Use BCI to Generate Attention-Based Metadata for the Assessment of Effective Learning Duration
| Autor: | Ju Chuan Wu, Pei Wen Lu, Xin Mao Chen, Yang Ting Shen |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
medicine.diagnostic_test
Computer science Headset 02 engineering and technology Variation (game tree) Electroencephalography Metadata 03 medical and health sciences 0302 clinical medicine Human–computer interaction 020204 information systems 0202 electrical engineering electronic engineering information engineering medicine Duration (project management) Affective computing Tag system 030217 neurology & neurosurgery Brain–computer interface |
| Zdroj: | Learning and Collaboration Technologies. Learning and Teaching ISBN: 9783319911519 HCI (25) |
| DOI: | 10.1007/978-3-319-91152-6_31 |
| Popis: | 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 15 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 232 s. |
| Databáze: | OpenAIRE |
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