The invention discloses an
online learning cognitive anomaly student detection method and
system based on multi-
modal representation, and belongs to the field of
anomaly detection. The method comprises the following steps: firstly, acquiring multi-
modal data in a student learning process, and constructing a student
relation graph; then inputting the multi-
modal data of each student into a multi-modal learning representation module pre-trained by an auxiliary classification task to obtain multi-modal embedding corresponding to each student; finally, the multi-mode embedding and student
relation graph is input into a cognitive anomaly student detection module which is trained through a comparative learning framework in advance, and cognitive anomaly scores of students corresponding to the target nodes are obtained.According to the method, student cognitive information can be automatically extracted, heavy work of manual
processing is avoided, and the learning efficiency is improved. And meanwhile, cognitive anomaly student detection is realized based on multi-view multi-strategy comparison, the
cognitive level of students on the platform can be detected on the basis of considering multiple influence factors, and the
online learning effect is assisted to be improved.