Pearson Education, Inc.

Mark Potter

Brian Moriarty

David Strong

Sean York

Tim Stewart

Angela McAllister

Kimberly Runyon

Laura Pionek

Victoria Kortan

Daniel L Ensign

Chadwick Reimers

Alison Doucette

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 content aggregation creation are disclosed herein. The system can include memory having a content database and an aggregation database. The system can include a user device having a first network interface and a first I/O subsystem. The system can include a server that can: provide content to the user device via a first electrical signal; receive a selection of a portion of the provided content from the user device via a second electrical signal; automatically extract sentences from the selected portion of the provided content via a natural language processor; automatically generate a parse tree for one of the automatically extracted sentences; identify noun phrases from the part of speech tags within the parse tree; place content associated with one of the noun phrase in a content aggregation; and output the content aggregation to the user device.

System and method for decay-based content provisioning

Systems and methods for determining mastery in a Bayesian network are disclosed herein. The system can include memory including a content library database containing content for delivery to a user. The system can include at least one processor that can receive an assertion from a user device and identify one or several nodes relevant to the received assertion. The at least one processor can further evaluate the assertion and calculate a node mastery probability for the identified one or several relevant nodes. The at least one processor can calculate mastery of related nodes and determine mastery of an objective based on the mastery of the relevant nodes and the related nodes. The at least one processor can generate a mastery bar and update the mastery bar with the determined mastery of the objective.

Systems and methods for automated bayesian-network based mastery determination

Systems and methods for content selection with first and second recommendation engines are disclosed herein. The system can include a memory include a content library database and a model database. The system can include a user device having a first network interface and a first I/O subsystem. The system can include one or more servers that can include a packet selection system and a presentation system. These one or more servers can: receive response data from the user device; provide received response data to a first recommendation engine; alert a second recommendation engine when a selected next node is a placeholder node; retrieve at least one statistical model relevant to selection of next node content; and select next node content based on an output of the at least one statistical model.

Systems and methods for automatic individual misconception remediation

Systems and methods for content provisioning are disclosed herein. The system can include memory having a content database, a task database, and a user profile database. The system can include a user device having a first network interface and a first I/O subsystem. The system can include a server that can: receive a user identifier from the user device; retrieve user information from the user profile database, which user information identifies one or several attributes of the user; retrieve user task data from the task database, which user task data identifies a plurality of tasks for completion by the user; automatically generate prioritization data for the plurality of tasks identified by the user task data; select a task based on the prioritization data; and send content relating to the selected task to the user device.

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Pittsburgh, PA, United States of America

Mark Potter