Doc2Vec &Naïve Bayes: Learners’ Cognitive Presence Assessment through Asynchronous Online Discussion TQ Transcripts
Hind Hayati, Abdessamad Chanaa, Mohammed Khalidi Idrissi, Samir Bennani, RIME Team, Mohammadia School of Engineering, Mohammed V University in Rabat
iJET Volume 14, Number 8, ISSN 1863-0383 Publisher: International Association of Online Engineering, Kassel, Germany
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.
Hayati, H., Chanaa, A., Khalidi Idrissi, M. & Bennani, S. (2019). Doc2Vec &Naïve Bayes: Learners’ Cognitive Presence Assessment through Asynchronous Online Discussion TQ Transcripts. International Journal of Emerging Technologies in Learning (iJET), 14(8), 70-81. Kassel, Germany: International Association of Online Engineering.