Novel Handcrafted Features for Cognitive Attention Analysis of Students in Flipped Classroom

Abstract

Flipped Classroom is an innovative learning pedagogy based on students’ academic engagement inside and outside the classroom. Students take lessons from pre-loaded lecture videos through desktop, tablets and mobiles before coming to the classroom. Inside the classroom, the sole focus is on doubt clearing and problem solving. However, it is very difficult to ensure that students really pay attention while watching lecture videos. This is a concern, given the levels of distraction the students are exposed to in this age of internet. Electroencephalogram (EEG) signals can be captured from brain of students and used to monitor their attention.In this study, we develop an efficient approach of feature engineering to analyze the attention level of students in Flipped Classroom from captured brain wave signals. We process the EEG signals using Fast Fourier Transform (FFT). Subsequently, we apply our proposed novel handcrafted features method to obtain the features. Standard classification methods are employed to test the effectiveness of our designed features. Experimental results demonstrate that our proposed handcrafted features perform better than standard FFT-derived-frequency bands.