<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Education informatization is an important initiative for the country to realize education modernization, which is related to the development of China’s education. The use of a single evaluation of teaching effectiveness obviously cannot meet the assessment needs. Based on the premise of intelligent assistance through a hybrid brain-computer interface, this paper aims to study an evaluation method for teaching effectiveness using artificial intelligence. Firstly, EEG signals are collected through an EEG acquisition device, and then they are preprocessed. Second, the pre-processed signals are subjected to feature extraction, and then the features are classified using a specific pattern recognition method, and finally the classification results are used to assess the effectiveness of English teaching for college students. Based on the evaluation results, the learning system is optimized and improved. The signals collected by EEG devices carry a lot of noise, and in this paper, the processing of the original signals includes a smoothing filter, de-baseline filter, filter filter, and Kalman filter to achieve a higher signal-to-noise ratio. Multivariate evaluation results are provided by the evaluation system that is assisted by artificial intelligence. Compared with other evaluation methods to enrich the evaluation information, based on the above system effectiveness evaluation study, as far as the F1 value is concerned, this paper’s method is the highest, reaching 0.815, which is 0.048 higher than the highest F1 value of the traditional algorithm, and in the other two values, this paper’s method is also higher than other methods. This verifies the rationality and effectiveness of the teaching effectiveness evaluation system based on a hybrid brain-computer interface proposed in this paper.</p>
</abstract>

Education informatization is an important initiative for the country to realize education modernization, which is related to the development of China’s education. The use of a single evaluation of teaching effectiveness obviously cannot meet the assessment needs. Based on the premise of intelligent assistance through a hybrid brain-computer interface, this paper aims to study an evaluation method for teaching effectiveness using artificial intelligence. Firstly, EEG signals are collected through an EEG acquisition device, and then they are preprocessed. Second, the pre-processed signals are subjected to feature extraction, and then the features are classified using a specific pattern recognition method, and finally the classification results are used to assess the effectiveness of English teaching for college students. Based on the evaluation results, the learning system is optimized and improved. The signals collected by EEG devices carry a lot of noise, and in this paper, the processing of the original signals includes a smoothing filter, de-baseline filter, filter filter, and Kalman filter to achieve a higher signal-to-noise ratio. Multivariate evaluation results are provided by the evaluation system that is assisted by artificial intelligence. Compared with other evaluation methods to enrich the evaluation information, based on the above system effectiveness evaluation study, as far as the F1 value is concerned, this paper’s method is the highest, reaching 0.815, which is 0.048 higher than the highest F1 value of the traditional algorithm, and in the other two values, this paper’s method is also higher than other methods. This verifies the rationality and effectiveness of the teaching effectiveness evaluation system based on a hybrid brain-computer interface proposed in this paper.

Artificial Intelligence-Assisted Evaluation of English Teaching Effectiveness in Colleges and Universities

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

Education informatization is an important initiative for the country to realize education modernization, which is related to the development of China’s...

Artificial Intelligence-Assisted Evaluation of English Teaching Effectiveness in Colleges and Universities

Online veröffentlicht: 05. Aug. 2024

Seitenbereich: -

Eingereicht: 06. Apr. 2024

Akzeptiert: 26. Juni 2024

DOI: https://doi.org/10.2478/amns-2024-2078

Schlüsselwörter
EEG signal, English teaching, Artificial intelligence, Pattern recognition

© 2024 Hong Meng, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Artificial Intelligence-Assisted Evaluation of English Teaching Effectiveness in Colleges and Universities

Online veröffentlicht: 05. Aug. 2024

Seitenbereich: -

Eingereicht: 06. Apr. 2024

Akzeptiert: 26. Juni 2024

DOI: https://doi.org/10.2478/amns-2024-2078

SchlüsselwörterEEG signal, English teaching, Artificial intelligence, Pattern recognition

© 2024 Hong Meng, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Schlüsselwörter
EEG signal, English teaching, Artificial intelligence, Pattern recognition