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The analysis of learning interactions during online studying is a necessary task for designing online courses and sequencing key interactions, which enables online learning platforms to provide users with more efficient and personalized service. However, the research on predicting the interaction itself is not sufficient and the temporal information of interaction sequences hasn't been fully investigated. To fill in this gap, based on the interaction data collected from Massive Open Online Courses (MOOCs), this paper aims to simultaneously predict a user's next interaction and the occurrence time to that interaction. Three different neural network models: the long short-term memory, the recurrent marked temporal point process, and the event recurrent point process, are applied on the MOOC interaction dataset. It concludes that taking the correlation between the user action and its occurrence time into consideration can greatly improve the model performance, and that the prediction results are conducive to exploring dropout rates or online learning habits and performances.

Featured Application: The proposed automatic emotion recognition system has been deployed in the classroom environment (education) but it can be used anywhere to monitor the emotions of humans, i.e., health, banking, industries, social welfare etc. Emotions play a vital role in education. Technological advancement in computer vision using deep learning models has improved automatic emotion recognition. In this study, a real-time automatic emotion recognition system is developed incorporating novel salient facial features for classroom assessment using a deep learning model. The proposed novel facial features for each emotion are initially detected using HOG for face recognition, and automatic emotion recognition is then performed by training a convolutional neural network (CNN) that takes real-time input from a camera deployed in the classroom. The proposed emotion recognition system will analyze the facial expressions of each student during learning. The selected emotional states are happiness, sadness, and fear along with the cognitive–emotional states of satisfaction, dissatisfaction, and concentration. The selected emotional states are tested against selected variables gender, department, lecture time, seating positions, and the difficulty of a subject. The proposed system contributes to improve classroom learning. [ABSTRACT FROM AUTHOR]

Emotions play a vital role in education. Technological advancement in computer vision using deep learning models has improved automatic emotion recognition. In this study, a real-time automatic emotion recognition system is developed incorporating novel salient facial features for classroom assessment using a deep learning model. The proposed novel facial features for each emotion are initially detected using HOG for face recognition, and automatic emotion recognition is then performed by training a convolutional neural network (CNN) that takes real-time input from a camera deployed in the classroom. The proposed emotion recognition system will analyze the facial expressions of each student during learning. The selected emotional states are happiness, sadness, and fear along with the cognitive–emotional states of satisfaction, dissatisfaction, and concentration. The selected emotional states are tested against selected variables gender, department, lecture time, seating positions, and the difficulty of a subject. The proposed system contributes to improve classroom learning.