Pearson Education, Inc.

Miles T Loring

Vishal Kapoor

Holly Garner

Peter Foltz

Nicholas A Dronen

Cole Joseph Cecil

Jesse E Peterson

Umamakeswaran Veerasingam

Sanjay Zope

Paul Grudnitski

Erik R Racho

Anne Hong

Keith Jones

Xindeling Pan

Lynn Chang

John P Sadauskas, Jr

John Paul Sadauskas, Jr

Systems and methods for automated aggregated content commenting are disclosed herein. Such a system can include a memory including a content database; an evaluation criteria database; and a comment database. The system can include at least one server. The at least one server can: receive a content identifier; receive a user identifier; provide a representation of the piece of content to the user; receive a comment from the user identifying a portion of the piece of content; generate a portion of the comment database linked to the user and the content; generate an entry in the portion of the comment database linked to the user and the content; and store the comment in the entry in the portion of the comment database.

Systems and methods for automated aggregated content comment generation

A file receiver receives an electronic structure file that includes structure-file data associated with a spatial arrangement and detects a content object for processing that includes content-object data. A file transformation engine transforms the structure-file data from the structure file into an electronic record. A rendering engine renders an image of the transformed structure-file data arranged in the spatial arrangement. An interface engine detects an input corresponding to specification of a position of a data segment. A parsing engine defines a segment-position specification indicative of the position. A template engine generates an electronic template that associates an identifier of the data segment with the segment-position specification and associates the electronic template with a template identifier. A record classifier determines that the content object corresponds to the template identifier. The parsing engine further extracts, using the segment-position specification, a portion of the content-object data that corresponds to the data segment.

Electronic template generation, data extraction and response detection

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

A distributed processing method is disclosed herein for evaluating student work product. 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 an analysis results from the one or several independent processing units, and can update the result based on a value scoring the machine learning of the independent processing unit.

Distributed processing systems

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 an analysis results from the one or several independent processing units, and can update the result based on a value characterizing the machine learning of the independent processing unit.

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North Liberty, IA, United States of America

Miles T Loring