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公开(公告)号：US20200005157A1
公开(公告)日：2020-01-02
申请号：US16544745
申请日：2019-08-19
Applicant: Pearson Education, Inc.
Inventor： Mark Rosenstein , Kyle Habermehl , Scott Hellman , Alok Baikadi , Peter Foltz , Lee Becker , Luis M. Oros , Jill Budden , Marcia Derr
Abstract: 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.
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公开(公告)号：US20190026357A1
公开(公告)日：2019-01-24
申请号：US16024561
申请日：2018-06-29
Applicant: Pearson Education, Inc.
Inventor： David Strong , Scott Hellman , Johann Larusson , Jake Noble , Timothy J. Stewart , Alex Nickel , Luis Oros , Quinn Lathrop , Daniel Tonks , Peter Foltz
Abstract: 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.
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公开(公告)号：US11817014B2
公开(公告)日：2023-11-14
申请号：US16281033
申请日：2019-02-20
Applicant: Pearson Education, Inc.
Inventor： Lee Becker , William Murray , Peter Foltz , Mark Rosenstein , Alok Baikadi , Scott Hellman , Kyle Habermehl , Jill Budden , Stephen Hopkins , Andrew Gorman
IPC: G06F17/00 , G09B7/02 , G06F16/904 , G06N20/00 , G06F9/451 , G06F16/40 , G06F3/0481 , G09B7/06 , G06F40/30 , G06F40/205 , G06F18/214 , G06F18/21
CPC classification number: G09B7/02 , G06F3/0481 , G06F9/451 , G06F16/40 , G06F16/904 , G06F18/214 , G06F18/217 , G06F18/2148 , G06F18/2178 , G06F40/205 , G06F40/30 , G06N20/00 , G09B7/06
Abstract: 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.
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公开(公告)号：US11475245B2
公开(公告)日：2022-10-18
申请号：US16280639
申请日：2019-02-20
Applicant: Pearson Education, Inc.
Inventor： Peter Foltz , Mark Rosenstein , Alok Baikadi , Lee Becker , Stephen Hopkins , Jill Budden , Luis M. Oros , Kyle Habermehl , Scott Hellman , William Murray , Andrew Gorman
IPC: G06K9/62 , G06F16/904 , G06N20/00 , G06F9/451 , G06F16/40 , G06F3/0481 , G09B7/02 , G09B7/06 , G06F40/30 , G06F40/205
Abstract: 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.
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5.发明授权
公开(公告)号：US11443140B2
公开(公告)日：2022-09-13
申请号：US16280984
申请日：2019-02-20
Applicant: Pearson Education, Inc.
Inventor： Scott Hellman , Lee Becker , Samuel Downs , Alok Baikadi , William Murray , Kyle Habermehl , Peter Foltz , Mark Rosenstein
IPC: G06F17/00 , G06K9/62 , G06F16/904 , G06N20/00 , G06F9/451 , G06F16/40 , G06F3/0481 , G09B7/02 , G09B7/06 , G06F40/30 , G06F40/205
Abstract: 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.
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公开(公告)号：US11741849B2
公开(公告)日：2023-08-29
申请号：US16281048
申请日：2019-02-20
Applicant: Pearson Education, Inc.
Inventor： Scott Hellman , William Murray , Kyle Habermehl , Alok Baikadi , Jill Budden , Andrew Gorman , Mark Rosenstein , Lee Becker , Stephen Hopkins , Peter Foltz
IPC: G09B7/02 , G06F16/904 , G06N20/00 , G06F9/451 , G06F16/40 , G06F3/0481 , G09B7/06 , G06F40/30 , G06F40/205 , G06F18/214 , G06F18/21
CPC classification number: G09B7/02 , G06F3/0481 , G06F9/451 , G06F16/40 , G06F16/904 , G06F18/214 , G06F18/217 , G06F18/2148 , G06F18/2178 , G06F40/205 , G06F40/30 , G06N20/00 , G09B7/06
Abstract: 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.
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公开(公告)号：US20190258716A1
公开(公告)日：2019-08-22
申请号：US16281033
申请日：2019-02-20
Applicant: Pearson Education, Inc.
Inventor： Lee Becker , William Murray , Peter Foltz , Mark Rosenstein , Alok Baikadi , Scott Hellman , Kyle Habermehl , Jill Budden , Stephen Hopkins , Andrew Gorman
Abstract: 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.
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8.发明申请
公开(公告)号：US20190258715A1
公开(公告)日：2019-08-22
申请号：US16280984
申请日：2019-02-20
Applicant: Pearson Education, Inc.
Inventor： Scott Hellman , Lee Becker , Samuel Downs , Alok Baikadi , William Murray , Kyle Habermehl , Peter Foltz , Mark Rosenstein
Abstract: 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.
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公开(公告)号：US11068043B2
公开(公告)日：2021-07-20
申请号：US16024561
申请日：2018-06-29
Applicant: Pearson Education, Inc.
Inventor： David Strong , Scott Hellman , Johann Larusson , Jake Noble , Timothy J. Stewart , Alex Nickel , Luis Oros , Quinn Lathrop , Daniel Tonks , Peter Foltz
IPC: G06F3/01 , G06F16/955 , G06F3/0346 , G06F9/451 , G06F16/28 , G06N5/02 , G06N20/00 , G10L15/18 , G10L15/22 , G10L15/30
Abstract: 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.
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公开(公告)号：US20190259293A1
公开(公告)日：2019-08-22
申请号：US16281048
申请日：2019-02-20
Applicant: Pearson Education, Inc.
Inventor： Scott Hellman , William Murray , Kyle Habermehl , Alok Baikadi , Jill Budden , Andrew Gorman , Mark Rosenstein , Lee Becker , Stephen Hopkins , Peter Foltz
Abstract: 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.
