Pearson Education, Inc.

Ncs Pearson, Inc.

Darrick Jensen

Peter Pascale

Michael Nealis

Wayne Edward Bailey

Andrew Stockinger

Gregory Anderson

Matthew Maloney

Steven C Nordberg

Colleen M Dolan

Jide Fajobi

Ronald D Lancaster, Jr

Vladan Pulec

Joseph Brutsche

Julianne Michael Schaffer

Ronald D Lancaster Jr

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

A system includes one or more server hardware computing devices includes one or more processors and memory storing instructions that, when executed by the one or more processors, cause the system to receive a request to verify an identity of a user attempting to complete an examination provided by an online proctored examination platform to a candidate user authorized to complete the examination and repeatedly, at a predefined interval during the examination, determine, based at least in part on user data collected by the online proctored examination platform during the examination, the user data identifying the user at a time that the interval elapses, whether the user is the candidate user, and, responsive to a determination that the user is not the candidate user, perform one or more of a plurality of actions associated with a failed identity verification, the plurality of actions including stopping the examination.

Security and content protection by continuous identity verification

A system includes a candidate device including a processor configured to implement a candidate evaluation software application and a server computer configured to communicate with the candidate device using a network. The server computer is configured to receive, from the candidate device, a request to access an evaluation service. The request includes a candidate identifier. The server computer is configured to access historical candidate data in a memory accessible by the server computer to determine a trust score for a candidate associated with the candidate identifier, and when the trust score exceeds a threshold, transmit a credential to the candidate device enabling an evaluation of the candidate to be performed on the candidate device via a secured local area network.

Proctor test environment with user devices

A system includes one or more server hardware computing devices communicatively coupled to a network. The one or more server hardware computing devices include one or more processors and memory storing specific computer-executable instructions that, when executed by the one or more processors, cause the system to receive a first request to calculate a trust score for a user associated with a user account of an online proctored examination platform. The trust score represents results of a risk analysis of the user. The instructions cause the system to obtain internal data of the online proctored examination platform, the internal data describing usage of the online proctored examination platform by the user, perform the risk analysis using at least the internal data to produce the trust score, associate the trust score with the user account, and perform an action based on the trust score.

Security and content protection using candidate trust score

A system includes a candidate client device including a processor, a camera, and a microphone. The system includes one or more server hardware computing devices communicatively coupled to a network and in communication with the candidate client device. Each of the one or more server hardware computing devices includes at least one processor executing specific computer-executable instructions within a memory that, when executed, cause the one or more server hardware computing devices to receive the sequence of images and the audio data from the candidate client device, extract a first video attribute from the sequence of images, extract a first audio attribute from the audio data, determine that at least one of the first video attribute violates a first video condition and the first audio attribute violates a first audio condition, and prevent the candidate client device from performing a candidate evaluation function using candidate evaluation software application.

Security and content protection by test environment analysis

A system includes a client device and a server computer coupled to a network and in communication, via the network, with the client device. The server computer is configured to receive, from the client device, a queue request. The queue request includes a candidate identifier. The server computer is configured to determine a risk factor for a candidate associated with the candidate identifier, and, upon determining that the risk factor indicates a high probability of inappropriate behavior during evaluation by the candidate by comparing the risk factor to a risk threshold encode into a first array defining a first candidate queue a reference to the candidate identifier, identify, in a first administrative queue, a first administrator identifier, the first administrative queue being designated as an administration escalation queue, and establish at least one of a first video connection and a first audio data connection between the client device and a first administrator device associated with the first administrator identifier.

Automated registration and greeting process&#x2014;custom queueing (security)

A system includes a client device including a processor configured to implement a candidate evaluation software application and a server computer in communication, via a network, with the client device. The server computer is configured to receive, from the client device, a queue request. The queue request includes a candidate identifier. The server computer includes determine, using the candidate identifier, a candidate target attribute, identify a first candidate queue from a plurality of candidate queues using the candidate target attribute, and encode into the first candidate queue a reference to the candidate identifier. The server computer is configured to identify, in a first administrative queue, an administrator identifier, the first administrative queue being associated with the candidate target attribute, and establish at least one of a video and an audio data connection between the client device and an administrator device associated with the administrator identifier.

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Minneapolis, MN, United States of America

Darrick Jensen