Pearson Education, Inc.

Luis Manuel Oros

David Strong

Scott Hellman

Peter Foltz

Lee Becker

Johann A Larusson

Alok Baikadi

Kyle D Habermehl

Quinn N Lathrop

Mark Rosenstein

William M Murray

Victoria Kortan

Jill Budden

Timothy J Stewart

Stephen Hopkins

Andrew Gorman

Jacob Smith Noble

John Paul Sadauskas, Jr

Alex Nickel

David King

James David Corbin

Kateryna Lapina

Marcia Derr

Jake Noble

Daniel Tonks

Kelly Lynn Paris

Peter Alexander Sabitini

Matthew Sweeten

Kristina Evans

Ronald Itelman

Jacob Noble

Johann Larusson

Thomas Mctavish

Nina Shamsi

Eric Kattwinkel

Jake Nobel

Peter W Folz

Systems and methods for automated custom training of a scoring model are disclosed herein. The method include: receiving a plurality of responses received from a plurality of students in response to providing of a prompt; identifying an evaluation model relevant to the provided prompt, which evaluation model can be a machine learning model trained to output a score relevant to at least portions of a response; generating a training indicator that provides a graphical depiction of the degree to which the identified evaluation model is trained; determining a training status of the model; receiving at least one evaluation input when the model is identified as insufficiently trained; updating training of the evaluation model based on the at least one received evaluation input; and controlling the training indicator to reflect the degree to which the evaluation model is trained subsequent to the updating of the training of the evaluation model.

Systems and methods for automated evaluation model customization

Real time development of auto scoring essay models for custom created prompts

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

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 create independent clauses from the content files and further extract words to create incomplete clauses. The incomplete clauses are transmitted to the user device such that a user can view and respond to the incomplete clauses.

Personalized automatic content aggregation generation

Growing community of inventors

Brooklyn, NY, United States of America

Luis Manuel Oros