| Abstract: | Rapid progress in information and communication technologies (ICTs) has fueled the popularity of e-learning for educational purposes. However, an e-learning environment is limited in that online instructors cannot monitor immediately whether students remain focus during online autonomous learning. Therefore, this study develops a novel attention aware system (AAS) capable of recognizing students` attention levels accurately based on EEG signals, thus having high potential to be applied in providing timely alert for conveying low-attention level feedback to online instructors in an e-learning environment. To construct AAS, attention responses of students and their corresponding EEG signals are gathered on a continuous performance test (CPT), i.e. An attention assessment test. Next, the AAS is constructed by using training and testing data by the NeuroSky brainwave detector and the support vector machine (SVM), a well-known machine learning model. Additionally, based on the discrete wavelet transform (DWT), the collected EEG signals are decomposed into five primary bands (i.e. Alpha, beta, gamma, theta and delta) as well as each primary band contains five statistical parameters (including approximate entropy, total variation, energy, skewness and standard deviation), thus generating twenty five potential brainwave features associated with students` attention level for constructing the AAS. An attempt based on genetic algorithm (GA) is also made to enhance the prediction performance of the proposed AAS in terms of identifying students` attention levels. According to GA, the seven most influential features are selected from twenty-five considered features, parameters of the proposed AAS are optimized as well. Analytical results indicate that the proposed AAS can accurately recognize individual student`s attention state as either a high or low level, and the average accuracy rate reaches as high as 90.39 %. Moreover, the proposed AAS is integrated with a video lecture tagging system to examine whether the proposed AAS can accurately detect students` low-attention periods while learning about electrical safety in the workplace via a video lecture. An experiment is designed to assess the prediction performance of the proposed AAS in terms of identifying the periods of video lecture with high-or low-attention levels during learning processes. Analytical results indicate that the proposed AAS can accurately identify the low-attention periods of video lecture generated by students when engaging in a learning activity with video lecture. Results of this study demonstrate that the proposed AAS is an effective attention aware system, capable of assisting online instructors in evaluating students` attention levels to enhance their online learning performance. |
