This paper proposes a novel method for evaluating the video-based learning performance by using brain computer interface (BCI). We develop Interactive Brain Tagging system (IBTS) to collect learns’ physiological affective metadata: attention. IBTS uses the EEG headset to measure learners’ brainwave and convert it into the evaluable attention value. When learners are watching video, their attention values are recorded every one second and marked in each corresponding video clip. We visaulize the variation of attention and tried to find out the continuous duration of higher attention level in a video. We used a 15Â min’ video to conduct the experiment with 31 subjects. The result presented the difference of individual and collective attention duration. Moreover, in our case, the collected result suggested that the appropriate video time with higher attention may locate in 232Â s.
CITATION STYLE
Shen, Y. T., Chen, X. M., Lu, P. W., & Wu, J. C. (2018). Use BCI to Generate Attention-Based Metadata for the Assessment of Effective Learning Duration. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10925 LNCS, pp. 407–417). Springer Verlag. https://doi.org/10.1007/978-3-319-91152-6_31
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