US 12,283,198 B2
Systems and methods for automatically revising feedback in electronic learning systems
Brian Cepuran, Kitchener (CA); Jeremy Auger, Kitchener (CA); and John Baker, Kitchener (CA)
Assigned to D2L Corporation, Kitchener (CA)
Filed by D2L Corporation, Kitchener (CA)
Filed on Apr. 13, 2022, as Appl. No. 17/719,665.
Claims priority of provisional application 63/174,089, filed on Apr. 13, 2021.
Prior Publication US 2022/0327947 A1, Oct. 13, 2022
Int. Cl. G09B 7/02 (2006.01); G06F 40/166 (2020.01); G06F 40/253 (2020.01); G06F 40/279 (2020.01); G06F 40/30 (2020.01); G06N 20/00 (2019.01)
CPC G09B 7/02 (2013.01) [G06F 40/166 (2020.01); G06F 40/253 (2020.01); G06F 40/279 (2020.01); G06F 40/30 (2020.01); G06N 20/00 (2019.01)] 20 Claims
OG exemplary drawing
 
1. A method for automatically revising feedback in an electronic learning system, the method comprising operating at least one processor to:
receive, from a computing device associated with a reviewer, initial feedback text data submitted by the reviewer to evaluate electronic input submitted by a learner, the reviewer and the learner being users of the electronic learning system, the electronic input comprising an evaluative submission;
divide the initial feedback text data submitted by the reviewer into a plurality of portions;
detect a sentiment associated with each portion of the initial feedback text data submitted by the reviewer;
identify a plurality of sentiment groups in the initial feedback text data submitted by the reviewer, each sentiment group consisting of one or more portions of the initial feedback text data associated with a common sentiment;
select at least one feedback processing module based on the one or more portions of the initial feedback text data corresponding to the sentiment group, the at least one feedback processing module comprising at least one machine-learned model;
for each sentiment group, apply the at least one machine-learned model of the selected at least one feedback processing module to the corresponding one or more portions of the initial feedback text data submitted by the reviewer to determine at least one suggested revision for the portion of the initial feedback text data submitted by the reviewer; and
generate revised feedback text data indicating each suggested revision for the initial feedback text data submitted by the reviewer.