公开(公告)号：US20240355218A1

公开(公告)日：2024-10-24

申请号：US18334371

申请日：2023-06-13

Applicant: CENTRAL CHINA NORMAL UNIVERSITY

Inventor： Zongkai YANG ,  Lili Liu ,  Sannyuya LIU ,  Jingying Chen ,  Ceyu Deng ,  Jiaer Chen ,  Yutao Ling ,  Shulan Jin ,  Shengming WANG

IPC: G09B7/00

CPC classification number: G09B7/00

Abstract: Disclosed are training method and system for autism language barrier based on adaptive learning scaffold, and the method includes the following steps: analyzing and assessing the state of the user before training to obtain an analysis result, and generating an initialized training path based on the analysis result; obtaining training question information, predicting a question-answering correct rate of the user based on user information and training question information, constructing a proximal development zone, and adding training questions that meet the accuracy requirements to the proximal development zone; updating the initialized training path, classifying the training questions in the proximal development zone, adding the classified training questions to the main training task or branch training task, and the user will perform learning and training according to the training task. The disclosure may recommend a suitable training path, and formulate training tasks that are suitable for the user's ability level.

公开(公告)号：US10916158B2

公开(公告)日：2021-02-09

申请号：US16697205

申请日：2019-11-27

Applicant: CENTRAL CHINA NORMAL UNIVERSITY

Inventor： Zongkai Yang ,  Jingying Chen ,  Sannvya Liu ,  Ruyi Xu ,  Kun Zhang ,  Leyuan Liu ,  Shixin Peng ,  Zhicheng Dai

IPC: G09B19/00 ,  G06K9/00 ,  G06K9/62 ,  G06Q50/20 ,  G09B7/00

Abstract: The invention provides a classroom cognitive load detection system belonging to the field of education informationization, which includes the following. A task completion feature collecting module records an answer response time and a correct answer rate of a student when completing a task. A cognitive load self-assessment collecting module quantifies and analyzes a mental effort and a task subjective difficulty by a rating scale. An expression and attention feature collecting module collects a student classroom performance video to obtain a face region through a face detection and counting a smiley face duration and a watching duration of the student according to a video analysis result. A feature fusion module fuses aforesaid six indexes into a characteristic vector. A cognitive load determining module inputs the characteristic vector to a classifier to identify a classroom cognitive load level of the student.