Detecting the Confusion of Students in Massive Open Online Courses Using EEG

Abstract

Confusion among students hinders learning and contributes to demotivation and disinterest in the course materials. However, it takes a lot of time and resources to identify confused pupils in extensive courses. Using LSTM and Attention, we suggest a deep learning model for monitoring students' confusion by EEG signals from students when they watching MOOC videos. The model obtained an accuracy of 0.82 on the EEG data, exceeding the previous experimental results for this dataset. Experiments show that the attention mechanism picks up on the significance of various features on prediction results. It can effectively solve the overfitting problem and improve the model classification effect.

ODL-BCI: Optimal deep learning model for brain-computer interface to classify students confusion via hyperparameter tuning

“…The study contributes to developing more accurate and reliable brain signal analysis techniques. Kabir et al [16] Men and Li [7] investigate using EEG signals to detect student confusion in Massive Open Online Courses (MOOCs). Their study demonstrates the potential of EEG-based analysis in monitoring and understanding the cognitive states of students during online learning.…”

“…Men and Li [12] investigate the use of EEG signals to detect student confusion in Massive Open Online Courses (MOOCs). Their study demonstrates the potential of EEG-based analysis in monitoring and understanding the cognitive states of students during online learning.…”

“…One critical aspect lies in monitoring and assessing students' confusion levels in education, which can provide valuable insights into their learning behaviours and enable educators to tailor personalized teaching methods for improved learning outcomes [7,8]. Despite its potential, extracting meaningful information from EEG data is complex, requiring sophisticated analytical models to handle its high dimensionality and variability [9,10].…”

“…EEG, a representation of the brain’s electrical activity, offers invaluable insights into cognitive states and has diverse applications, ranging from medical diagnostics to enhancing learning experiences in education [4, 5, 6]. One critical aspect lies in monitoring and assessing students’ confusion levels in education, which can provide valuable insights into their learning behaviours and enable educators to tailor personalized teaching methods for improved learning outcomes [7, 8]. Despite its potential, extracting meaningful information from EEG data is complex, requiring sophisticated analytical models to handle its high dimensionality and variability [9, 10].…”