Electroencephalogram Signals for Detecting Confused Students in Online Education Platforms with Probability-Based Features

Abstract

Online education has emerged as an important educational medium during the COVID-19 pandemic. Despite the advantages of online education, it lacks face-to-face settings, which makes it very difficult to analyze the students’ level of interaction, understanding, and confusion. This study makes use of electroencephalogram (EEG) data for student confusion detection for the massive open online course (MOOC) platform. Existing approaches for confusion detection predominantly focus on model optimization and feature engineering is not very well studied. This study proposes a novel engineering approach that uses probability-based features (PBF) for increasing the efficacy of machine learning models. The PBF approach utilizes the probabilistic output from the random forest (RF) and gradient-boosting machine (GBM) as a feature vector to train machine learning models. Extensive experiments are performed by using the original features and PBF approach through several machine learning models with EEG data. Experimental results suggest that by using the PBF approach on EEG data, a 100% accuracy can be obtained for detecting confused students. K-fold cross-validation and performance comparison with existing approaches further corroborates the results.

Identification of Students’ Confusion in Classes from EEG Signals using Convolution Neural Network

“…With 10000 epochs, we have got 99% classification accuracy in finding mismatches. Some works are performed on the EEG signals confused dataset [23,21,11]. The probability-based features approach utilizes the probabilistic output from the random forest and gradient-boosting machine to train machine learning models to detect the confused student [11].…”

Modeling Behavior and Vaccine Hesitancy Using Twitter-Derived US Population Sentiment during the COVID-19 Pandemic to Predict Daily Vaccination Inoculations

“…Additionally, future research may improve the predictive R-square of the regression equations by including a variable for population segments, possibly identified by age and other demographics that may impact vaccine acceptance. Furthermore, the future research can improve the prediction accuracy by using machine learning modeling approaches, which have been proven to be more powerful and effective than conventional approaches [ 39 , 40 ].…”

SSC: The novel self-stack ensemble model for thyroid disease prediction

“…In this study, we tested several machine learning algorithms which have significant applications in different domains, such as the health care [ 40 ], Internet of Things (IoT) [ 41 ], machine vision [ 42 ], edge computing [ 43 ], education [ 44 , 45 ], and many others. In order to conduct a fair comparative evaluation of our proposed SSC model for the detection of thyroid disease, we chose the following machine learning classifiers: RF due to its effectiveness, interpretability, non-parametric nature, and high accuracy rate across a range of data types; GBM, which has various benefits including adaptability, robust tolerance to anomalous inputs, and high accuracy; AdaBoost, since it is less susceptible to overfitting; LR, because its training and implementation processes are simple; and Support Vector Classifier (SVC), that has advantages including efficiently handling high dimensional data [ 46 ].…”

“…The hyperparameter setting for deep learning models is according to the literature. We study the researchers who worked on the same kinds of datasets and we used the same kind of state-of-the-art architectures to achieve significant accuracy [ 35 , 44 , 47 ]. Additionally, we integrated these neural networks and used a CNN-LSTM hybrid to detect thyroid disease.…”