Pearson Education, Inc.

Daniel L Ensign

Victoria Kortan

Srirama Kolla

Sumona J Routh

Clayton Tong

Mark E Liedtke

Jacob Anderson

Mark Potter

Brian Moriarty

Chadwick Reimers

Zachary S Elewitz

Alison Doucette

Systems and methods are provided by which a machine learning model may be executed to determine the probability that a given user will respond correctly to a given assessment item of a digital assessment on their first attempt. The machine learning model may process feature data corresponding to the user and the assessment item in order to determine the probability. The feature data may be calculated periodically and/or in real time or near-real time according to a machine learning model definition based on assessment data corresponding to the user's activity and/or based on responses submitted by all users to the assessment item and/or to content related to the assessment item.

Systems and methods for predictive modelling of digital assessment performance

Systems and methods are described which relate to machine learning model validation. A first machine learning model may be trained to dependent variable data for a first population. A second machine learning model may be trained to simulate dependent variable data for the first population. The second machine learning model may then be applied to student activity data of a second population having different characteristics from the first population to produce simulated dependent variable data. The first machine learning model may then generate predictions for the second population, which may be validated via comparison to the simulated dependent variable data. A given simulated dependent variable value may be generated by the second machine learning model at a specific time T, where some features input to the machine learning model may be derived from datapoints occurring before Tand others being derived from datapoints occurring after T.

Systems and methods for validation of artificial intelligence models

Systems and methods are provided by which an adaptive learning engine may be executed to determine the probability that a given user will respond correctly to a given assessment item of a digital assessment on their first attempt. The adaptive learning engine may apply one or more machine learning models to feature data corresponding to the user and the assessment item in order to determine the probability. The feature data may be calculated periodically and/or in real time or near-real time according to a machine learning model definition based on assessment data corresponding to the user's activity and/or based on responses submitted globally by users to the assessment item and/or to content related to the assessment item. Based on the correct first attempt probability, the adaptive learning engine may identify and recommend assessment items for which a user should be preemptively assigned credit.

Systems and methods for assessment item credit assignment based on predictive modelling

Systems and methods for content provisioning are disclosed herein. The method includes receiving content corresponding to at least one source document, parsing the content, identifying segments from the parsed content, generating a networked grouping of the segments, receiving historical user information about a plurality of users, training a model by using the historical user information, receiving activities of a user, parsing the activities of the user, identifying components from the parsed activities, correlating the components with the segments, extracting features from the activities of the user based on the correlation, and using the trained model to estimate a mastery level of the user based on the features.

Personalized content distribution

Systems and methods for automated data packet selection and delivery are disclosed herein. The system can include a memory containing a data packet database including data packets for delivery to a user; and a user profile database including information identifying at least one user. The system can include a user device and one or several servers. The one or several servers can: receive a request for delivery of a set of data packets to a user via the user device; identify potential data packets for delivery to the user via the user device; determine a probability of the user providing a desired response to each of the potential data packets; weight the data packets according to weighting data and the determined probability; select a set of data packets from the potential data packets; and provide the set of data packets to the user via the user device.

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Fort Lupton, CO, United States of America

Daniel L Ensign