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http://hdl.handle.net/2080/3574
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DC Field | Value | Language |
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dc.contributor.author | Shaw, Rabi | - |
dc.contributor.author | Mohanty, Chinmay | - |
dc.contributor.author | Pradhan, Animesh | - |
dc.contributor.author | Patra, Bidyut Kr | - |
dc.date.accessioned | 2021-07-26T12:38:30Z | - |
dc.date.available | 2021-07-26T12:38:30Z | - |
dc.date.issued | 2021-07 | - |
dc.identifier.citation | IEEE 21st International Conference on Advanced Learning Technologies, 12-15 July 2021, ICALT 2021 | en_US |
dc.identifier.uri | http://hdl.handle.net/2080/3574 | - |
dc.description | Copyright of this paper is with proceedings publisher | en_US |
dc.description.abstract | Flipped Classroom is a mode of learning which is developed based on students’ academic engagement inside and outside the classroom. In this learning pedagogy, students take lessons from pre-loaded lecture videos before coming to the classroom for doubt clearing, discussion, problem solving, etc. However, it is very difficult to ensure that students really pay attention while watching lecture videos. In this paper, we adopt a feature selection technique called 1D local binary pattern (1D-LBP) to analyze captured brain signals of the students. The proposed feature selection technique is termed as 1D Multi-Point Local Ternary Pattern (MP-LTP), which extracts unique statistical features from EEG signals. Subsequently, standard classification techniques are exploited to analyze the attention level of students. Experimental results show that the proposed method outperforms state-of-the-art classification techniques using LBP | en_US |
dc.subject | Electroencephalogram (EEG), | en_US |
dc.subject | Multi-Point Local | en_US |
dc.subject | Ternary Pattern (MP-LTP), | en_US |
dc.subject | Flipped Learning (FL), | en_US |
dc.subject | Discrete Wavelet Packet Transform (WPT | en_US |
dc.title | Attention Analysis in Flipped Classroom using 1D Multi-Point Local Ternary Patterns | en_US |
Appears in Collections: | Conference Papers |
Files in This Item:
File | Description | Size | Format | |
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Patrab_ICALT2021.pdf | 136.03 kB | Adobe PDF | View/Open |
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