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. 24, 2024

Filed:

Jun. 10, 2021
Applicant:

The Hong Kong University of Science and Technology, Hong Kong, CN;

Inventors:

Haotian Li, Hong Kong, CN;

Yong Wang, Hong Kong, CN;

Huan Wei, Hong Kong, CN;

Huamin Qu, Hong Kong, CN;

Attorneys:
Primary Examiner:
Int. Cl.
CPC ...
G09B 7/02 (2006.01); G06N 3/045 (2023.01); G06N 3/047 (2023.01); G06F 17/18 (2006.01); G06F 18/2413 (2023.01); G06N 3/04 (2023.01); G06N 3/08 (2023.01); G06Q 10/04 (2023.01); G06Q 50/20 (2012.01); G09B 7/04 (2006.01);
U.S. Cl.
CPC ...
G09B 7/02 (2013.01); G06N 3/045 (2023.01); G06N 3/047 (2023.01); G06F 17/18 (2013.01); G06F 18/2414 (2023.01); G06N 3/04 (2013.01); G06N 3/08 (2013.01); G06Q 10/04 (2013.01); G06Q 50/205 (2013.01); G09B 7/04 (2013.01);
Abstract

A method for predicting student performance in interactive question pools, comprising: a data processing and feature extraction module for: extracting from historical score data records statistical student features reflecting the students' past performances on the questions; and statistical question features indicating a question popularity of each question and the average students' scores on each question; and extracting from the pointing device movement data records a plurality of interactive edge features representing the characteristics of the students' problem-solving behaviors; a network construction module for: performing an Edge2Node transformation to transform the interactive edge features into one or more interaction nodes; and constructing a heterogeneous student-interaction-question (SIQ) network; and a prediction module for processing the SIQ network using a residual relational graph neural network to predict a student's score on unattempt questions.


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