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The interaction between teachers and students is critical to the learning process. Student success and learner satisfaction have consistently improved in educational situations where instructors and students connect frequently and meaningfully. The Community of Inquiry (CoI) framework, as well as the sense of belonging concept, have received a significant amount of attention from researchers investigating online learning since its debut. The current study focuses on the CoI framework in general, and in particular on studies on teaching, social, and cognitive presences in connection to students’ feeling of belonging and grit enhancement. This research investigated the opinion of 310 students at the Aurel Vlaicu University of Arad regarding their satisfaction with their teachers’ presences, their academic sense of belonging, and their grit. Our methodology followed an innovative approach. First, we employed a network analysis on all subscales’ mean scores, and then we performed a sequential mediation analysis based on both the network analysis results and the conclusions from the literature review. We tested whether students’ perceived classroom comfort and perceived faculty support sequentially mediated the relationship between teacher’s cognitive presence and students’ grit. According to the scientific literature, teacher’s cognitive presence consists of four fundamental categories: triggering events, exploration, integration, and resolution, which specifically the validation of knowledge by cooperation and reflection in a community of inquiry. We further tested if sense of belonging might mediate the relationship between teachers’ cognitive presence and students’ grit. The results show that students’ perceived classroom comfort and perceived faculty support partially and significantly sequentially mediate the relationship between teachers’ cognitive presence triggering events and students’ grit. The results are then further used to suggest possible recommendations for designing holistic learning environments in Romanian higher education institutions.

International audience; Pattern of emotion identification is one of the improvised research application regarding facial expression as major concern, in those cases, conventional facial expressions for patterns identification. The present model is based on signal collected from phys-iological sensors followed by consecutive deployment of unsupervised machine learning model. The proposed model is unsu-pervised in following aspects: firstly, it introduces Expectation Maximization problem with respect to unknown emotion labels to be derived from the measures. Correlation of physiological signal and individual emotion labels can be identified. This follows a considerable emotion classification method. However, the output of EM model doesn’t ensure the correct identification of emo-tion class, if any. We introduce Support Vector Regression (SVR) as output module of this model. Hence, we try to forecast the probable classes of emotion after investigating the ranges of values and appropriate standard threshold values of physiological signal with respect to respective emotion class e.g. angry, frustration and joy. This should be noted that, the proposed model doesn’t envisage facial expression analysis. However, after successful implementation of Gaussian behaviors of mixed physio-logical signal, we can enhance the accuracy of identification. Significant emotional context exists in output with more precise results of emotion identification phases.

International audience