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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：US11449762B2
公开(公告)日：2022-09-20
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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