Pearson Education, Inc.

Timothy J Stewart

David Strong

Johann A Larusson

Quinn N Lathrop

Luis Manuel Oros

Alex Nickel

Scott Hellman

Peter Foltz

Jacob Smith Noble

Todd Fredrich

Matthew Sweeten

James David Corbin

Kristina Evans

Daniel Tonks

Jake Noble

Johann Larusson

David W Strong

Jake Nobel

Peter W Folz

Systems and methods for virtual reality interaction evaluation are disclosed herein. The system can include a memory including: an interaction sub-database containing information relating to user interactions with at least one virtual asset in a virtual environment, and a content library database containing a plurality of virtual assets and information relating to those virtual assets. The system can include at least one server that can determine user engagement with at least one of the plurality of virtual assets, receive data indicative of an interaction with at least one of the plurality of virtual assets, and determine an interaction type of the interaction associated with the received data. The server can perform a speech capture and analysis process, perform a manipulation process, generate an evaluation of the user interactions with the at least one of the plurality of virtual assets, and deliver the generated evaluation.

Systems and methods for virtual reality-based grouping evaluation

Systems and methods for automated objective identification are disclosed herein. The system can include a user device and a memory. The memory can include a filed database including field data associated with previously identified objectives. The memory can include a scoring database containing a machine-learning scoring algorithm. The system can include at least one server that can receive an identifier of a content portion, extract field information identifying at least one attribute of the identified content portion from the content portion, input extracted field information into the machine-learning scoring algorithm; receive identification of objectives forming a set of objectives from the machine-learning scoring algorithm; sort the objectives forming the set of objectives according to scores generated for each of the objectives in the set of objectives; and output the sorted objectives to the user device.

System and methods for automatic machine-learning based objective recommendation

Systems and methods for automatic generating of a Bayes net content graph are disclosed herein. The system can include a memory including a mapping matrix. The system can include at least one server. The at least one server can generate a user matrix having n columns and p rows. In some aspects, each of the n columns is associated with a student and each of the p rows is associated with a content item. The at least one server can: store the user matrix in the memory; retrieve the mapping matrix from the memory; iteratively identify prerequisite relationships between the skills identified in the user matrix; generate edges between the skills in the user matrix based on the iteratively identified prerequisite relationships; and orient the edges between the skill.

Platform-agnostic Bayes net content aggregation system and method

Generating personalized aggregated content is disclosed herein. The system can include a memory include an aggregated content 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 content files from the user device. A server can parse the content files and further generate features and feature vectors based on a related domain model. Content from the parsed content files may then be used to generate cards or content aggregations.

Personalized automatic content aggregation generation

Growing community of inventors

Aurora, CO, United States of America

Timothy J Stewart