Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2025 Jul 1;15(1):22226.
doi: 10.1038/s41598-025-06901-1.

A learning behavior classification model based on classroom meta-action sequences

Zhaoyu Shou  1   2 , Xiaohu Yuan  3 , Dongxu Li  1 , Jianwen Mo  1 , Huibing Zhang  4 , Hua Yuan  1 , Ziyong Wu  5
Affiliations

A learning behavior classification model based on classroom meta-action sequences

Zhaoyu Shou et al. Sci Rep. .

Abstract

The individual adaptive behavioral interpretation of students' learning behaviors is a vital link for instructional process interventions. Accurately recognizing learning behaviors and conducting a complete judgment of classroom meta-action sequences are essential for the individual adaptive behavioral interpretation of students' learning behaviors. This paper proposes a learning behavior classification model based on classroom meta-action sequences (ConvTran-Fibo-CA-Enhanced). The model employs the Fibonacci sequence for location encoding to augment the positional attributes of classroom meta-action sequences. It also integrates Channel Attention and Data Augmentation techniques to improve the model's ability to comprehend these sequences, thereby increasing the accuracy of learning behavior classification and verifying the completeness of classroom meta-action sequences. Experimental results show that the proposed model outperforms baseline models on human activity recognition public datasets and learning behavior classification and meta-action sequences completeness judgment datasets in smart classroom scenarios.

Keywords: Channel attention; ConvTran; Data augmentation; Learning behavior classification; Positional encoding.

PubMed Disclaimer

Conflict of interest statement

Declarations. Competing interests: The authors declare no competing interests. Ethics approval and consent to participate: This study adhered to the principles outlined in the Helsinki Declaration. All participants were thoroughly informed about the purpose and procedures of the research, and written informed consent was obtained from each participant before the experiment commenced.

Figures

Fig.1
Fig.1
Distribution of learning behaviors and classroom meta-actions.
Fig. 2
Fig. 2
ConvTran-Fibo-CA-enhanced network architecture diagram.
Fig. 3
Fig. 3
The vector and eRPE network structure.
Fig. 4
Fig. 4
The channel attention mechanism network architecture diagram.
Fig. 5
Fig. 5
Training results of the two models on the public datasets.
Fig. 6
Fig. 6
Comparison of embedding vectors at position 1 and position 30 between two different positional encoding methods.
Fig. 7
Fig. 7
Training comparison curve of the model on the GUET5 dataset.
Fig. 8
Fig. 8
Training results of GUET5 completeness judgment dataset.
Fig. 9
Fig. 9
The confusion matrix for the test results of ConvTran and ConvTran-Fibo-CA-Enhanced on the GUET5 Completeness Judgment dataset.
See this image and copyright information in PMC

References

    1. Saha, A., Rajak, S., Saha, J. & Chowdhury, C. A survey of machine learning and meta-heuristics approaches for sensor-based human activity recognition systems. J. Ambient Intell. Hum. Comput.15, 29–56 (2024).
    1. Dentamaro, V., Gattulli, V., Impedovo, D. & Manca, F. Human activity recognition with smartphone-integrated sensors: A survey. Expert Syst. Appl. 123–143 (2024).
    1. Yang, J., Nguyen, M. N., San, P. P., Li, X. & Krishnaswamy, S. Deep convolutional neural networks on multichannel time series for human activity recognition. In Ijcai. Vol. 15. 3995–4001 (2015).
    1. Shi, X. et al. Convolutional lstm network: A machine learning approach for precipitation nowcasting. Adv. Neural Inf. Process. Syst.28 (2015).
    1. Ordóñez, F. J. & Roggen, D. Deep convolutional and lstm recurrent neural networks for multimodal wearable activity recognition. Sensors16, 115 (2016). - PMC - PubMed

LinkOut - more resources