… • We propose a new deep learning architecture for multiple human activity recognition using
… function that computes the probability distribution over the predicted classes of activities. …

… based learning often in the lab, we focus on using classroom instruction in authentic learning
… -based models to detect engagement during learning in authentic classroom environments. …

… LSTM) network architecture used for activity classification with nine classes. n stands for the
… (LSTM) network architecture used for activity classification with nine classes. n stands for the …

… Human activity recognition (HAR) is one of the most … of different multimodal human activity
recognition methods where … the HAR; (e) what are different multimodal HAR methods; (f) how a …

… of CS education, while there were studies focusing on teaching … learner modeling and
multimodal affect detection, despite the … of multimodality and ITS, ubiquitous systems for learning, …

… In some applications, such as affect detection during learning, multimodal data channels can
… context (eg, game-based learning in a classroom vs. a laboratory), indicating more studies …

… ’ learning behavior using data obtained from multimodal distribution. We employed computer
algorithms to classify students’ learning … However, with the recognition of technological …

… learning models, as demonstrated by the high performance of image classification and saliency
detection on out-of-training classes… analysis with the saliency detection results, we obtain …

… In the OvO approach, first, an SVM classifier is trained for each pair of classes. This implies
that for C number of classes, we must train C(C − 1)/2 classifiers. In the testing phase, a test …

… recognition of students in the e-learning environment, there are limited works on affective
state recognition of students in the classroom … ; (c) Multi-modal affective state recognition of …