Abstract: Assessing students' cognitive ability is one of the most important prerequisites for improving learning effectiveness, and the process involves aspects such as exercises, students' answers and teaching cases. In order to effectively assess students' cognitive ability, this paper proposes a Chinese text classification model that can automatically and accurately classify Bloom's cognitive hierarchy of exercises, starting from the exercises. Firstly, FreeLB perturbation is added to the input Embedding to enhance the generalization performance of the model, and Chinese-RoBERTa-wwm is used to obtain the pooler information and sequence information of the text; secondly, LSTM is used to extract the deep-associative features in the sequence information and combine with the pooler information to construct the semantically informative word vectors; lastly, the word vectors are fed into BiLSTM to learn the sequence bi-directional dependency information to obtain more comprehensive semantic features to achieve the accurate classification of the exercises. Experiments show that the model proposed in this paper significantly outperforms the baseline model on three Chinese public datasets, achieving 94.8%, 94.09% and 94.71% accuracies respectively, and also effectively performs the Bloom cognitive hierarchy classification task on two Chinese exercise datasets with less data.

Zhaoyu Shou, Yipeng Liu, Dongxu Li, Jianwen Mo and Huibing Zhang, “A Bloom Cognitive Hierarchical Classification Model for Chinese Exercises Based on Improved Chinese-RoBERTa-wwm and BiLSTM” International Journal of Advanced Computer Science and Applications(IJACSA), 15(3), 2024. http://dx.doi.org/10.14569/IJACSA.2024.0150349

@article{Shou2024,
title = {A Bloom Cognitive Hierarchical Classification Model for Chinese Exercises Based on Improved Chinese-RoBERTa-wwm and BiLSTM},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.0150349},
url = {http://dx.doi.org/10.14569/IJACSA.2024.0150349},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {3},
author = {Zhaoyu Shou and Yipeng Liu and Dongxu Li and Jianwen Mo and Huibing Zhang}
}