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.

The existence of a hole near a growing fatigue crack can cause the crack trajectory to deviate. Unless the hole is too close to the crack, the crack is arrested at the edge of the hole and does not progress further. The purpose of this paper was to predict the crack propagation and lifetime of two-dimension geometries for linear elastic materials in mixed-mode loading using a finite element source code program written in Visual Fortran language. The finite element mesh is generated using the advancing front method. The onset criterion of crack propagation was based on the equivalent stress intensity factor which provides the most important parameter that must be accurately estimated for the mixed-mode loading condition. The maximum circumferential stress theory was used as a direction criterion. The modified compact tension (MCTS) was studied to demonstrate the influence of the hole’s presence on the direction of crack growth and fatigue life for different configurations. The Paris’ law model has been employed to evaluate the mixed-mode fatigue life for MCTS in different configurations under the linear elastic fracture mechanics (LEFMs) assumption. The framework involves a progressive crack extension study of stress intensity factors (SIFs), crack growth direction, and fatigue life estimation. The results show that the fatigue growth was attracted to the hole either changes its direction to reach the hole or floats by the hole and grows as the hole is missed. The results of the study agree with several crack propagation experiments in the literature revealing similar crack propagation trajectory observations.