Ncs Pearson, Inc.

Pearson Education, Inc.

Vladan Pulec

Michael Nealis

Peter Pascale

Darrick Jensen

Joseph Brutsche

Corey Hoesley

Nick Wilson

Systems and methods may involve processing of entity data by machine learning models to produce one or more entity and/aggregate risk scores and/or aggregate anticipated risk scores, which may be compared to one or more thresholds to determine when one or more predefined actions should be taken. The entity data may be collected for various entities related to an exam registration and delivery process, which may include a candidate, an exam, a test center, an exam registration event, a proctor, and an exam delivery event. The exam registration and delivery process may include multiple states—each being associated with a different set of entities. Aggregate risk scores for a given state may be calculated using only entity data for the set of entities associated with that state. The predetermined actions taken may also be dependent on the current state.

Recommendation engine for testing conditions based on evaluation of test entity scores

Evaluation of a registration process

Systems and methods may involve processing of entity data by nested machine learning models to produce one or more aggregate risk scores, which may be compared to one or more thresholds to determine when one or more predefined actions should be taken. The entity data may be collected for various entities related to an exam registration and delivery process, which may include a candidate, an exam, a test center, an exam registration event, a proctor, and an exam delivery event. Entity data for each entity may be separately processed by entity-specific machine learning models to generate intermediate entity risk scores. The intermediate entity risk scores may be input to an aggregate machine learning model, which may output an aggregate risk score. A resource management server may cause the predefined actions to be taken after comparing the aggregate risk score to the one or more thresholds.

Systems and methods for risk analysis and mitigation with nested machine learning models for exam registration and delivery processes

Systems and methods may involve processing of entity data by machine learning models to produce one or more aggregate risk scores, which may be compared to one or more thresholds to determine when one or more predefined actions should be taken. The entity data may be collected for various entities related to an exam registration and delivery process, which may include a candidate, an exam, a test center, an exam registration event, a proctor, and an exam delivery event. The exam registration and delivery process may include multiple states—each being associated with a different set of entities. Aggregate risk scores for a given state may be calculated using only entity data for the set of entities associated with that state. The predetermined actions taken may also be dependent on the current state.

Systems and methods for state-based risk analysis and mitigation for exam registration and delivery processes

Systems and methods for secure content delivery are disclosed herein. The system can include a content driver communicatingly connected with a user device via a layered protocol model or via a User Datagram Protocol (UDP). The content driver can generate a signal directing the creation of a secured partition on a bootable media device connected to a user device, identify content for delivery, and generate pixel data for the content. The content driver can send the pixel data to the user device, wherein the user device is configured to store the pixel data in the secured partition of the bootable media device. The content driver can receive a plurality of response inputs from the user device, wherein the response inputs are generated by a software application running on the bootable media device, generate a response based on the received response inputs, and provide the generated response to an evaluation module.

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Shakopee, MN, United States of America

Vladan Pulec