Pearson Education, Inc.

Peter Foltz

Scott Hellman

Lee Becker

Kyle D Habermehl

Alok Baikadi

Mark Rosenstein

Vishal Kapoor

Miles T Loring

Holly Garner

Jill Budden

Nicholas A Dronen

William M Murray

Stephen Hopkins

Luis Manuel Oros

Andrew Gorman

David Strong

Johann A Larusson

Quinn N Lathrop

Timothy J Stewart

Jian Cheng

Alex Nickel

Jake Noble

Samuel Downs

Marcia Derr

Daniel Tonks

Lakshmi Ramachandran

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 machine learning model training quality control

Systems and methods for interface-based automated custom authored prompt evaluation

Systems and methods for interface-based machine learning model output customization

Systems and methods for automated evaluation model customization

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

Systems and methods for automated machine learning model training for a custom authored prompt

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

A distributed processing system is disclosed herein. The distributed processing system includes a server, a database server, and an application server that are interconnected via a network, and connected via the network to a plurality of independent processing units. The independent processing units can include an analysis engine that is machine-learning-capable, and thus uniquely completes its processing tasks. The server can provide one or several pieces of data to one or several of the independent processing units, can receive analysis results from these one or several independent processing units, and can update the result based on a value characterizing the machine learning of the independent processing unit.

Resource allocation in distributed processing systems

Systems and methods for alerting a third party device according to a result of automated evaluation of a received response packet according to at least one pattern in the data packet are disclosed herein. The system can include a memory having a pattern database and a model database. The system can further include a user device and a content management server. The content management server can receive a response packet and identify one or several patterns within the response packet. Based on the presence or absence of these one or several patterns in the response packet, the content management server can generate an evaluation of the response packet and generate and send an alert with the results of the evaluation.

System and method for automated pattern based alert generation

Growing community of inventors

Boulder, CO, United States of America

Peter Foltz