基于深度学习的思维能力预测方法、装置、设备及介质
By using a deep learning-based approach and pre-trained text classification and large language models, input vectors are constructed to predict learners' thinking abilities. This solves the problem of existing assessment methods being affected by expert preferences and achieves fast and accurate assessment of thinking abilities.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- HUAZHONG NORMAL UNIV
- Filing Date
- 2024-05-28
- Publication Date
- 2026-07-17
AI Technical Summary
Existing methods for assessing learners' thinking abilities are influenced by expert preferences and assessment standards, and lack objective, accurate, and convenient assessment tools.
This study employs a deep learning-based approach. By acquiring a set of practice texts already used by the user, and utilizing a pre-trained text classification model and a thinking ability prediction model, an input vector is constructed to predict thinking ability. This includes a pre-trained text classification model, Dropout layer, fully connected layer, and non-linear activation layer, which are then combined with a large language model for training and mapping to obtain the thinking ability prediction results.
It enables rapid and accurate prediction of learners' thinking abilities without the need for expert supervision, thus improving the efficiency and accuracy of assessment.
Smart Images
Figure CN118520111B_ABST