Due to the lack of face to face interaction in online learning environment, this article aims essentially to give tutors the opportunity to understand and analyze learners’ cognitive behavior. In this perspective, we propose an automatic system to assess learners’ cognitive presence regarding their social interactions within synchronous online discussions. Combining Natural Language Preprocessing, Doc2Vec document embedding method and machine learning techniques; we first make some transformations and preprocessing to the given transcripts, then we apply Doc2Vec method to represent each message as a vector that will be concatenated with LIWC and context features. The vectors are input data of Naïve Bayes algorithm; a machine learning method; that aims to classify transcripts according to cognitive presence categories.

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Massive Open Online Courses (MOOCs) have recently become a very motivating research field in education. Analyzing MOOCs discussion forums presents important issues since it can create challenges for understanding and appropriately identifying student sentiment behaviours. Using the high effectiveness of deep learning, this study aims to classify forum posts based on their sentiment polarity using two experiments. The first use the three known sentiment labels (positive/negative/neutral) and the second one employs sevens labels. The classification method implemented the Hierarchical Attention Network (HAN) algorithm; it combines the attention mechanism with a hierarchical network that simulates the same hierarchical structure of the document. The analysis of 29604 discussion posts from Stanford University affirms the effectiveness of our model. HAN achieved a classification accuracy of 70.3%, which surpassed the other prediction results using usual text classification models. These results are promising and have implications on the future development of automated sentiment analysis tool on e-learning discussion forum.