The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Sep. 13, 2022

Filed:

Feb. 10, 2020
Applicant:

Pearson Education, Inc., Bloomington, MN (US);

Inventors:

Mark E. Liedtke, Castle Rock, CO (US);

Sumona J. Routh, Arvada, CO (US);

Clayton Tong, Parker, CO (US);

Daniel L. Ensign, Fort Lupton, CO (US);

Victoria Kortan, Centennial, CO (US);

Srirama Kolla, Highlands Ranch, CO (US);

Assignee:

PEARSON EDUCATION, INC., Bloomington, IL (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G09B 7/00 (2006.01); G06N 20/00 (2019.01); G06N 5/04 (2006.01); G06N 20/20 (2019.01); G06F 16/14 (2019.01); G06K 9/62 (2022.01); G09B 7/02 (2006.01); G09B 7/06 (2006.01);
U.S. Cl.
CPC ...
G09B 7/00 (2013.01); G06F 16/156 (2019.01); G06K 9/6226 (2013.01); G06N 5/04 (2013.01); G06N 20/00 (2019.01); G06N 20/20 (2019.01); G09B 7/02 (2013.01); G09B 7/06 (2013.01);
Abstract

Systems and methods are provided by which an adaptive learning engine may be executed to determine the probability that a given user will respond correctly to a given assessment item of a digital assessment on their first attempt. The adaptive learning engine may apply one or more machine learning models to feature data corresponding to the user and the assessment item in order to determine the probability. The feature data may be calculated periodically and/or in real time or near-real time according to a machine learning model definition based on assessment data corresponding to the user's activity and/or based on responses submitted globally by users to the assessment item and/or to content related to the assessment item. Based on the correct first attempt probability, the adaptive learning engine may identify and recommend assessment items for which a user should be preemptively assigned credit.


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