A Behavior Sequence Based Psychological Feature Extraction Model for Students in English Teaching
DOI:
https://doi.org/10.3991/ijet.v18i02.35503Keywords:
Feature extraction, Approximate entropy, Local mean decomposition, Support vector machineAbstract
With the rapid development of science and technology, intelligent teaching has been gradually popularized in the campus, and the change of relevant teaching modes has also been carried out. In the new English teaching, it is necessary to analyze the students’ psychological behavior. In order to get accurate results, the research captures the students’ behavior as a behavior sequence, and uses the improved combined kernel support vector machine to extract features on the basis of approximate entropy and local mean decomposition. At the same time, it is compared with the traditional support vector machine, the traditional least square vector machine and the back-propagation neural network algorithm. The experimental results show that the improved algorithm has higher accuracy, recall and accuracy than the other three algorithms. The overall accuracy of the improved algorithm is 82.53%, the highest accuracy is 90.12%, the overall recall is 58.64% and the overall precision is 86.92%. The experimental results show that the improved algorithm has a significant improvement in performance, and can be applied to the extraction of students’ psychological characteristics. Therefore, the algorithm can be used in conventional blended English teaching, which is convenient for teachers to carry out English teaching more efficiently.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2022 Xiaoyu Cai, Jarrytt McDonald
This work is licensed under a Creative Commons Attribution 4.0 International License.
The submitting author warrants that the submission is original and that she/he is the author of the submission together with the named co-authors; to the extend the submission incorporates text passages, figures, data or other material from the work of others, the submitting author has obtained any necessary permission.
Articles in this journal are published under the Creative Commons Attribution Licence (CC-BY What does this mean?). This is to get more legal certainty about what readers can do with published articles, and thus a wider dissemination and archiving, which in turn makes publishing with this journal more valuable for you, the authors.
By submitting an article the author grants to this journal the non-exclusive right to publish it. The author retains the copyright and the publishing rights for his article without any restrictions.