The Hong Kong University of Science and Technology

Huan Wei

Huamin Qu

Yong Wang

Haotian Li

Min Xu

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.

Peer-inspired student performance prediction in interactive online question pools with graph neural network

A system for proctoring online exams includes a client-side computing system and a visual analytics system. The client-side computing system includes a camera configured to obtain video data corresponding to a user while taking an exam and one or more input devices configured to obtain interaction data, wherein the interaction data includes mouse movements of the user while taking the exam. The visual analytics system is configured to obtain the video data and the interaction data from the client-side computing system, analyze the exam data to detect abnormal behavior by the user based at least in part on mouse movement data, and generate one or more visualizations of the analyzed exam data to be used in determining whether or not the user has cheated during the exam.

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Hong Kong, China

Huan Wei