to a new response received from a new test-taker, the score estimating a skill of the new test-taker.

A non-transitory computer-readable storage medium comprising computer executable instructions which, when executed, cause a processing system including N processor cores to execute steps comprising:

• accessing response data that is based on responses to test questions from multiple test-takers, the response data including data for multiple response variables;
• and
• analyzing the response data using a generalized latent variable model, the analysis including an application of a parallel Expectation parallel Maximization (PEPM) algorithm,
• wherein a parallel Expectation step of the PEPM algorithm includes:
• subdividing the test-takers into N groups of test-takers, N being greater than one, and
• performing computations for the N groups in parallel using the N processor cores, data for each of the N groups being processed by a respective core of the N processor cores, wherein the computations include, for each of the multiple test-takers, calculating the posterior of the latent variable given responses;
• and
• wherein a parallel Maximization step of the PEPM algorithm includes:
• subdividing the response variables into N groups of response variables, and
• performing computations for the N groups of response variables in parallel using the N processor cores, data for each of the N groups being processed by a respective core of the N processor cores, wherein the computations include, for each response variable of the multiple response variables, updating parameters associated with the response variable,
• wherein results of the PEPM algorithm are utilized to provide a score to a new response received from a new test-taker, the score estimating a skill of the new test-taker.