Adaptive e-learning can be used to personalize learning environments for students to meet their individual demands. Individual differences depend on the students' personality traits. Numerous studies have indicated that understanding the role of personality in the learning process can facilitate learning. Hence, personality identification in e-learning is a critical issue in education. In this study, we propose the enhanced extended nearest neighbor (EENN) algorithm to automatically identify two of the Big Five personality traits from students' behavior in online learning: openness to experience and extraversion. The performance of the proposed method is evaluated using a 5-fold cross-validation approach on data from 662 senior high school students. The experimental results indicate that the EENN method can automatically recognize personality at an average accuracy of 0.758. The optimized method that combines EENN with particle swarm optimization (EENN-PSO) significantly improves the identification, resulting in an average accuracy of 0.976. The results can benefit students by increasing the accuracy of personalization based on their personality traits while simultaneously allowing them to be better understood and possibly allowing their instructors to provide more appropriate learning interventions.

In recent years, computerized adaptive testing (CAT) has gained popularity as an important means to evaluate students' ability. Assigning tags to test questions is crucial in CAT. Manual tagging is widely used for constructing question banks; however, this approach is time-consuming and might lead to consistency issues. Automatic question tagging, an alternative, has not been studied extensively. In this paper, we propose a position-based attention model and keywords-based model to automatically tag questions with knowledge units. With regard to multiple-choice questions, the proposed models employ mechanisms to capture useful information from keywords to enhance tagging performance. Unlike traditional machine learning-based tagging methods, our models utilize deep neural networks to represent questions using contextual information. The experimental results show that our proposed models outperform some traditional classification and topic methods by a large margin on an English question bank dataset.

The ability to recognize dynamical processes in physical events and to abstract physical concepts or even to reveal physical laws, lies at the core of human cognitive development. Modern machine learning methods have been given high hopes for realizing such ability, but are still in their infancy. Here we present our progress in this capability. The main purposes of this paper are to use neural networks for classifying the dynamical skyrmion phases of videos and to demonstrate that neural networks can learn physical concepts from the dynamical process. To this end, we employ multiple neural networks to recognize the static phases (image format) and dynamical phases (video format) of a particle-based skyrmion model. Our results show that neural networks, without any prior knowledge, can not only correctly classify these phases, but also predict the phase boundaries that agree with those obtained by simulation. We further propose a parameter visualization scheme to interpret what neural networks have learned. We show that neural networks can learn two order parameters from videos of dynamical phases and predict the critical values of two order parameters. Finally, we demonstrate that only two order parameters are needed to identify videos of skyrmion dynamical phases. Hence, this parameter visualization scheme can be used to determine how many order parameters are needed to fully recognize the input phases. Our work sheds light on the future use of neural networks in discovering meaningful physical concepts and revealing unknown yet physical laws from videos.