Abstract
Interest plays a vital role in students’ learning performance. Accurately measuring situational interest in the classroom environment is important for understanding the learning mechanism and improving teaching. However, self-report measurements frequently encounter issues of subjectivity and ambiguity, and it is hard to collect dynamic self-report scales without disturbance in the naturalistic environment. Thanks to the development of neuroscience and portable biosensors, it has become possible to represent psychological states with neurophysiological features in the classroom environment. In this study, multimodal neurophysiological signals, including electroencephalograph (EEG), electrodermal activity (EDA), and photoplethysmography (PPG), were applied to represent situational interest under both laboratory (Study 1) and naturalistic (Study 2) paradigms. A total of 33 features were extracted, and 7 different statistical indicators were calculated for each of them across all the epochs. Among these features, 47 in Study 1 and 49 in Study 2 demonstrated significant correlation with self-report situational interest. Employing a machine learning model, the analysis yielded a mean absolute error (MAE) of 0.772 and mean squared error (MSE) of 0.883 for the dataset in Study 1. However, the model was not robust on data from Study 2. These findings offer empirical support for the conceptual framework of situational interest, demonstrate the potential of neurophysiological data in educational assessments, and also highlight the challenges in naturalistic paradigm.
First Page
108
Last Page
125
Ethics Approval
Yes
Declaration Statement
This work was supported by the Beijing Educational Science Foundation of the Fourteenth 5-year Planning (BGEA23019). The authors appreciate Xinqiao Gao, Baosong Li, Zhilin Qu, Fei Qin, Guannan Yao, Xinya Liu, Kun Wang, Liyi Yang, Jianhua Zhang, Ziyan Xu, Zheng Dong, Wenhui He, Xiaomeng Xu, and Yingyao Fu for their assistance during data collection.
Recommended Citation
Liu, X., Ye, J., & Zhang, Y. (2023). Multimodal neurophysiological representations of high school students’ situational interest: A machine learning approach. Journal of Educational Technology Development and Exchange (JETDE), 16(2), 108-125. https://doi.org/10.18785/jetde.1602.07
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
